Compare Promptfoo and Respan side by side. Both are tools in the Observability, Prompts & Evals category.
Updated March 10, 2026
Choose Promptfoo if completely free and open source (MIT license).
Choose Respan if unified observability across all LLM providers in one dashboard.
Want to compare Promptfoo and Respan on your own traffic?
Respan lets you trace LLM and agent calls across any model or framework, A/B test prompts on production traffic, and route requests across 250+ models through one gateway. Free tier covers 10K traces per month. Setup in 5 minutes, no credit card.
| Category | Observability, Prompts & Evals | Observability, Prompts & Evals |
| Website | promptfoo.dev | respan.ai |
Promptfoo is an open-source tool for testing prompts, agents, and RAGs, with AI red teaming, pentesting, and vulnerability scanning for LLMs. Built under MIT license, Promptfoo was originally developed for LLM apps serving over 10 million users in production. The platform compares performance across GPT, Claude, Gemini, Llama, and more with simple declarative configs supporting command line and CI/CD integration. The Community version includes up to 10,000 probes monthly at no charge, with infrastructure costs typically USD 50-500 monthly for hosting and LLM API calls. Developers praise Promptfoo for its speed, quality-of-life features like live reloads and caching, security features including red teaming, and budget-friendly open-source model. However, the CLI-focused approach creates friction for non-technical team members, and the platform lacks end-to-end observability, version control for prompts, and test management features needed for complex production agents.
Respan Observability provides comprehensive LLM monitoring and debugging for AI applications in production. The platform tracks every prompt, completion, latency metric, cost, and quality signal across all LLM providers from a single dashboard, giving engineering teams full visibility into their AI stack.
The observability suite includes real-time tracing of LLM calls with detailed breakdowns of token usage, response times, and error rates. Teams can set up alerts for cost spikes, latency degradation, or quality drops, and drill into individual traces to debug issues. Built-in evaluation tools enable automated quality scoring of LLM outputs using custom rubrics or reference-based evaluation.
Prompt management features allow teams to version, test, and deploy prompts without code changes. A/B testing capabilities enable comparing model performance across different configurations, and semantic caching identifies repeated queries to reduce costs. The platform integrates with popular frameworks like LangChain, LlamaIndex, and the Vercel AI SDK.
Tools for monitoring LLM applications in production, managing and versioning prompts, and evaluating model outputs. Includes tracing, logging, cost tracking, prompt engineering platforms, automated evaluation frameworks, and human annotation workflows.
Browse all Observability, Prompts & Evalstools →One platform for routing, observability, tracing, and evals across every LLM provider.