Promptfoo vs DeepEval
Detailed side-by-side comparison to help you choose the right tool
Promptfoo
🔴DeveloperLLM Evaluation & Testing
Open-source LLM evaluation and red-teaming framework for testing prompts, models, and agents locally or in CI.
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FreeDeepEval
🔴DeveloperTesting & Quality
Open-source LLM evaluation framework with 50+ research-backed metrics including hallucination detection, tool use correctness, and conversational quality. Pytest-style testing for AI agents with CI/CD integration.
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FreeFeature Comparison
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💡 Our Take
Choose Promptfoo if you need a broader AI security testing surface that includes red teaming, guardrails, model security, MCP Proxy, and code scanning. Choose DeepEval if your main need is a developer-focused evaluation framework for LLM outputs and you do not need Promptfoo’s broader security platform positioning.
Promptfoo - Pros & Cons
Pros
- ✓Apache 2.0 local runner with CI-friendly configuration
- ✓Broad provider and assertion coverage
- ✓Evaluation and OWASP-oriented red teaming in one workflow
Cons
- ✗Good test datasets still require substantial human judgment
- ✗LLM-as-judge assertions add cost and evaluator bias
- ✗Hosted Team and Enterprise pricing is not publicly verified here
DeepEval - Pros & Cons
Pros
- ✓Comprehensive LLM evaluation metric suite — 50+ metrics covering hallucination, relevancy, tool correctness, bias, toxicity, and conversational quality
- ✓Pytest integration feels natural for Python developers — LLM tests run alongside unit tests in existing CI/CD pipelines with deployment gating
- ✓Tool correctness metric specifically designed for validating AI agent behavior — checks correct tool selection, parameters, and sequencing
- ✓Open-source core (MIT license) runs locally at zero platform cost — only pay for LLM API calls used by metrics
- ✓Confident AI cloud offers low-cost tracing at $1/GB-month with adjustable retention — competitive pricing for the observability tier
- ✓Active development with frequent new metrics and features — grew from 14+ to 50+ metrics, backed by Y Combinator
Cons
- ✗Metrics require LLM API calls (GPT-4, Claude) for evaluation — adds cost that scales with dataset size and metric count
- ✗Some metrics can be computationally expensive and slow for large evaluation datasets, especially multi-turn conversational metrics
- ✗Confident AI cloud required for collaboration, dataset management, monitoring, and dashboards — open-source alone lacks team features
- ✗Metric accuracy depends on the evaluator model quality — weaker models produce less reliable scores, creating cost pressure to use expensive models
- ✗Free tier of Confident AI is restrictive: 5 test runs/week, 1 week data retention, 2 seats, 1 project
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