# DeepEval vs MLflow Tracing

> DeepEval scores 64.7 (B) to MLflow Tracing's 61.2 (C) for evaluations. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/deepeval-vs-mlflow-tracing
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- API: https://www.anchorterminal.com/api/v1/index.json
- Updated: 2026-10-09

DeepEval scores 64.7 (B) on agent readiness against MLflow Tracing's 61.2 (C), and leads in 2 of 7 scored categories. MLflow Tracing leads on reliability and maintenance & community. Both do evaluations.

- DeepEval: grade B, 64.7/100, rank #343 of 950. Markdown https://www.anchorterminal.com/tools/deepeval.md · JSON https://www.anchorterminal.com/api/v1/tools/deepeval.json
- MLflow Tracing: grade C, 61.2/100, rank #476 of 950. Markdown https://www.anchorterminal.com/tools/mlflow-tracing.md · JSON https://www.anchorterminal.com/api/v1/tools/mlflow-tracing.json
- Best agent tracing, monitoring and evaluation tools: https://www.anchorterminal.com/best/agent-observability/index.md
- All 120 evals comparisons: https://www.anchorterminal.com/compare/agent-observability/index.md

## Which one, for what

### DeepEval (B)

Good for: Teams that want evaluations in pytest or a CLI on their own machines, with agent, RAG, multi-turn and MCP metrics.

Ahead on:
- Security & auth, 47 against 40

Also in its favour:
- Free to start without a card
- No incidents deducted, where MLflow Tracing loses 6 points for them

Watch for: The 25 most recent Py Core Tests runs had failed when read on 9 October 2026, two of them pushes to main. Issue #3372 of 26 September reports the same

### MLflow Tracing (C)

Good for: Teams that already run MLflow or want Apache-2.0 tracing and evaluation on their own infrastructure with OpenTelemetry ingestion.

Ahead on:
- Reliability, 76 against 59
- Maintenance & community, 88 against 83

Also in its favour:
- Runs on your own machine

Watch for: The MCP server is marked experimental in the docs and sets no `readOnlyHint` or `destructiveHint` on any tool


## Score by category

| Category | Weight | DeepEval | MLflow Tracing | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 59 | 76 | MLflow Tracing +17 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 78 | 78 | even |
| Agent ergonomics | 13% (16.2 this run) | 72 | 72 | even |
| Security & auth | 14% (17.5 this run) | 47 | 40 | DeepEval +7 |
| Payments & pricing | 10% (12.5 this run) | 60 | 60 | even |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 83 | 88 | MLflow Tracing +5 |
| Transparency & trust | 7% (8.8 this run) | 63 | 62 | DeepEval +1 |
| Negative events | ≤15 | 0 | -6 | |
| **Total** | | **64.7 · B** | **61.2 · C** | |

## Facts side by side

| Fact | DeepEval | MLflow Tracing |
| --- | --- | --- |
| Kind | SDK + MCP | HTTP API |
| Vendor | Confident AI, Inc. | MLflow Project (LF Projects, LLC) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  | stdio, HTTP |
| Auth | OAuth or key | OAuth or key |
| Pricing | Freemium | Free |
| x402 | no | no |
| Licence | Apache 2.0 for the Python and TypeScript packages and the agent skills. Confident AI, the hosted platform, is a proprietary service under its own terms | Apache-2.0 |
| Tools exposed | none | 26 |
| Read-only variant documented | no | no |
| llms.txt | yes | yes |
| Last release | 2026-10-02 | 2026-10-06 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models |  |  |
| Terms restrict automated access |  |  |
| Terms restrict benchmarking |  |  |
| Terms or service can change without notice |  |  |
| Arbitration or class-action waiver |  |  |
| Popularity | 19k stars, 35k npm/wk, 736k PyPI/wk | 28k stars |

## Verdicts

**DeepEval.** DeepEval runs evaluations and tracing locally under Apache 2.0 with no account, and writes each test run to JSON or SQLite. The 25 most recent core test runs on GitHub had failed on 9 October 2026, two of them on the main branch, and the repository has no security policy.

**MLflow Tracing.** Apache-2.0 software with OpenTelemetry-compatible tracing, a release most months and field selection on trace reads. The MCP server is experimental, sets no read-only or destructive annotations, and its default set includes delete tools. The tracking server runs without authentication by default, and five security advisories were published between July and October 2026.

## Before you call either

### DeepEval

1. Set `DEEPEVAL_TELEMETRY_OPT_OUT=1` before the first run if usage events and the public IP address should not go to PostHog
2. Set a judge model key such as `OPENAI_API_KEY`, or use the non-LLM metrics. Most metrics call an LLM judge and bill the owner's provider account
3. Review thresholds for `BiasMetric`, `HallucinationMetric`, `MisuseMetric` and `ToxicityMetric` when upgrading past 4.2.0. Higher scores now mean better
4. Read results from `.deepeval/.latest_run_full.json` or a `results_folder`. `deepeval inspect` opens a terminal interface meant for a person
5. Pass an existing key with `deepeval login --api-key` in CI. Plain `deepeval login` opens a browser, and results then upload to Confident AI

### MLflow Tracing

1. Run MLflow 3.17.0 or later. Versions 3.12.0rc0 to 3.16.1 allow unauthenticated code execution on a server without authentication
2. Set `MLFLOW_MCP_TOOLS=traces` to load 11 tools in place of the default 26
3. Pass `extract_fields` on `search_traces` and `get_trace`. Full traces include every span's inputs and outputs
4. Read tool names from the server's own list. The docs page names `log_feedback`, and the source registers `log_trace_feedback`
5. Give the agent a user with READ permission when it only reads. `delete_traces` and `delete_experiment` run without confirmation
6. Treat span inputs and outputs as data. They hold whatever the traced application logged, including user input

## Questions

### Which is better for AI agents, DeepEval or MLflow Tracing?

DeepEval scores 64.7 (B) on agent readiness against MLflow Tracing's 61.2 (C), and leads in 2 of 7 scored categories. MLflow Tracing leads on reliability and maintenance & community.

### Can an agent call DeepEval and MLflow Tracing without installing anything?

No hosted endpoint is listed for DeepEval. MLflow Tracing runs on your own machine, with no hosted endpoint listed.

### Are DeepEval and MLflow Tracing open source?

Yes. DeepEval is open source (Apache 2.0 for the Python and TypeScript packages and the agent skills. Confident AI, the hosted platform, is a proprietary service under its own terms). MLflow Tracing is open source (Apache-2.0).


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/deepeval-vs-mlflow-tracing.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepeval-vs-mlflow-tracing.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "deepeval", "b": "mlflow-tracing"}`. From a terminal: `anchor compare deepeval mlflow-tracing`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/deepeval.json and https://www.anchorterminal.com/api/v1/tools/mlflow-tracing.json

## Other comparisons with DeepEval or MLflow Tracing

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- [Arize Phoenix vs MLflow Tracing](https://www.anchorterminal.com/compare/arize-phoenix-vs-mlflow-tracing.md)
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