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DeepEval vs Langfuse

· 6 min read
Kritin Vongthongsri

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
yes
no
Conversational metrics
Evaluates LLM chatbot conversationals
yes
no
Agentic metrics
Evaluates agentic workflows, tool use
yes
no
Red teaming metrics
Metrics for LLM safety and security like bias, PII leakage
yes
no
Multi-modal metrics
Metrics involving image generations as well
yes
no
Use case specific metrics
Summarization, JSON correctness, etc.
yes
yes
Custom, research-backed metrics
Custom metrics builder should have research-backing
yes
no
Custom, deterministic metrics
Custom, LLM powered decision-based metrics
yes
no
Fully customizable metrics
Use existing metric templates for full customization
yes
Limited
Explanability
Metric provides reasons for all runs
yes
yes
Run using any LLM judge
Not vendor-locked into any framework for LLM providers
yes
no
JSON-confineable
Custom LLM judges can be forced to output valid JSON for metrics
yes
Limited
Verbose debugging
Debug LLM thinking processes during evaluation
yes
no
Caching
Optionally save metric scores to avoid re-computation
yes
no
Cost tracking
Track LLM judge token usage cost for each metric run
yes
no
Integrates with Confident AI
Custom metrics or not, whether it can be on the cloud
yes
no

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
yes
no
Generate from ground truth
Synthesize goldens that are grounded in context
yes
no
Generate free form goldens
Synthesize goldens that are not grounded
yes
no
Quality filtering
Remove goldens that do not meet the quality standards
yes
no
Non vendor-lockin
No Langchain, LlamaIndex, etc. required
yes
no
Customize language
Generate in français, español, deutsch, italiano, 日本語, etc.
yes
no
Customize output format
Generate SQL, code, etc. not just simple QA
yes
no
Supports any LLMs
Generate using any LLMs, with JSON confinement
yes
no
Save generations to Confident AI
Not just generate, but bring it to your organization
yes
no

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.
yes
no
Attack simulation
Simulate adversarial attacks to expose vulnerabilities
yes
no
Single-turn attack methods
Prompt injection, ROT-13, leetspeak, etc.
yes
no
Multi-turn attack methods
Linear jailbreaking, tree jailbreaking, etc.
yes
no
Data privacy metrics
PII leakage, prompt leakage, etc.
yes
no
Responsible AI metrics
Bias, toxicity, fairness, etc.
yes
no
Unauthorized access metrics
RBAC, SSRF, shell injection, sql injection, etc.
yes
no
Brand image metrics
Misinformation, IP infringement, robustness, etc.
yes
no
Illegal risks metrics
Illegal activity, graphic content, peronsal safety, etc.
yes
no
OWASP Top 10 for LLMs
Follows industry guidelines and standards
yes
no

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.
yes
no
HellaSwag
Vulnerabilities such as bias, toxicity, misinformation, etc.
yes
no
Big-Bench Hard
Vulnerabilities such as bias, toxicity, misinformation, etc.
yes
no
DROP
Vulnerabilities such as bias, toxicity, misinformation, etc.
yes
no
TruthfulQA
Vulnerabilities such as bias, toxicity, misinformation, etc.
yes
no
HellaSwag
Vulnerabilities such as bias, toxicity, misinformation, etc.
yes
no

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
yes
no
LangChain & LangGraph
Run evals within the Lang ecosystem, or apps built with it
yes
yes
LlamaIndex
Run evals within the LlamaIndex ecosystem, or apps built with it
yes
yes
Hugging Face
Run evals during fine-tuning/training of models
yes
yes
ChromaDB
Run evals on RAG pipelines built on Chroma
yes
no
Weaviate
Run evals on RAG pipelines built on Weaviate
yes
no
Elastic
Run evals on RAG pipelines built on Elastic
yes
no
QDrant
Run evals on RAG pipelines built on Qdrant
yes
no
PGVector
Run evals on RAG pipelines built on PGVector
yes
no
Langsmith
Can be used within the Langsmith platform
yes
no
Helicone
Can be used within the Helicone platform
yes
no
Confident AI
Integrated with Confident AI
yes
no

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
yes
yes
Sharable testing reports
Comprehensive reports that can be shared with stakeholders
yes
no
A|B regression testing
Determine any breaking changes before deployment
yes
no
Prompts and models experimentation
Figure out which prompts and models work best
yes
Limited
Dataset editor
Domain experts can edit datasets on the cloud
yes
yes
Dataset revision history & backups
Point in time recovery, edit history, etc.
yes
Limited
Metric score analysis
Score distributions, mean, median, standard deviation, etc.
yes
no
Metric validation
False positives, false negatives, confusion matrices, etc.
yes
no
Prompt versioning
Edit and manage prompts on the cloud instead of CSV
yes
yes
Metrics on the cloud
Run metrics on the platform instead of locally
yes
no
Trigger evals via HTTPs
For users that are using (java/type)script
yes
no
Trigger evals without code
For stakeholders that are non-technical
yes
no
Alerts and notifications
Pings your slack, teams, discord, after each evaluation run.
yes
no
LLM observability & tracing
Monitor LLM interactions in production
yes
yes
Online metrics in production
Continuously monitor LLM performance
yes
yes
Human feedback collection
Collect feedback from internal team members or end users
yes
yes
LLM guardrails
Ultra-low latency guardrails in production
yes
no
LLM red teaming
Managed LLM safety testing and attack curation
yes
no
Self-hosting
On-prem deployment so nothing leaves your data center
yes
no
SSO
Authenticate with your Idp of choice
yes
yes
User roles & permissions
Custom roles, permissions, data segregation for different teams
yes
yes
Transparent pricing
Pricing should be available on the website
yes
yes
HIPAA-ready
For companies in the healthcare indudstry
yes
no
SOCII certification
For companies that need additional security compliance
yes
yes

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.