5 posts tagged with "comparisons"

As an open-source all-in-one LLM evaluation framework, DeepEval replaces a lot of LLMOps tools. It is great if you:

1. Need highly accurate and reliable quantitative benchmarks for your LLM application
2. Want easy control over your evaluation pipeline with modular, research-backed metrics
3. Are looking for an open-source framework that leads to an enterprise-ready platform for organization wide, collaborative LLM evaluation
4. Want to scale beyond testing not just for functionality, but also for safety

This guide is an overview of some alternatives to DeepEval, how they compare, and why people choose DeepEval.

Ragas​

• Company: Exploding Gradients, Inc.
• Founded: 2023
• Best known for: RAG evaluation
• Best for: Data scientist, researchers

Ragas is most known for RAG evaluation, where the founders originally released a paper on the referenceless evaluation of RAG pipelines back in early 2023.

Ragas vs Deepeval Summary​

DeepEval

Ragas

RAG metrics

The popular RAG metrics such as faithfulness

Conversational metrics

Evaluates LLM chatbot conversationals

Agentic metrics

Evaluates agentic workflows, tool use

Safety LLM red teaming

Metrics for LLM safety and security like bias, PII leakage

Multi-modal LLM evaluation

Metrics involving image generations as well

Custom, research-backed metrics

Custom metrics builder with research-backing

Custom, deterministic metrics

Custom, LLM powered decision-based metrics

Open-source

Open with nothing to hide

LLM evaluation platform

Testing reports, regression A|B testing, metric analysis, metric validation

LLM observability platform

LLM tracing, monitoring, cost & latency tracking

Enterprise-ready platform

SSO, compliance, user roles & permissions, etc.

Is Confident in their product

Just kidding

Key differences​

1. Developer Experience: DeepEval offers a highly customizable and developer-friendly experience with plug-and-play metrics, Pytest CI/CD integration, graceful error handling, great documentation, while Ragas provides a data science approach and can feel more rigid and lackluster in comparison.
2. Breadth of features: DeepEval supports a wide range of LLM evaluation types beyond RAG, including chatbot, agents, and scales to safety testing, whereas Ragas is more narrowly focused on RAG-specific evaluation metrics.
3. Platform support: DeepEval is integrated natively with Confident AI, which makes it easy to bring LLM evaluation to entire organizations. Ragas on the other hand barely has a platform and all it does is an UI for metric annotation.

What people like about Ragas​

Ragas is praised for its research approach to evaluating RAG pipelines, and has built-in synthetic data generation makes it easy for teams to get started with RAG evaluation.

What people dislike about Ragas​

Developers often find Ragas frustrating to use due to:

• Poor support for customizations such as metrics and LLM judges
• Minimal ecosystem, most of which borrowed from LangChain, that doesn't go beyond RAG
• Sparse documentation that are hard to navigate
• Frequent unhandled errors that make customization a challenge

Read more on DeepEval vs Ragas.

Arize AI Phoenix​

• Company: Arize AI, Inc
• Founded: 2020
• Best known for: ML observability, monitoring, & tracing
• Best for: ML engineers

Arize AI's Phoenix product is most known for LLM monitoring and tracing, where the company originally started doing traditional ML observability but has since focused more into LLM tracing since early 2023.

Arize vs Deepeval Summary​

DeepEval

Arize AI

RAG metrics

The popular RAG metrics such as faithfulness

Conversational metrics

Evaluates LLM chatbot conversationals

Agentic metrics

Evaluates agentic workflows, tool use

Limited

Safety LLM red teaming

Metrics for LLM safety and security like bias, PII leakage

Multi-modal LLM evaluation

Metrics involving image generations as well

Custom, research-backed metrics

Custom metrics builder with research-backing

Custom, deterministic metrics

Custom, LLM powered decision-based metrics

Open-source

Open with nothing to hide

LLM evaluation platform

Testing reports, regression A|B testing, metric analysis, metric validation

Limited

LLM observability platform

LLM tracing, monitoring, cost & latency tracking

Enterprise-ready platform

SSO, compliance, user roles & permissions, etc.

Is Confident in their product

Just kidding

Key differences​

1. LLM evaluation focus: DeepEval is purpose-built for LLM evaluation with native support for RAG, chatbot, agentic experimentation, with synthetic data generation capabilities, whereas Arize AI is a broader LLM observability platform that is better for one-off debugging via tracing.
2. Evalution metrics: DeepEval provides reliable, customizable, and deterministic evaluation metrics built specifically for LLMs, whereas Arize's metrics is more for surface-level insight; helpful to glance at, but can't rely on 100%.
3. Scales to safety testing: DeepEval scales seamlessly into safety-critical use cases like red teaming through attack simulations, while Arize lacks the depth needed to support structured safety workflows out of the box.

What people like about Arize​

Arize is appreciated for being a comprehensive observability platform with LLM-specific dashboards, making it useful for teams looking to monitor production behavior in one place.

What people dislike about Arize​

While broad in scope, Arize can feel limited for LLM experimentation due to a lack of built-in evaluation features like LLM regression testing before deployment, and its focus on observability makes it less flexible for iterative development.

Pricing is also an issue. Arize AI pushes for annual contracts for basic features like compliance reports that you would normally expect.

Promptfoo​

• Company: Promptfoo, Inc.
• Founded: 2023
• Best known for: LLM security testing
• Best for: Data scientists, AI security engineers

Promptfoo is known for being focused on security testing and red teaming for LLM systems, and offer most of its testing capabilities in yaml files instead of code.

Promptfoo vs Deepeval Summary​

DeepEval

Promptfoo

RAG metrics

The popular RAG metrics such as faithfulness

Conversational metrics

Evaluates LLM chatbot conversationals

Agentic metrics

Evaluates agentic workflows, tool use

Safety LLM red teaming

Metrics for LLM safety and security like bias, PII leakage

Multi-modal LLM evaluation

Metrics involving image generations as well

Custom, research-backed metrics

Custom metrics builder with research-backing

Custom, deterministic metrics

Custom, LLM powered decision-based metrics

Open-source

Open with nothing to hide

LLM evaluation platform

Testing reports, regression A|B testing, metric analysis, metric validation

LLM observability platform

LLM tracing, monitoring, cost & latency tracking

Limited

Enterprise-ready platform

SSO, compliance, user roles & permissions, etc.

Half-way there

Is Confident in their product

Just kidding

Key differences​

1. Breadth of metrics: DeepEval supports a wide range (60+) of metrics across prompt, RAG, chatbot, and safety testing, while Promptfoo is limited to basic RAG and safety metrics.
2. Developer experience: DeepEval offers a clean, code-first experience with intuitive APIs, whereas Promptfoo relies heavily on YAML files and plugin-based abstractions, which can feel rigid and unfriendly to developers.
3. More comprehensive platform: DeepEval is 100% integrated with Confident AI, which is a full-fledged evaluation platform with support for regression testing, test case management, observability, and red teaming, while Promptfoo is a minimal tool focused mainly on generating risk assesments on red teaming results.

What people like about Promptfoo​

Promptfoo makes it easy to get started with LLM testing by letting users define test cases and evaluations in YAML, which works well for simple use cases and appeals to non-coders or data scientists looking for quick results.

What people dislike about Promptfoo​

Promptfoo offers a limited set of metrics (mainly RAG and safety), and its YAML-heavy workflow makes it hard to customize or scale; the abstraction model adds friction for developers, and the lack of a programmatic API or deeper platform features limits advanced experimentation, regression testing, and red teaming.

Langfuse​

• Company: Langfuse GmbH / Finto Technologies Inc.
• Founded: 2022
• Best known for: LLM observability & tracing
• Best for: LLM engineers

Langfuse vs Deepeval Summary​

Key differences​

1. Evaluation focus: DeepEval is focused on structured LLM evaluation with support for metrics, regression testing, and test management, while Langfuse centers more on observability and tracing with lightweight evaluation hooks.
2. Dataset curation: DeepEval includes tools for curating, versioning, and managing test datasets for systematic evaluation (locally or on Confident AI), whereas Langfuse provides labeling and feedback collection but lacks a full dataset management workflow.
3. Scales to red teaming: DeepEval is designed to scale into advanced safety testing like red teaming and fairness evaluations, while Langfuse does not offer built-in capabilities for proactive adversarial testing.

What people like about Langfuse​

Langfuse has a great developer experience with clear documentation, helpful tracing tools, and a transparent pricing and a set of platform features that make it easy to debug and observe LLM behavior in real time.

What people dislike about Langfuse​

While useful for one-off tracing, Langfuse isn't well-suited for systematic evaluation like A/B testing or regression tracking; its playground is disconnected from your actual app, and it lacks deeper support for ongoing evaluation workflows like red teaming or test versioning.

Braintrust​

• Company: Braintrust Data, Inc.
• Founded: 2023
• Best known for: LLM observability & tracing
• Best for: LLM engineers

Braintrust vs Deepeval Summary​

DeepEval

Braintrust

RAG metrics

The popular RAG metrics such as faithfulness

Conversational metrics

Evaluates LLM chatbot conversationals

Agentic metrics

Evaluates agentic workflows, tool use

Limited

Safety LLM red teaming

Metrics for LLM safety and security like bias, PII leakage

Multi-modal LLM evaluation

Metrics involving image generations as well

Custom, research-backed metrics

Custom metrics builder with research-backing

Custom, deterministic metrics

Custom, LLM powered decision-based metrics

Open-source

Open with nothing to hide

LLM evaluation platform

Testing reports, regression A|B testing, metric analysis, metric validation

LLM observability platform

LLM tracing, monitoring, cost & latency tracking

Enterprise-ready platform

SSO, compliance, user roles & permissions, etc.

Is Confident in their product

Just kidding

Key differences​

1. Open vs Closed-source: DeepEval is open-source, giving developers complete flexibility and control over their metrics and evaluation datasets, while Braintrust Data is closed-source, making it difficult to customize evaluation logic or integrate with different LLMs.
2. Developer experience: DeepEval offers a clean, code-first experience with minimal setup and intuitive APIs, whereas Braintrust can feel overwhelming due to dense documentation and limited customizability under the hood.
3. Safety testing: DeepEval supports structured safety testing workflows like red teaming and robustness evaluations, while Braintrust Data lacks native support for safety testing altogether.

What people like about Braintrust​

Braintrust Data provides an end-to-end platform for tracking and evaluating LLM applications, with a wide range of built-in features for teams looking for a plug-and-play solution without having to build from scratch.

What people dislike about Braintrust​

The platform is closed-source, making it difficult to customize evaluation metrics or integrate with different LLMs, and its dense, sprawling documentation can overwhelm new users; additionally, it lacks support for safety-focused testing like red teaming or robustness checks.

Why people choose DeepEval?​

DeepEval is purpose-built for the ideal LLM evaluation workflow with support for prompt, RAG, agents, and chatbot testing. It offers full customizability, reliable and reproducible results like no one else, and allow users to trust fully for pre-deployment regressions testing and A|B experimentation for prompts and models.

Its enterprise-ready cloud platform Confident AI takes no extra lines of code to integration, and allows you to take LLM evaluation to your organization once you see value with DeepEval. It is self-served, has transparent pricing, and teams can upgrade to more features whenever they are ready and feel comfortable after testing the entire platform out.

It includes additional toolkits such as synthetic dataset generation and LLM red teaming so your team never has to stitch together multiple tools for your LLMOps purpose.

TL;DR: Arize is great for tracing LLM apps, especially for monitoring and debugging, but lacks key evaluation features like conversational metrics, test control, and safety checks. DeepEval offers a full evaluation stack—built for production, CI/CD, custom metrics, and Confident AI integration for collaboration and reporting. The right fit depends on whether you're focused solely on observability or also care about building scalable LLM testing into your LLM stack.

How is DeepEval Different?​

1. Evaluation laser-focused​

While Arize AI offers evaluations through spans and traces for one-off debugging during LLM observability, DeepEval focuses on custom benchmarking for LLM applications. We place a strong emphasis on high-quality metrics and robust evaluation features.

This means:

• More accurate evaluation results, powered by research-backed metrics
• Highly controllable, customizable metrics to fit any evaluation use case
• Robust A/B testing tools to find the best-performing LLM iterations
• Powerful statistical analyzers to uncover deep insights from your test runs
• Comprehensive dataset editing to help you curate and scale evaluations
• Scalable LLM safety testing to help you safeguard your LLM—not just optimize it
• Organization-wide collaboration between engineers, domain experts, and stakeholders

2. We obsess over your team's experience​

We obsess over a great developer experience. From better error handling to spinning off entire repos (like breaking red teaming into DeepTeam), we iterate based on what you ask for and what you need. Every Discord question is a chance to improve DeepEval—and if the docs don’t have the answer, that’s on us to build more.

But DeepEval isn’t just optimized for DX. It's also built for teams—engineers, domain experts, and stakeholders. That’s why the platform is baked-in with collaborative features like shared dataset editing and publicly sharable test report links.

LLM evaluation isn’t a solo task—it’s a team effort.

3. We ship at lightning speed​

We’re always active on DeepEval's Discord—whether it’s bug reports, feature ideas, or just a quick question, we’re on it. Most updates ship in under 3 days, and even the more ambitious ones rarely take more than a week.

But we don’t just react—we obsess over how to make DeepEval better. The LLM space moves fast, and we stay ahead so you don’t have to. If something clearly improves the product, we don’t wait. We build.

Take the DAG metric, for example, which took less than a week from idea to docs. Prior to DAG, there was no way to define custom metrics with full control and ease of use—but our users needed it, so we made one.

4. We're always here for you... literally​

We’re always in Discord and live in a voice channel. Most of the time, we’re muted and heads-down, but our presence means you can jump in, ask questions, and get help, whenver you want.

DeepEval is where it is today because of our community—your feedback has shaped the product at every step. And with fast, direct support, we can make DeepEval better, faster.

5. We offer more features with less bugs​

We built DeepEval as engineers from Google and AI researchers from Princeton—so we move fast, ship a lot, and don’t break things.

Every feature we ship is deliberate. No fluff, no bloat—just what’s necessary to make your evals better. We’ll break them down in the next sections with clear comparison tables.

Because we ship more and fix faster (most bugs are resolved in under 3 days), you’ll have a smoother dev experience—and ship your own features at lightning speed.

6. We scale with your evaluation needs​

When you use DeepEval, it takes no additional configuration to bring LLM evaluation to your entire organization. Everything is automatically integrated with Confident AI, which is the dashboard/UI for the evaluation results of DeepEval.

This means 0 extra lines of code to:

• Analyze metric score distributions, averages, and median scores
• Generate testing reports for you to inspect and debug test cases
• Download and save testing results as CSV/JSON
• Share testing reports within your organization and external stakeholders
• Regression testing to determine whether your LLM app is OK to deploy
• Experimentation with different models and prompts side-by-side
• Keep datasets centralized on the cloud

Apart from Confident AI, DeepEval also offers DeepTeam, a new package specific for red teaming, which is for safety testing LLM systems. When you use DeepEval, you won't run into a point where you have to leave its ecosystem because we don't support what you're looking for.

Comparing DeepEval and Arize​

Arize AI’s main product, Phoenix, is a tool for debugging LLM applications and running evaluations. Originally built for traditional ML workflows (which it still supports), the company pivoted in 2023 to focus primarily on LLM observability.

While Phoenix’s strong emphasis on tracing makes it a solid choice for observability, its evaluation capabilities are limited in several key areas:

• Metrics are only available as prompt templates
• No support for A/B regression testing
• No statistical analysis of metric scores
• No ability to experiment with prompts or models

Prompt template-based metrics means they aren’t research-backed, offer little control, and rely on one-off LLM generations. That might be fine for early-stage debugging, but it quickly becomes a bottleneck when you need to run structured experiments, compare prompts and models, or communicate performance clearly to stakeholders.

Metrics​

Arize supports a few types of metrics like RAG, agentic, and use-case-specific ones. But these are all based on prompt templates and not backed by research.

This also means you can only create custom metrics using prompt templates. DeepEval, on the other hand, lets you build your own metrics from scratch or use flexible tools to customize them.

DeepEval

Arize

RAG metrics

The popular RAG metrics such as faithfulness

Conversational metrics

Evaluates LLM chatbot conversationals

Agentic metrics

Evaluates agentic workflows, tool use

Limited

Red teaming metrics

Metrics for LLM safety and security like bias, PII leakage

Multi-modal metrics

Metrics involving image generations as well

Use case specific metrics

Summarization, JSON correctness, etc.

Custom, research-backed metrics

Custom metrics builder should have research-backing

Custom, deterministic metrics

Custom, LLM powered decision-based metrics

Fully customizable metrics

Use existing metric templates for full customization

Explanability

Metric provides reasons for all runs

Run using any LLM judge

Not vendor-locked into any framework for LLM providers

JSON-confineable

Custom LLM judges can be forced to output valid JSON for metrics

Limited

Verbose debugging

Debug LLM thinking processes during evaluation

Caching

Optionally save metric scores to avoid re-computation

Cost tracking

Track LLM judge token usage cost for each metric run

Integrates with Confident AI

Custom metrics or not, whether it can be on the cloud

Dataset Generation​

Arize offers a simplistic dataset generation interface, which requires supplying an entire prompt template to generate synthetic queries from your knowledge base contexts.

In DeepEval, you can create your dataset from research-backed data generation with just your documents.

DeepEval

Arize

Generate from documents

Synthesize goldens that are grounded in documents

Generate from ground truth

Synthesize goldens that are grounded in context

Generate free form goldens

Synthesize goldens that are not grounded

Quality filtering

Remove goldens that do not meet the quality standards

Non vendor-lockin

No Langchain, LlamaIndex, etc. required

Customize language

Generate in français, español, deutsch, italiano, 日本語, etc.

Customize output format

Generate SQL, code, etc. not just simple QA

Supports any LLMs

Generate using any LLMs, with JSON confinement

Save generations to Confident AI

Not just generate, but bring it to your organization

Red teaming​

We built DeepTeam—our second open-source package—as the easiest way to scale LLM red teaming without leaving the DeepEval ecosystem. Safety testing shouldn’t require switching tools or learning a new setup.

Arize doesn't offer red-teaming.

DeepEval

Arize

Predefined vulnerabilities

Vulnerabilities such as bias, toxicity, misinformation, etc.

Attack simulation

Simulate adversarial attacks to expose vulnerabilities

Single-turn attack methods

Prompt injection, ROT-13, leetspeak, etc.

Multi-turn attack methods

Linear jailbreaking, tree jailbreaking, etc.

Data privacy metrics

PII leakage, prompt leakage, etc.

Responsible AI metrics

Bias, toxicity, fairness, etc.

Unauthorized access metrics

RBAC, SSRF, shell injection, sql injection, etc.

Brand image metrics

Misinformation, IP infringement, robustness, etc.

Illegal risks metrics

Illegal activity, graphic content, peronsal safety, etc.

OWASP Top 10 for LLMs

Follows industry guidelines and standards

Using DeepTeam for LLM red teaming means you get the same experience from DeepEval, even for LLM safety and security testing.

Checkout DeepTeam's documentation, which powers DeepEval's red teaming capabilities, for more detail.

Benchmarks​

DeepEval is the first framework to make LLM benchmarks easy and accessible. Before, benchmarking models meant digging through isolated repos, dealing with heavy compute, and setting up complex systems.

With DeepEval, you can set up a model once and run all your benchmarks in under 10 lines of code.

DeepEval

Arize

MMLU

Vulnerabilities such as bias, toxicity, misinformation, etc.

HellaSwag

Vulnerabilities such as bias, toxicity, misinformation, etc.

Big-Bench Hard

Vulnerabilities such as bias, toxicity, misinformation, etc.

DROP

Vulnerabilities such as bias, toxicity, misinformation, etc.

TruthfulQA

Vulnerabilities such as bias, toxicity, misinformation, etc.

HellaSwag

Vulnerabilities such as bias, toxicity, misinformation, etc.

This is not the entire list (DeepEval has 15 benchmarks and counting), and Arize offers no benchmarks at all.

Integrations​

Both tools offer integrations—but DeepEval goes further. While Arize mainly integrates with LLM frameworks like LangChain and LlamaIndex for tracing, DeepEval also supports evaluation integrations on top of observability.

That means teams can evaluate their LLM apps—no matter what stack they’re using—not just trace them.

DeepEval

Arize

Pytest

First-class integration with Pytest for testing in CI/CD

LangChain & LangGraph

Run evals within the Lang ecosystem, or apps built with it

LlamaIndex

Run evals within the LlamaIndex ecosystem, or apps built with it

Hugging Face

Run evals during fine-tuning/training of models

ChromaDB

Run evals on RAG pipelines built on Chroma

Weaviate

Run evals on RAG pipelines built on Weaviate

Elastic

Run evals on RAG pipelines built on Elastic

QDrant

Run evals on RAG pipelines built on Qdrant

PGVector

Run evals on RAG pipelines built on PGVector

Langsmith

Can be used within the Langsmith platform

Helicone

Can be used within the Helicone platform

Confident AI

Integrated with Confident AI

DeepEval also integrates directly with LLM providers to power its metrics—since DeepEval metrics are LLM agnostic.

Platform​

Both DeepEval and Arize has their own platforms. DeepEval's platform is called Confident AI, and Arize's platform is called Phoenix.

Confident AI is built for powerful, customizable evaluation and benchmarking. Phoenix, on the other hand, is more focused on observability.

DeepEval

Arize

Metric annotation

Annotate the correctness of each metric

Sharable testing reports

Comprehensive reports that can be shared with stakeholders

A|B regression testing

Determine any breaking changes before deployment

Prompts and models experimentation

Figure out which prompts and models work best

Dataset editor

Domain experts can edit datasets on the cloud

Dataset revision history & backups

Point in time recovery, edit history, etc.

Limited

Metric score analysis

Score distributions, mean, median, standard deviation, etc.

Metric validation

False positives, false negatives, confusion matrices, etc.

Prompt versioning

Edit and manage prompts on the cloud instead of CSV

Metrics on the cloud

Run metrics on the platform instead of locally

Trigger evals via HTTPs

For users that are using (java/type)script

Trigger evals without code

For stakeholders that are non-technical

Alerts and notifications

Pings your slack, teams, discord, after each evaluation run.

LLM observability & tracing

Monitor LLM interactions in production

Online metrics in production

Continuously monitor LLM performance

Human feedback collection

Collect feedback from internal team members or end users

LLM guardrails

Ultra-low latency guardrails in production

LLM red teaming

Managed LLM safety testing and attack curation

Self-hosting

On-prem deployment so nothing leaves your data center

SSO

Authenticate with your Idp of choice

User roles & permissions

Custom roles, permissions, data segregation for different teams

Transparent pricing

Pricing should be available on the website

HIPAA-ready

For companies in the healthcare indudstry

SOCII certification

For companies that need additional security compliance

Confident AI is also self-served, meaning you don't have to talk to us to try it out. Sign up here.

Conclusion​

If there’s one thing to remember: Arize is great for debugging, while Confident AI is built for LLM evaluation and benchmarking.

Both have their strengths and some feature overlap—but it really comes down to what you care about more: evaluation or observability.

If you want to do both, go with Confident AI. Most observability tools cover the basics, but few give you the depth and flexibility we offer for evaluation. That should be more than enough to get started with DeepEval.

TL;DR: Langfuse has strong tracing capabilities, which is useful for debugging and monitoring in production, and easy to adopt thanks to solid integrations. It supports evaluations at a basic level, but lacks advanced features for heavier experimentation like A/B testing, custom metrics, granular test control. Langfuse takes a prompt-template-based approach to metrics (similar to Arize) which can be simplistic, but lacks the accuracy of research-backed metrics. The right tool depends on whether you’re focused solely on observability, or also investing in scalable, research-backed evaluation.

How is DeepEval Different?​

1. Evaluation-First approach​

Langfuse's tracing-first approach means evaluations are built into that workflow, which works well for lightweight checks. DeepEval, by contrast, is purpose-built for LLM benchmarking—with a robust evaluation feature set that includes custom metrics, granular test control, and scalable evaluation pipelines tailored for deeper experimentation.

This means:

• Research-backed metrics for accurate, trustworthy evaluation results
• Fully customizable metrics to fit your exact use case
• Built-in A/B testing to compare model versions and identify top performers
• Advanced analytics, including per-metric breakdowns across datasets, models, and time
• Collaborative dataset editing to curate, iterate, and scale fast
• End-to-end safety testing to ensure your LLM is not just accurate, but secure
• Team-wide collaboration that brings engineers, researchers, and stakeholders into one loop

2. Team-wide collaboration​

We’re obsessed with UX and DX: iterations, better error messages, and spinning off focused tools like DeepTeam (DeepEval red-teaming spinoff repo) when it provides a better experience. But DeepEval isn’t just for solo devs. It’s built for teams—engineers, researchers, and stakeholders—with shared dataset editing, public test reports, and everything you need to collaborate. LLM evals is a team effort, and we’re building for that.

3. Ship, ship, ship​

Many of the features in DeepEval today were requested by our community. That's because we’re always active on DeepEval’s Discord, listening for bugs, feedback, and feature ideas. Most requests ship in under 3 days—bigger ones usually land within a week. Don’t hesitate to ask. If it helps you move faster, we’ll build it—for free.

The DAG metric is a perfect example: it went from idea to live docs in under a week. Before that, there was no clean way to define custom metrics with both full control and ease of use. Our users needed it, so we made it happen.

4. Lean features, more features, fewer bugs​

We don’t believe in feature sprawl. Everything in DeepEval is built with purpose—to make your evaluations sharper, faster, and more reliable. No noise, just what moves the needle (more information in the table below).

We also built DeepEval as engineers from Google and AI researchers from Princeton—so we move fast, ship a lot, and don’t break things.

5. Founder accessibility​

You’ll find us in the DeepEval Discord voice chat pretty much all the time — even if we’re muted, we’re there. It’s our way of staying open and approachable, which makes it super easy for users to hop in, say hi, or ask questions.

6. We scale with your evaluation needs​

When you use DeepEval, everything is automatically integrated with Confident AI, which is the dashboard for analyzing DeepEval's evaluation results. This means it takes 0 extra lines of code to bring LLM evaluation to your team, and entire organization:

• Analyze metric score distributions, averages, and median scores
• Generate testing reports for you to inspect and debug test cases
• Download and save testing results as CSV/JSON
• Share testing reports within your organization and external stakeholders
• Regression testing to determine whether your LLM app is OK to deploy
• Experimentation with different models and prompts side-by-side
• Keep datasets centralized on the cloud

Moreover, at some point, you’ll need to test for safety, not just performance. DeepEval includes DeepTeam, a built-in package for red teaming and safety testing LLMs. No need to switch tools or leave the ecosystem as your evaluation needs grow.

Comparing DeepEval and Langfuse​

Langfuse has strong tracing capabilities and is easy to adopt due to solid integrations, making it a solid choice for debugging LLM applications. However, its evaluation capabilities are limited in several key areas:

• Metrics are only available as prompt templates
• No support for A/B regression testing
• No statistical analysis of metric scores
• Limited ability to experiment with prompts, models, and other LLM parameters

Prompt template-based metrics aren’t research-backed, offer limited control, and depend on single LLM outputs. They’re fine for early debugging or lightweight production checks, but they break down fast when you need structured experiments, side-by-side comparisons, or clear reporting for stakeholders.

Metrics​

Langfuse allows users to create custom metrics using prompt templates but doesn't provide out-of-the-box metrics. This means you can use any prompt template to calculate metrics, but it also means that the metrics are research-backed, and don't give you granualr score control.

DeepEval

Langfuse

RAG metrics

The popular RAG metrics such as faithfulness

Conversational metrics

Evaluates LLM chatbot conversationals

Agentic metrics

Evaluates agentic workflows, tool use

Red teaming metrics

Metrics for LLM safety and security like bias, PII leakage

Multi-modal metrics

Metrics involving image generations as well

Use case specific metrics

Summarization, JSON correctness, etc.

Custom, research-backed metrics

Custom metrics builder should have research-backing

Custom, deterministic metrics

Custom, LLM powered decision-based metrics

Fully customizable metrics

Use existing metric templates for full customization

Limited

Explanability

Metric provides reasons for all runs

Run using any LLM judge

Not vendor-locked into any framework for LLM providers

JSON-confineable

Custom LLM judges can be forced to output valid JSON for metrics

Limited

Verbose debugging

Debug LLM thinking processes during evaluation

Caching

Optionally save metric scores to avoid re-computation

Cost tracking

Track LLM judge token usage cost for each metric run

Integrates with Confident AI

Custom metrics or not, whether it can be on the cloud

Dataset Generation​

Langfuse offers a dataset management UI, but doesn't have dataset generation capabilities.

DeepEval

Langfuse

Generate from documents

Synthesize goldens that are grounded in documents

Generate from ground truth

Synthesize goldens that are grounded in context

Generate free form goldens

Synthesize goldens that are not grounded

Quality filtering

Remove goldens that do not meet the quality standards

Non vendor-lockin

No Langchain, LlamaIndex, etc. required

Customize language

Generate in français, español, deutsch, italiano, 日本語, etc.

Customize output format

Generate SQL, code, etc. not just simple QA

Supports any LLMs

Generate using any LLMs, with JSON confinement

Save generations to Confident AI

Not just generate, but bring it to your organization

Red teaming​

We created DeepTeam, our second open-source package, to make LLM red-teaming seamless (without the need to switch tool ecosystems) and scalable—when the need for LLM safety and security testing arises.

Langfuse doesn't offer red-teaming.

DeepEval

Langfuse

Predefined vulnerabilities

Vulnerabilities such as bias, toxicity, misinformation, etc.

Attack simulation

Simulate adversarial attacks to expose vulnerabilities

Single-turn attack methods

Prompt injection, ROT-13, leetspeak, etc.

Multi-turn attack methods

Linear jailbreaking, tree jailbreaking, etc.

Data privacy metrics

PII leakage, prompt leakage, etc.

Responsible AI metrics

Bias, toxicity, fairness, etc.

Unauthorized access metrics

RBAC, SSRF, shell injection, sql injection, etc.

Brand image metrics

Misinformation, IP infringement, robustness, etc.

Illegal risks metrics

Illegal activity, graphic content, peronsal safety, etc.

OWASP Top 10 for LLMs

Follows industry guidelines and standards

Using DeepTeam for LLM red-teaming means you get the same experience from using DeepEval for evaluations, but with LLM safety and security testing.

Checkout DeepTeam's documentation for more detail.

Benchmarks​

DeepEval is the first framework to make LLM benchmarking easy and accessible. Previously, benchmarking meant digging through scattered repos, wrangling compute, and managing complex setups. With DeepEval, you can configure your model once and run all your benchmarks in under 10 lines of code.

Langfuse doesn't offer LLM benchmarking.

DeepEval

Langfuse

MMLU

Vulnerabilities such as bias, toxicity, misinformation, etc.

HellaSwag

Vulnerabilities such as bias, toxicity, misinformation, etc.

Big-Bench Hard

Vulnerabilities such as bias, toxicity, misinformation, etc.

DROP

Vulnerabilities such as bias, toxicity, misinformation, etc.

TruthfulQA

Vulnerabilities such as bias, toxicity, misinformation, etc.

HellaSwag

Vulnerabilities such as bias, toxicity, misinformation, etc.

This is not the entire list (DeepEval has 15 benchmarks and counting).

Integrations​

Both tools offer a variety of integrations. Langfuse mainly integrates with LLM frameworks like LangChain and LlamaIndex for tracing, while DeepEval also supports evaluation integrations on top of observability.

DeepEval

Langfuse

Pytest

First-class integration with Pytest for testing in CI/CD

LangChain & LangGraph

Run evals within the Lang ecosystem, or apps built with it

LlamaIndex

Run evals within the LlamaIndex ecosystem, or apps built with it

Hugging Face

Run evals during fine-tuning/training of models

ChromaDB

Run evals on RAG pipelines built on Chroma

Weaviate

Run evals on RAG pipelines built on Weaviate

Elastic

Run evals on RAG pipelines built on Elastic

QDrant

Run evals on RAG pipelines built on Qdrant

PGVector

Run evals on RAG pipelines built on PGVector

Langsmith

Can be used within the Langsmith platform

Helicone

Can be used within the Helicone platform

Confident AI

Integrated with Confident AI

DeepEval also integrates directly with LLM providers to power its metrics, from closed-source providers like OpenAI and Azure to open-source providers like Ollama, vLLM, and more.

Platform​

Both DeepEval and Langfuse has their own platforms. DeepEval's platform is called Confident AI, and Langfuse's platform is also called Langfuse. Confident AI is built for powerful, customizable evaluation and benchmarking. Langfuse, on the other hand, is more focused on observability.

DeepEval

Langfuse

Metric annotation

Annotate the correctness of each metric

Sharable testing reports

Comprehensive reports that can be shared with stakeholders

A|B regression testing

Determine any breaking changes before deployment

Prompts and models experimentation

Figure out which prompts and models work best

Limited

Dataset editor

Domain experts can edit datasets on the cloud

Dataset revision history & backups

Point in time recovery, edit history, etc.

Limited

Metric score analysis

Score distributions, mean, median, standard deviation, etc.

Metric validation

False positives, false negatives, confusion matrices, etc.

Prompt versioning

Edit and manage prompts on the cloud instead of CSV

Metrics on the cloud

Run metrics on the platform instead of locally

Trigger evals via HTTPs

For users that are using (java/type)script

Trigger evals without code

For stakeholders that are non-technical

Alerts and notifications

Pings your slack, teams, discord, after each evaluation run.

LLM observability & tracing

Monitor LLM interactions in production

Online metrics in production

Continuously monitor LLM performance

Human feedback collection

Collect feedback from internal team members or end users

LLM guardrails

Ultra-low latency guardrails in production

LLM red teaming

Managed LLM safety testing and attack curation

Self-hosting

On-prem deployment so nothing leaves your data center

SSO

Authenticate with your Idp of choice

User roles & permissions

Custom roles, permissions, data segregation for different teams

Transparent pricing

Pricing should be available on the website

HIPAA-ready

For companies in the healthcare indudstry

SOCII certification

For companies that need additional security compliance

Confident AI is also self-served, meaning you don't have to talk to us to try it out. Sign up here.

Conclusion​

If there’s one takeaway: Langfuse is built for debugging, Confident AI is built for evaluation. They overlap in places, but the difference comes down to focus — observability vs. benchmarking. If you care about both, go with Confident AI, since it gives you far more depth and flexibility when it comes to evaluation.

TL;DR: Ragas is well-suited for lightweight experimentation — much like using pandas for quick data analysis. DeepEval takes a broader approach, offering a full evaluation ecosystem designed for production workflows, CI/CD integration, custom metrics, and integration with Confident AI for team collaboration, reporting, and analysis. The right tool depends on whether you're running ad hoc evaluations or building scalable LLM testing into your LLM stack.

How is DeepEval Different?​

1. We're built for developers​

DeepEval was created by founders with a mixture of engineering backgrounds from Google and AI research backgrounds from Princeton. What you'll find is DeepEval is much more suited for an engineering workflow, while providing the necessary research in its metrics.

This means:

• Unit-testing in CI/CD pipelines with DeepEval's first-class pytest integration
• Modular, plug-and-play metrics that you can use to build your own evaluation pipeline
• Less bugs and clearer error messages, so you know exactly what is going on
• Extensive customizations with no vendor-locking into any LLM or framework
• Abstracted into clear, extendable classess and methods for better reusability
• Clean, readable code that is essential if you ever need to customize DeepEval for yourself
• Exhuastive ecosystem, meaning you can easily build on top of DeepEval while taking advantage of DeepEval's features

2. We care about your experience, a lot​

We care about the usability of DeepEval and wake up everyday thinking about how we can make either the codebase or documentation better to help our users do LLM evaluation better. In fact, everytime someone asks a question in DeepEval's discord, we always try to respond with not just an answer but a relevant link to the documentation that they can read more on. If there is no such relevant link that we can provide users, that means our documentation needs improving.

In terms of the codebase, a recent example is we actually broke away DeepEval's red teaming (safety testing) features into a whole now package, called DeepTeam, which took around a month of work, just so users that primarily need LLM red teaming can work in that repo instead.

3. We have a vibrant community​

Whenever we're working, the team is always in the discord community on a voice call. Alhough we might not be talking all the time (in fact most times on mute), we do this to let users know we're always here whenever they run into a problem.

This means you'll find people are more willing to ask questions with active discussions going on.

4. We ship extremely fast​

We always aim to resolve issues in DeepEval's discord in < 3 days. Sometimes, especially if there's too much going on in the company, it takes another week longer, and if you raise an issue on GitHub issues instead, we might miss it, but other than that, we're pretty consistent.

We also take a huge amount of effort to ship the latest features required for the best LLM evaluation in an extremely short amount of time (it took under a week for the entire DAG metric to be built, tested, with documentation written). When we see something that could clearly help our users, we get it done.

5. We offer more features, with less bugs​

Our heavy engineering backgrounds allow us to ship more features with less bugs in them. Given that we aim to handle all errors that happen within DeepEval gracefully, your experience when using DeepEval will be a lot better.

There's going to be a few comparison tables in later sections to talk more about the additional features you're going to get with DeepEval.

6. We scale with your evaluation needs​

When you use DeepEval, it takes no additional configuration to bring LLM evaluation to your entire organization. Everything is automatically integrated with Confident AI, which is the dashboard/UI for the evaluation results of DeepEval.

This means 0 extra lines of code to:

• Analyze metric score distributions, averages, and median scores
• Generate testing reports for you to inspect and debug test cases
• Download and save testing results as CSV/JSON
• Share testing reports within your organization and external stakeholders
• Regression testing to determine whether your LLM app is OK to deploy
• Experimentation with different models and prompts side-by-side
• Keep datasets centralized on the cloud

Apart from Confident AI, DeepEval also offers DeepTeam, a new package specific for red teaming, which is for safety testing LLM systems. When you use DeepEval, you won't run into a point where you have to leave its ecosystem because we don't support what you're looking for.

Comparing DeepEval and Ragas​

If DeepEval is so good, why is Ragas so popular? Ragas started off as a research paper that focused on the reference-less evaluation of RAG pipelines in early 2023 and got mentioned by OpenAI during their dev day in November 2023.

But the very research nature of Ragas means that you're not going to get as good a developer experience compared to DeepEval. In fact, we had to re-implement all of Ragas's metrics into our own RAG metrics back in early 2024 because they didn't offer things such as:

• Explanability (reasoning for metric scores)
• Verbose debugging (the thinking process of LLM judges used for evaluation)
• Using any custom LLM-as-a-judge (as required by many organizations)
• Evaluation cost tracking

And our users simply couldn't wait for Ragas to ship it before being able to use it in DeepEval's ecosystem (that's why you see that we have our own RAG metrics, and the RAGASMetric, which just wraps around Ragas' metrics but with less functionality).

For those that argues that Ragas is more trusted because they have a research-paper, that was back in 2023 and the metrics has changed a lot since then.

Metrics​

DeepEval and Ragas both specialize in RAG evaluation, however:

• Ragas's metrics has limited support for explanability, verbose log debugging, and error handling, and customizations
• DeepEval's metrics go beyond RAG, with support for agentic workflows, LLM chatbot conversations, all through its plug-and-play metrics.

DeepEval also integrates with Confident AI so you can bring these metrics to your organization whenever you're ready.

DeepEval

Ragas

RAG metrics

The popular RAG metrics such as faithfulness

Conversational metrics

Evaluates LLM chatbot conversationals

Agentic metrics

Evaluates agentic workflows, tool use

Red teaming metrics

Metrics for LLM safety and security like bias, PII leakage

Multi-modal metrics

Metrics involving image generations as well

Use case specific metrics

Summarization, JSON correctness, etc.

Custom, research-backed metrics

Custom metrics builder should have research-backing

Custom, deterministic metrics

Custom, LLM powered decision-based metrics

Fully customizable metrics

Use existing metric templates for full customization

Explanability

Metric provides reasons for all runs

Run using any LLM judge

Not vendor-locked into any framework for LLM providers

JSON-confineable

Custom LLM judges can be forced to output valid JSON for metrics

Verbose debugging

Debug LLM thinking processes during evaluation

Caching

Optionally save metric scores to avoid re-computation

Cost tracking

Track LLM judge token usage cost for each metric run

Integrates with Confident AI

Custom metrics or not, whether it can be on the cloud

Dataset Generation​

DeepEval and Ragas both offers in dataset generation, and while Ragas is deeply locked into the Langchain and LlamaIndex ecosystem, meaning you can't easily generate from any documents, and offers limited customizations, DeepEval's synthesizer is 100% customizable within a few lines of code

If you look at the table below, you'll see that DeepEval's synthesizer is very flexible.

DeepEval

Ragas

Generate from documents

Synthesize goldens that are grounded in documents

Generate from ground truth

Synthesize goldens that are grounded in context

Generate free form goldens

Synthesize goldens that are not grounded

Quality filtering

Remove goldens that do not meet the quality standards

Non vendor-lockin

No Langchain, LlamaIndex, etc. required

Customize language

Generate in français, español, deutsch, italiano, 日本語, etc.

Customize output format

Generate SQL, code, etc. not just simple QA

Supports any LLMs

Generate using any LLMs, with JSON confinement

Save generations to Confident AI

Not just generate, but bring it to your organization

Red teaming​

We even built a second open-source package dedicated for red teaming within DeepEval's ecosystem, just so you don't have to worry about switching frameworks as you scale to safety testing.

Ragas offers no red teaming at all.

DeepEval

Ragas

Predefined vulnerabilities

Vulnerabilities such as bias, toxicity, misinformation, etc.

Attack simulation

Simulate adversarial attacks to expose vulnerabilities

Single-turn attack methods

Prompt injection, ROT-13, leetspeak, etc.

Multi-turn attack methods

Linear jailbreaking, tree jailbreaking, etc.

Data privacy metrics

PII leakage, prompt leakage, etc.

Responsible AI metrics

Bias, toxicity, fairness, etc.

Unauthorized access metrics

RBAC, SSRF, shell injection, sql injection, etc.

Brand image metrics

Misinformation, IP infringement, robustness, etc.

Illegal risks metrics

Illegal activity, graphic content, peronsal safety, etc.

OWASP Top 10 for LLMs

Follows industry guidelines and standards

We want users to stay in DeepEval's ecosystem even for LLM red teaming, because this allows us to provide you the same experience you get from DeepEval, even for LLM safety and security testing.

Checkout DeepTeam's documentation, which powers DeepEval's red teaming capabilities, for more detail.

Benchmarks​

This was more of a fun project, but when we noticed LLM benchmarks were so get hold of we decided to make DeepEval the first framework to make LLM benchmarks so widely accessible. In the past, benchmarking foundational models were compute-heavy and messy. Now with DeepEval, 10 lines of code is all that is needed.

DeepEval

Ragas

MMLU

Vulnerabilities such as bias, toxicity, misinformation, etc.

HellaSwag

Vulnerabilities such as bias, toxicity, misinformation, etc.

Big-Bench Hard

Vulnerabilities such as bias, toxicity, misinformation, etc.

DROP

Vulnerabilities such as bias, toxicity, misinformation, etc.

TruthfulQA

Vulnerabilities such as bias, toxicity, misinformation, etc.

HellaSwag

Vulnerabilities such as bias, toxicity, misinformation, etc.

This is not the entire list (DeepEval has 15 benchmarks and counting), and Ragas offers no benchmarks at all.

Integrations​

Both offer integrations, but with a different focus. Ragas' integrations pushes users onto other platforms such as Langsmith and Helicone, while DeepEval is more focused on providing users the means to evaluate their LLM applications no matter whatever stack they are currently using.

DeepEval

Ragas

Pytest

First-class integration with Pytest for testing in CI/CD

LangChain & LangGraph

Run evals within the Lang ecosystem, or apps built with it

LlamaIndex

Run evals within the LlamaIndex ecosystem, or apps built with it

Hugging Face

Run evals during fine-tuning/training of models

ChromaDB

Run evals on RAG pipelines built on Chroma

Weaviate

Run evals on RAG pipelines built on Weaviate

Elastic

Run evals on RAG pipelines built on Elastic

QDrant

Run evals on RAG pipelines built on Qdrant

PGVector

Run evals on RAG pipelines built on PGVector

Langsmith

Can be used within the Langsmith platform

Helicone

Can be used within the Helicone platform

Confident AI

Integrated with Confident AI

You'll notice that Ragas does not own their platform integrations such as LangSmith, while DeepEval owns Confident AI. This means bringing LLM evaluation to your organization is 10x easier using DeepEval.

Platform​

Both DeepEval and Ragas has their own platforms. DeepEval's platform is called Confident AI, and Ragas's platform is also called Ragas.

Both have varying degrees of capabilities, and you can draw your own conclusions from the table below.

DeepEval

Ragas

Metric annotation

Annotate the correctness of each metric

Sharable testing reports

Comprehensive reports that can be shared with stakeholders

A|B regression testing

Determine any breaking changes before deployment

Prompts and models experimentation

Figure out which prompts and models work best

Dataset editor

Domain experts can edit datasets on the cloud

Dataset revision history & backups

Point in time recovery, edit history, etc.

Metric score analysis

Score distributions, mean, median, standard deviation, etc.

Metric validation

False positives, false negatives, confusion matrices, etc.

Prompt versioning

Edit and manage prompts on the cloud instead of CSV

Metrics on the cloud

Run metrics on the platform instead of locally

Trigger evals via HTTPs

For users that are using (java/type)script

Trigger evals without code

For stakeholders that are non-technical

Alerts and notifications

Pings your slack, teams, discord, after each evaluation run.

LLM observability & tracing

Monitor LLM interactions in production

Online metrics in production

Continuously monitor LLM performance

Human feedback collection

Collect feedback from internal team members or end users

LLM guardrails

Ultra-low latency guardrails in production

LLM red teaming

Managed LLM safety testing and attack curation

Self-hosting

On-prem deployment so nothing leaves your data center

SSO

Authenticate with your Idp of choice

User roles & permissions

Custom roles, permissions, data segregation for different teams

Transparent pricing

Pricing should be available on the website

HIPAA-ready

For companies in the healthcare indudstry

SOCII certification

For companies that need additional security compliance

Confident AI is also self-served, meaning you don't have to talk to us to try it out. Sign up here.

Conclusion​

If there's one thing to remember, we care about your LLM evaluation experience more than anyone else, and apart from anything else this should be more than enough to get started with DeepEval.

TL;DR: TruLens offers useful tooling for basic LLM app monitoring and runtime feedback, but it’s still early-stage and lacks many core evaluation features — including agentic and conversational metrics, granular test control, and safety testing. DeepEval takes a more complete approach to LLM evaluation, supporting structured testing, CI/CD workflows, custom metrics, and integration with Confident AI for collaborative analysis, sharing, and decision-making across teams.

What Makes DeepEval Stand Out?​

1. Purpose-Built for Developers​

DeepEval is designed by engineers with roots at Google and AI researchers from Princeton — so naturally, it's built to slot right into an engineering workflow without sacrificing metric rigor.

Key developer-focused advantages include:

• Seamless CI/CD integration via native pytest support
• Composable metric modules for flexible pipeline design
• Cleaner error messaging and fewer bugs
• No vendor lock-in — works across LLMs and frameworks
• Extendable abstractions built with reusable class structures
• Readable, modifiable code that scales with your needs
• Ecosystem ready — DeepEval is built to be built on

2. We Obsess Over Developer Experience​

From docs to DX, we sweat the details. Whether it's refining error handling or breaking off red teaming into a separate package (deepteam), we're constantly iterating based on what you need.

Every Discord question is an opportunity to improve the product. If the docs don’t have an answer, that’s our cue to fix it.

3. The Community is Active (and Always On)​

We're always around — literally. The team hangs out in the DeepEval Discord voice chat while working (yes, even if muted). It makes us accessible, and users feel more comfortable jumping in and asking for help. It’s part of our culture.

4. Fast Releases, Fast Fixes​

Most issues reported in Discord are resolved in under 3 days. If it takes longer, we communicate — and we prioritize.

When something clearly helps our users, we move fast. For instance, we shipped the full DAG metric — code, tests, and docs — in under a week.

5. More Features, Fewer Bugs​

Because our foundation is engineering-first, you get a broader feature set with fewer issues. We aim for graceful error handling and smooth dev experience, so you're not left guessing when something goes wrong.

Comparison tables below will show what you get with DeepEval out of the box.

6. Scales with Your Org​

DeepEval works out of the box for teams — no extra setup needed. It integrates automatically with Confident AI, our dashboard for visualizing and sharing LLM evaluation results.

Without writing any additional code, you can:

• Visualize score distributions and trends
• Generate and share test reports internally or externally
• Export results to CSV or JSON
• Run regression tests for safe deployment
• Compare prompts, models, or changes side-by-side
• Manage and reuse centralized datasets

For safety-focused teams, DeepTeam (our red teaming toolkit) plugs right in. DeepEval is an ecosystem — not a dead end.

Comparing DeepEval and Trulens​

If you're reading this, there's a good chance you're in academia. Trulens was founded by Stanford professors and got really popular back in late 2023 and early 2024 through a DeepLearning course with Andrew Ng. However the traction slowly died after this initial boost, especially after the Snowflake acquisition.

And so, you'll find DeepEval provides a lot more well-rounded features and support for all different use cases (RAG, agentic, conversations), and completes all parts of the evaluation workflow (dataset generation, benchmarking, platform integration, etc.).

Metrics​

DeepEval does RAG evaluation very well, but it doesn't end there.

DeepEval

Trulens

RAG metrics

The popular RAG metrics such as faithfulness

Conversational metrics

Evaluates LLM chatbot conversationals

Agentic metrics

Evaluates agentic workflows, tool use

Red teaming metrics

Metrics for LLM safety and security like bias, PII leakage

Multi-modal metrics

Metrics involving image generations as well

Use case specific metrics

Summarization, JSON correctness, etc.

Custom, research-backed metrics

Custom metrics builder should have research-backing

Custom, deterministic metrics

Custom, LLM powered decision-based metrics

Fully customizable metrics

Use existing metric templates for full customization

Explanability

Metric provides reasons for all runs

Run using any LLM judge

Not vendor-locked into any framework for LLM providers

JSON-confineable

Custom LLM judges can be forced to output valid JSON for metrics

Verbose debugging

Debug LLM thinking processes during evaluation

Caching

Optionally save metric scores to avoid re-computation

Cost tracking

Track LLM judge token usage cost for each metric run

Integrates with Confident AI

Custom metrics or not, whether it can be on the cloud

Dataset Generation​

DeepEval offers a comprehensive synthetic data generator while Trulens does not have any generation capabilities.

DeepEval

Trulens

Generate from documents

Synthesize goldens that are grounded in documents

Generate from ground truth

Synthesize goldens that are grounded in context

Generate free form goldens

Synthesize goldens that are not grounded

Quality filtering

Remove goldens that do not meet the quality standards

Non vendor-lockin

No Langchain, LlamaIndex, etc. required

Customize language

Generate in français, español, deutsch, italiano, 日本語, etc.

Customize output format

Generate SQL, code, etc. not just simple QA

Supports any LLMs

Generate using any LLMs, with JSON confinement

Save generations to Confident AI

Not just generate, but bring it to your organization

Red teaming​

Trulens offers no red teaming at all, so only DeepEval will help you as you scale to safety and security LLM testing.

DeepEval

Trulens

Predefined vulnerabilities

Vulnerabilities such as bias, toxicity, misinformation, etc.

Attack simulation

Simulate adversarial attacks to expose vulnerabilities

Single-turn attack methods

Prompt injection, ROT-13, leetspeak, etc.

Multi-turn attack methods

Linear jailbreaking, tree jailbreaking, etc.

Data privacy metrics

PII leakage, prompt leakage, etc.

Responsible AI metrics

Bias, toxicity, fairness, etc.

Unauthorized access metrics

RBAC, SSRF, shell injection, sql injection, etc.

Brand image metrics

Misinformation, IP infringement, robustness, etc.

Illegal risks metrics

Illegal activity, graphic content, peronsal safety, etc.

OWASP Top 10 for LLMs

Follows industry guidelines and standards

Checkout DeepTeam's documentation, which powers DeepEval's red teaming capabilities, for more detail.

Benchmarks​

In the past, benchmarking foundational models were compute-heavy and messy. Now with DeepEval, 10 lines of code is all that is needed.

DeepEval

Trulens

MMLU

Vulnerabilities such as bias, toxicity, misinformation, etc.

HellaSwag

Vulnerabilities such as bias, toxicity, misinformation, etc.

Big-Bench Hard

Vulnerabilities such as bias, toxicity, misinformation, etc.

DROP

Vulnerabilities such as bias, toxicity, misinformation, etc.

TruthfulQA

Vulnerabilities such as bias, toxicity, misinformation, etc.

HellaSwag

Vulnerabilities such as bias, toxicity, misinformation, etc.

This is not the entire list (DeepEval has 15 benchmarks and counting), and Trulens offers no benchmarks at all.

Integrations​

DeepEval offers countless integrations with the tools you are likely already building with.

DeepEval

Trulens

Pytest

First-class integration with Pytest for testing in CI/CD

LangChain & LangGraph

Run evals within the Lang ecosystem, or apps built with it

LlamaIndex

Run evals within the LlamaIndex ecosystem, or apps built with it

Hugging Face

Run evals during fine-tuning/training of models

ChromaDB

Run evals on RAG pipelines built on Chroma

Weaviate

Run evals on RAG pipelines built on Weaviate

Elastic

Run evals on RAG pipelines built on Elastic

QDrant

Run evals on RAG pipelines built on Qdrant

PGVector

Run evals on RAG pipelines built on PGVector

Snowflake

Integrated with Snowflake logs

Confident AI

Integrated with Confident AI

Platform​

DeepEval's platform is called Confident AI, and Trulen's platform is hidden and minimal.

DeepEval

Trulens

Sharable testing reports

Comprehensive reports that can be shared with stakeholders

A|B regression testing

Determine any breaking changes before deployment

Prompts and models experimentation

Figure out which prompts and models work best

Dataset editor

Domain experts can edit datasets on the cloud

Dataset revision history & backups

Point in time recovery, edit history, etc.

Metric score analysis

Score distributions, mean, median, standard deviation, etc.

Metric annotation

Annotate the correctness of each metric

Metric validation

False positives, false negatives, confusion matrices, etc.

Prompt versioning

Edit and manage prompts on the cloud instead of CSV

Metrics on the cloud

Run metrics on the platform instead of locally

Trigger evals via HTTPs

For users that are using (java/type)script

Trigger evals without code

For stakeholders that are non-technical

Alerts and notifications

Pings your slack, teams, discord, after each evaluation run.

LLM observability & tracing

Monitor LLM interactions in production

Online metrics in production

Continuously monitor LLM performance

Human feedback collection

Collect feedback from internal team members or end users

LLM guardrails

Ultra-low latency guardrails in production

LLM red teaming

Managed LLM safety testing and attack curation

Self-hosting

On-prem deployment so nothing leaves your data center

SSO

Authenticate with your Idp of choice

User roles & permissions

Custom roles, permissions, data segregation for different teams

Transparent pricing

Pricing should be available on the website

HIPAA-ready

For companies in the healthcare indudstry

SOCII certification

For companies that need additional security compliance

Confident AI is also self-served, meaning you don't have to talk to us to try it out. Sign up here.

Conclusion​

DeepEval offers much more features and better community, and should be more than enough to support all your LLM evaluation needs. Get started with DeepEval here.