DeepEval vs Promptfoo in 2026 - Pytest or YAML for LLM Evals

DeepEval and Promptfoo are the two names that come up when a team decides it needs real LLM evals in CI. They solve the same problem from opposite directions. DeepEval is pytest for LLM apps - SDK-first, Python, metrics-led. Promptfoo is YAML config in version control - CLI-driven and red-teaming-led. Neither is better in the abstract. The right answer depends on how your team already works and whether security testing is part of the job. I have added Braintrust as the third option, because if you want a managed platform that ties evals to observability and blocks bad merges, that is a different shape again.

The short version

Tool	Best for	Approach	License / price
DeepEval	Python teams who think in tests	SDK-first, pytest-style	Apache-2.0 OSS / cloud from $200/mo
Promptfoo	Security and CI teams	YAML config, CLI	MIT, free forever / Enterprise contact-sales
Braintrust	Turnkey evals + CI gates + observability	Managed platform	Free / $249/mo, hybrid self-host

DeepEval: pytest for LLM apps

DeepEval brings the pytest mental model to LLM evaluation. You use assert_test, mark tests with @pytest.mark, and run the whole suite with deepeval test run - it drops into an existing Python test suite and CI pipeline like it belongs there. Reviewers call it the most SDET-friendly LLM testing framework. Under the runner sits a deep library of 50+ research-backed metrics, including the widely-cited G-Eval, plus hallucination, answer relevancy, faithfulness and RAG metrics. The OSS framework is Apache-2.0 and fully usable with no Confident AI account, and DeepTeam adds red-teaming with OWASP and NIST coverage. It is an independent YC W25 startup, founded in 2024 by Jeffrey Ip, with roughly 12.6k GitHub stars.

Two gotchas, both from the same design choice. Nearly all the metrics are LLM-as-judge, so every test case fires another LLM inference - big suites rack up real API bills, runs take minutes not seconds, and CI needs explicit timeouts. The second is the cloud: the Free tier is stingy at 5 test runs per week, and Confident AI’s Starter at $200/mo jumps 10x to Team at $2,000/mo with nothing in between. Live in the free framework as long as you can, and cost-model the judge calls before you scale the suite.

Promptfoo: YAML config and serious red-teaming

Promptfoo is config-driven, not SDK-first. Your evals are declarative YAML that lives in version control next to your prompts - a reviewer can read what “good” looks like in a pull request, rather than tracing assertions through Python files. Security and CI teams tend to love that shape. It is MIT-licensed and free forever, with roughly 23.5k GitHub stars, the most of the major eval tools.

The signature specialty is red-teaming. It ships 50+ attack plugins and built-in presets for the OWASP LLM Top 10, plus OWASP Agentic and NIST, probing for prompt injection, jailbreaks, PII leakage and more. If security testing is part of your eval story, nothing else in the open-source category is this complete. It also acts as its own OTLP receiver with a built-in trace viewer, so no separate Jaeger or Tempo to run.

The gotchas: the free tier caps red-teaming at 10k probes per month, and Enterprise and On-Premise are contact-sales only with no public pricing. And the ownership angle matters - OpenAI acquired Promptfoo on 9 March 2026. Both sides state it stays open source under MIT and folds into OpenAI Frontier, but long-term governance of an OSS project under a single large owner is a fair thing to weigh. The Promptfoo page covers it.

Braintrust: the managed platform that ties it together

If what you actually want is evals wired to observability with quality gates in your pipeline, Braintrust is a different animal. Its autoevals library ships working scorers, and its CI/CD quality gates can block a merge on a statistically significant regression - human review, automated scorers, tracing and datasets all share one system, with no per-seat charge.

The trade-offs: billing meters “processed data” in GB, counting every byte, with no hard spending cap, and the $0 Starter jumps straight to $249/mo. Self-host is hybrid-VPC only and Enterprise-only, and the Brainstore backend is closed. It is more turnkey than assembling DeepEval or Promptfoo into a pipeline yourself, but you pay for it, and you watch the meter.

DeepEval vs Promptfoo: which should you pick?

• Your team writes Python and thinks in tests - DeepEval. The pytest shape and G-Eval metrics fit the way you already work.
• Security testing and red-teaming are part of the job - Promptfoo. The OWASP mapping and 50+ attack plugins are the differentiator.
• You want evals in version control as declarative config - Promptfoo, free forever under MIT.
• You want research-backed scoring metrics out of the box - DeepEval, as long as you cost-model the LLM-as-judge calls.
• Vendor independence is a hard requirement - DeepEval. Promptfoo is now an OpenAI company; Confident AI is independent.
• You want a turnkey managed platform with CI gates and observability - Braintrust, with billing alerts on from day one.

Both DeepEval and Promptfoo are genuinely good, and plenty of teams run both - Promptfoo for security probes, DeepEval for metric-driven scoring. If you have to pick one, let your stack decide: Python test suites point to DeepEval, YAML-in-git and security testing point to Promptfoo. Every price and date here was read from each vendor’s own materials, verified on 26 July 2026, and this category ships breaking changes monthly, so we re-verify every 30 days.