DeepEval vs Helicone (2026)
Updated March 10, 2026
Overview
DeepEval is an open-source LLM evaluation framework built for unit testing AI outputs. It provides 14+ evaluation metrics including hallucination detection, answer relevancy, and contextual recall. Integrates with pytest, supports custom metrics, and works with any LLM provider for automated quality assurance in CI/CD pipelines.
Helicone is an open-source LLM observability and proxy platform. By adding a single line of code, developers get request logging, cost tracking, caching, rate limiting, and analytics for their LLM applications. Helicone supports all major LLM providers and offers both proxy and async logging modes. Popular with startups for its generous free tier and simple integration.
Strengths and tradeoffs
What each tool does well, and the limitations to keep in mind.
Pros
- Open-source
- Comprehensive metrics
Cons
- Manual setup
Pros
- Open-source with 5.2K GitHub stars and strong community support
- Minimal latency overhead (under 1ms for self-hosted)
- Flexible deployment options including cloud-hosted, self-hosted, or SDK-only
- Y Combinator-backed with SOC 2 Type II and HIPAA compliance
- No code changes required for AI Gateway features like caching and rate limiting
Cons
- Adds computational cost and one network hop for cloud deployment
- Limited pricing transparency on website
- May require infrastructure expertise for self-hosted setup
Compare DeepEval and Helicone 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 1,000+ models through one gateway.
Try Respan free