DeepEval vs Arize

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.