DeepEval vs Promptfoo
Detailed side-by-side comparison to help you choose the right tool
DeepEval
🔴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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FreePromptfoo
🔴DeveloperAI Evaluation
Open-source CLI and library for testing, evaluating, and red-teaming LLM prompts, models, and RAG pipelines — runs locally on your machine or in CI.
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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.
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
Promptfoo - Pros & Cons
Pros
- ✓Covers 6 product areas listed on the website: Red Teaming, Guardrails, Model Security, MCP Proxy, Code Scanning, and Evaluations.
- ✓Community plan is described as Free Forever and includes local or self-hosted operation, all LLM evaluation features, vulnerability scanning, and red teaming up to 10k probes per month.
- ✓Useful beyond prompt testing because it includes real-time guardrail positioning, model security monitoring, MCP Proxy protection, and IDE/CI/CD code scanning for LLM vulnerabilities.
- ✓Strong fit for regulated workflows because the website names 4 industry solution areas: Financial Services, Insurance, Telecommunications, and Real Estate.
- ✓Supports development workflows where evaluations and red-team checks can run before merge or release instead of relying only on post-deployment monitoring.
- ✓The site displays a public 20.6k metric alongside its open-source and community positioning, indicating substantial visible adoption or repository activity.
Cons
- ✗Public paid pricing is quote-based: Enterprise and On-Premise are listed as Custom rather than fixed monthly or annual prices.
- ✗The product surface is broad, so teams that only need simple prompt regression tests may find the security, guardrails, MCP proxy, and model-security positioning more than they need.
- ✗Red-teaming and evaluation quality still depend on well-designed test cases, assertions, graders, and representative datasets.
- ✗The website emphasizes development-time and security testing more than production observability, so teams may still need a tracing or monitoring platform alongside Promptfoo.
- ✗Enterprise suitability is clear, but self-serve details such as exact paid seat limits, usage caps beyond Community red-team probes, hosted data retention, and final contract terms are not visible in the public pricing content.
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