DeepEval vs Langfuse: Which LLM Evaluation Tool to Use

Teams shipping LLM features almost always reach a point where "it looks good in the demo" stops being enough. You need numbers. You need to know whether last week's prompt change made your retrieval-augmented chatbot better or quietly worse. Two tools show up again and again in that conversation: DeepEval and Langfuse. They are frequently pitted against each other, but they were built to solve different halves of the same problem.

DeepEval is a pytest-style evaluation framework. You write eval "tests" the same way you write unit tests, assert on metric scores, and fail the build when quality drops. Langfuse is an LLM observability and tracing platform. It captures every production call, records latency and cost, and lets you replay and score real traces after the fact. The most mature teams do not pick one. They run Langfuse in production to capture traces, then feed sampled traces into DeepEval (or RAGAS) to score them offline and in CI.

This guide walks through what each tool actually does, how their metrics differ, what they cost, how they wire into continuous integration, and the exact pattern for combining them. If you want the deeper single-tool tutorials, see the DeepEval pytest testing guide and the broader testing LLM applications guide.

The core distinction: evaluation vs observability

The single most important thing to understand is that "evaluation" and "observability" are not the same category, even though marketing pages blur them.

Evaluation answers: is this output good? You define a metric (faithfulness, relevancy, correctness), run it over a dataset, and get a score. Evaluation is a batch, offline, deterministic-ish activity. It belongs in CI and in nightly jobs.

Observability answers: what actually happened in production? Which user asked what, how long did the call take, what did it cost, which retrieved chunks were used, and where did the chain break? Observability is a streaming, always-on activity. It belongs in your running service.

DeepEval lives on the evaluation side. Langfuse lives on the observability side and then adds evaluation features on top of the traces it already has. When you see them "competing," it is usually on that overlap: Langfuse can score traces, and DeepEval can be pointed at logged data. But their centers of gravity are different, and that difference should drive your choice.

Dimension	DeepEval	Langfuse
Primary job	Offline evaluation and testing	Production tracing and observability
Mental model	pytest for LLMs	Datadog for LLMs
Where it runs	CI, local, nightly jobs	Inside your live service
Metrics	14+ built-in, research-backed	Trace-attached scores, LLM-as-judge, custom
Data source	Curated datasets and goldens	Real production traces
Self-host	Yes (open source core)	Yes (fully open source)
Best at	Pass/fail quality gates	Debugging real user sessions

What DeepEval brings: metrics and pytest ergonomics

DeepEval's headline feature is a library of ready-made metrics you can drop into a test. Rather than hand-writing an LLM-as-judge prompt for every quality dimension, you instantiate a metric class, give it a threshold, and assert.

The metrics span RAG quality, safety, and general correctness. A representative slice:

Metric	What it measures	Typical use
AnswerRelevancyMetric	Is the answer on-topic for the input?	Chatbots, QA
FaithfulnessMetric	Does the answer stay grounded in retrieved context?	RAG hallucination checks
ContextualPrecisionMetric	Are relevant chunks ranked highly?	Retriever tuning
ContextualRecallMetric	Was all needed context retrieved?	Retriever coverage
HallucinationMetric	Does output contradict provided context?	Factuality gates
GEval	Custom criteria you describe in plain English	Anything bespoke
ToxicityMetric	Harmful or toxic content	Safety gates
BiasMetric	Biased or unfair statements	Compliance

A basic DeepEval test looks and feels like pytest, because it is pytest under the hood:

import pytest
from deepeval import assert_test
from deepeval.metrics import AnswerRelevancyMetric, FaithfulnessMetric
from deepeval.test_case import LLMTestCase


def test_support_answer_quality():
    test_case = LLMTestCase(
        input="How do I reset my password?",
        actual_output=(
            "Go to Settings, click Security, then Reset Password. "
            "You will get a confirmation email within a few minutes."
        ),
        retrieval_context=[
            "Users reset passwords under Settings > Security > Reset Password.",
            "A confirmation email is sent after a reset request.",
        ],
    )

    relevancy = AnswerRelevancyMetric(threshold=0.7)
    faithfulness = FaithfulnessMetric(threshold=0.8)

    assert_test(test_case, [relevancy, faithfulness])

The GEval metric is where DeepEval gets genuinely flexible. You describe the criterion in natural language and DeepEval builds a scored LLM judge around it:

from deepeval.metrics import GEval
from deepeval.test_case import LLMTestCase, LLMTestCaseParams

correctness = GEval(
    name="Correctness",
    criteria=(
        "Determine whether the actual output is factually correct "
        "and fully answers the question in the input."
    ),
    evaluation_params=[
        LLMTestCaseParams.INPUT,
        LLMTestCaseParams.ACTUAL_OUTPUT,
        LLMTestCaseParams.EXPECTED_OUTPUT,
    ],
    threshold=0.7,
)

Because everything is a pytest test, you run it with the DeepEval runner and get familiar output:

deepeval test run test_support_answer_quality.py

That pytest-native design is the reason DeepEval slots so cleanly into CI. If your team already trusts a green test suite, a failing eval reads exactly like a failing unit test. For the deeper walkthrough, see the DeepEval pytest guide, and for how it compares to RAGAS specifically, the RAGAS metrics guide.

What Langfuse brings: tracing production reality

Langfuse starts from a completely different question: what is my app doing right now, for real users? You instrument your application, and every LLM call, every retrieval step, every tool invocation becomes a span inside a trace. You get latency, token counts, cost, inputs, outputs, and the full nesting of a multi-step agent.

Instrumenting with the Langfuse Python SDK is a decorator plus context:

from langfuse import Langfuse, observe
from openai import OpenAI

langfuse = Langfuse()
client = OpenAI()


@observe()
def answer_question(question: str) -> str:
    response = client.chat.completions.create(
        model="gpt-4o-mini",
        messages=[{"role": "user", "content": question}],
    )
    output = response.choices[0].message.content

    # Attach a score to the current trace for later analysis
    langfuse.score_current_trace(
        name="user_thumbs_up",
        value=1,
        comment="collected from UI feedback widget",
    )
    return output

Once traces flow in, Langfuse gives you dashboards for cost per user, slowest chains, error rates, and prompt version comparisons. Crucially, it lets you attach scores to traces, either from user feedback, from an LLM-as-judge Langfuse runs on its side, or from an external evaluator that you push back in. That last option is the bridge to DeepEval.

Langfuse also manages prompts as versioned assets, so you can roll a prompt forward or back and correlate the change with the score trend. This is observability doing double duty as a lightweight experiment tracker.

The pattern: Langfuse captures, DeepEval scores sampled traces

Here is the architecture that experienced teams converge on, and it resolves the "versus" framing entirely.

1. Langfuse runs in production and captures 100 percent of traces. Cheap, always-on, no judge model in the hot path.
2. You sample a subset of those traces (say a few hundred a day, stratified by feature or by low-feedback sessions).
3. You pull that sample and run DeepEval or RAGAS metrics over it offline. This is where the expensive LLM-as-judge work happens, out of the request path.
4. You push the resulting scores back into Langfuse as trace scores, so your dashboards show quality trends next to cost and latency.
5. The same DeepEval metrics run in CI against a curated golden dataset, so regressions are caught before they ship.

The reason to sample rather than judge every trace live is cost and latency. LLM-as-judge evaluation can cost as much as the original generation, sometimes more if the judge is a stronger model. Running it on every request would double your bill and add seconds of latency. Sampling gives you a statistically useful quality signal at a fraction of the cost.

A sketch of the offline scoring job:

from langfuse import Langfuse
from deepeval.metrics import FaithfulnessMetric
from deepeval.test_case import LLMTestCase

langfuse = Langfuse()

# Pull a recent sample of traces to score
traces = langfuse.api.trace.list(limit=200, tags=["rag-chat"]).data
metric = FaithfulnessMetric(threshold=0.8)

for trace in traces:
    test_case = LLMTestCase(
        input=trace.input,
        actual_output=trace.output,
        retrieval_context=trace.metadata.get("retrieved_chunks", []),
    )
    metric.measure(test_case)

    langfuse.create_score(
        trace_id=trace.id,
        name="faithfulness",
        value=metric.score,
        comment=metric.reason,
    )

This gives you the best of both: Langfuse's complete picture of production, and DeepEval's rigorous, thresholded scoring, without paying to judge every single call.

Metrics head to head

Both tools can produce scores, but they source them differently. DeepEval ships a curated, research-backed metric suite you configure with thresholds. Langfuse gives you a scoring API and an LLM-as-judge feature, but expects you to define most criteria yourself (or import them).

Capability	DeepEval	Langfuse
Built-in RAG metrics	Yes (precision, recall, faithfulness)	Via custom judge or imported evals
LLM-as-judge	Yes (GEval, custom)	Yes (managed evaluators)
Deterministic metrics	Yes (exact match, JSON, regex)	Yes (via custom scores)
Thresholded pass/fail	Native to the assert model	You define thresholds downstream
Human feedback scores	Not the focus	First-class (thumbs, ratings)
Trace-linked scores	Requires export/import	Native

The practical read: if your question is "did quality regress, yes or no, fail the build," DeepEval answers it more directly. If your question is "what is the quality distribution across real users this week," Langfuse answers it more directly. For a broader field comparison across tools, the LLM evals comparison is worth a read.

Pricing and self-hosting

Pricing changes often, so treat the following as approximate and verify on each vendor's current pricing page before you budget.

Tier	DeepEval	Langfuse
Open source / self-host	Free, core is open source	Free, fully open source
Free cloud	Confident AI free tier (limited)	Free hobby tier with trace limits
Team / Pro	Usage-based on Confident AI	Per-seat plus trace volume
Enterprise	Custom, SSO and support	Custom, SSO, SOC2, dedicated

Both tools have genuinely usable open-source cores, which matters if you are cost-sensitive or have data-residency requirements. DeepEval's evaluation library runs entirely locally (you only pay for the judge model's API calls). Langfuse can be self-hosted with Docker and a Postgres and ClickHouse backend, so you keep every trace inside your own infrastructure. The hosted offerings (Confident AI for DeepEval, Langfuse Cloud for Langfuse) add dashboards, collaboration, and managed scaling.

The main cost driver for both is not the tool license, it is the judge model. Any LLM-as-judge metric costs real tokens. Budget for that separately, and use sampling to keep it bounded.

CI integration compared

DeepEval was built for CI. Because tests are pytest tests, a GitHub Actions job is nearly boilerplate:

name: llm-eval
on: [pull_request]

jobs:
  deepeval:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: '3.12'
      - name: Install dependencies
        run: pip install deepeval
      - name: Run evaluation suite
        env:
          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
        run: deepeval test run tests/evals/

A failing metric fails the job, which blocks the merge. That is exactly the quality gate you want. For the full treatment of wiring evals into pipelines, thresholds, and non-determinism handling, see the LLM evaluation quality gates guide.

Langfuse's relationship to CI is looser. It is not a test runner, so you do not "fail a build" in Langfuse directly. Instead, you can query Langfuse scores from a CI step and assert on aggregates, or run Langfuse-managed evaluators on a dataset and check the result. It works, but it is a step you assemble rather than a first-class runner. This is why the combined pattern usually keeps DeepEval as the CI gate and Langfuse as the production monitor.

When to use each (and when to use both)

Choose DeepEval alone when your primary need is a quality gate in CI, you have or can build a golden dataset, and you are not yet drowning in production traffic. It is the fastest path to "the build fails when the model gets worse."

Choose Langfuse alone when your primary pain is not knowing what production is doing: cost surprises, latency spikes, mysterious failures in multi-step agents. Observability first, formal evaluation later.

Choose both, wired together, once you are running real traffic and want a closed loop: Langfuse captures everything, you sample and score with DeepEval, scores flow back to Langfuse, and the same DeepEval suite guards CI. This is the mature end state, and it is the reason the "versus" question is slightly wrong. For agent-specific evaluation across both, see the AI agent eval testing guide.

Common pitfalls

Do not judge every production trace live. It doubles cost and adds latency. Sample.

Do not treat a single eval run as ground truth. LLM-as-judge scores wobble run to run. Average multiple runs before you trust a number, especially near a threshold.

Do not confuse a trace score with a passing gate. A Langfuse dashboard trending up is reassuring, but it is not the same as a hard pass/fail that blocks a bad deploy. Keep the DeepEval gate.

Do not skip the golden dataset. Both tools are only as good as the data you evaluate against. A stale or leaky dataset flatters every model.

Do not forget cost controls. Cache judge results where possible, pick a cheaper judge model for cheap metrics, and cap sample size.

Do not let the two tools drift apart. If Langfuse records a trace field that DeepEval expects (the retrieved chunks, for instance), make sure your instrumentation actually populates it, or your offline scoring job will silently score against empty context and report misleadingly low faithfulness. The bridge between capture and scoring is only as reliable as the metadata you attach to each trace, so treat the trace schema as a contract between the two systems and validate it the same way you would validate any API payload.

Do not skip human review entirely. Automated scores are the backbone of the loop, but periodically pulling a handful of the lowest-scored traces and reading them by hand keeps your judge honest. It is the fastest way to catch a metric that is systematically wrong (for example, a faithfulness judge that penalizes correct answers phrased more concisely than the source), which no amount of automation will surface on its own.

Frequently Asked Questions

Is DeepEval a replacement for Langfuse?

No. DeepEval is an offline evaluation and testing framework, while Langfuse is a production tracing and observability platform. They solve different halves of the LLM quality problem. Most teams use both: Langfuse to capture production traces and DeepEval to score sampled traces and gate CI. Treating one as a drop-in for the other leaves a real gap.

Can I use DeepEval and Langfuse together?

Yes, and it is the recommended pattern. Langfuse captures every production trace, you sample a subset, run DeepEval metrics on that sample offline, then push the resulting scores back into Langfuse as trace scores. The same DeepEval suite also runs in CI against a golden dataset. This gives you production visibility and a hard quality gate at once.

Which tool is better for RAG evaluation?

DeepEval has a stronger built-in RAG metric suite (contextual precision, contextual recall, faithfulness) that you can threshold and assert on directly. Langfuse is better at showing which retrieved chunks were actually used in production. For scoring RAG quality in CI, lead with DeepEval or RAGAS. For debugging live retrieval, lead with Langfuse.

Do DeepEval and Langfuse cost money to run?

Both have free, self-hostable open-source cores. The real cost is the judge model: any LLM-as-judge metric spends tokens on every evaluation. DeepEval runs locally and you only pay your model provider. Langfuse can be self-hosted so traces stay in your infrastructure. Hosted tiers add dashboards and scaling for a subscription. Verify current pricing on each vendor's page.

Why sample traces instead of evaluating all of them?

Because LLM-as-judge evaluation can cost as much as the original generation and adds seconds of latency. Judging every production call would roughly double your bill and slow responses. Sampling a few hundred stratified traces gives a statistically meaningful quality signal at a small fraction of the cost, which is why the capture-then-sample pattern exists.

Does Langfuse have built-in evaluation metrics?

Langfuse offers managed LLM-as-judge evaluators and a flexible scoring API, but it does not ship the same curated, research-backed metric library as DeepEval. You typically define criteria yourself or import evals. If you want ready-made faithfulness and relevancy metrics with thresholds, pair Langfuse with DeepEval or RAGAS rather than relying on Langfuse alone.

Can either tool block a deploy on a regression?

DeepEval can, directly, because its tests are pytest tests that fail the CI job when a metric falls below threshold, which blocks the merge. Langfuse is not a test runner, so blocking a deploy with it means querying its scores in a CI step and asserting on aggregates yourself. For hard pass/fail gates, keep DeepEval as the gate and Langfuse as the monitor.

Conclusion

DeepEval and Langfuse are not really competitors, they are two halves of a healthy LLM quality practice. DeepEval gives you pytest-style evaluation with a rich metric library and a natural home in CI, where it fails the build when quality regresses. Langfuse gives you full production observability, capturing every trace with cost and latency, and lets you attach scores to real user sessions. The mature pattern uses both: Langfuse captures, you sample, DeepEval scores, and the scores flow back while a DeepEval gate guards your pipeline.

Start with whichever pain is louder. If you are shipping blind, add Langfuse first. If you keep breaking quality on merges, add DeepEval first. Then close the loop. Explore the full catalog of QA and evaluation skills at /skills to wire these tools into your workflow, and read the DeepEval pytest guide next to build your first eval suite today.