Promptfoo vs Opik

Detailed side-by-side comparison to help you choose the right tool

Promptfoo

🔴Developer

Testing & Quality

Open-source LLM testing and evaluation framework for systematically testing prompts, models, and AI agent behaviors with automated red-teaming.

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Starting Price

Free

Opik

🔴Developer

Testing & Quality

Open-source LLM observability and evaluation platform by Comet for tracing, testing, and monitoring AI applications and agentic workflows.

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Starting Price

Free

Feature Comparison

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FeaturePromptfooOpik
CategoryTesting & QualityTesting & Quality
Pricing Plans8 tiers8 tiers
Starting PriceFreeFree
Key Features

      Promptfoo - Pros & Cons

      Pros

      • Comprehensive red-teaming fills a critical gap in LLM safety tooling
      • Free Community tier includes all core evaluation features
      • Declarative YAML config makes test suites maintainable and version-controllable
      • OpenAI acquisition suggests strong continued development and integration

      Cons

      • OpenAI acquisition may affect future open-source direction
      • CLI-focused interface may be less accessible for non-technical users
      • Enterprise pricing not publicly listed

      Opik - Pros & Cons

      Pros

      • Fully open-source with no feature gating — self-host with complete functionality at zero cost
      • Automated prompt optimization removes manual trial-and-error from prompt engineering
      • Built-in guardrails provide safety and compliance without external dependencies
      • CI/CD-native testing catches LLM regressions before they reach production
      • Comprehensive tracing works across LLM calls, RAG systems, and multi-agent workflows
      • Free cloud tier eliminates infrastructure management for small teams and individual developers

      Cons

      • Self-hosted deployment requires managing infrastructure (ClickHouse, Redis, etc.)
      • Enterprise pricing is not publicly listed — requires contacting sales
      • Focused on LLM applications — not designed for traditional ML model training workflows
      • Learning curve for teams new to observability and evaluation concepts

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