Humanloop vs Helicone
Detailed side-by-side comparison to help you choose the right tool
Humanloop
🟡Low CodeBusiness Analytics
Former LLMOps platform for prompt engineering and evaluation, acquired by Anthropic in August 2025. Technology now integrated into Anthropic Console as the Workbench and Evaluations features.
Was this helpful?
Starting Price
DiscontinuedHelicone
🔴DeveloperBusiness Analytics
Open-source LLM observability platform and API gateway that provides cost analytics, request logging, caching, and rate limiting through a simple proxy-based integration requiring only a base URL change.
Was this helpful?
Starting Price
FreeFeature Comparison
Scroll horizontally to compare details.
Humanloop - Pros & Cons
Pros
- ✓Core evaluation technology preserved and enhanced within Anthropic's enterprise platform with direct model provider integration
- ✓Pioneered evaluation-driven development methodology that became an industry standard for LLMOps
- ✓Prompt-as-code approach with version control, branching, and rollback brought software engineering rigor to prompt management
- ✓Human-in-the-loop workflows enabled domain experts to contribute to model improvement without engineering knowledge
- ✓Anthropic integration means evaluation tools now have native access to Claude model internals for deeper testing capabilities
Cons
- ✗No longer available as a standalone product — requires commitment to Anthropic's ecosystem for continued access
- ✗Teams using non-Anthropic models (GPT, Gemini) lose access to Humanloop's model-agnostic evaluation capabilities
- ✗Migration from standalone Humanloop to Anthropic Console required significant workflow changes for existing customers
- ✗Some advanced features from the standalone product may not have full parity in the integrated Anthropic Console version
Helicone - Pros & Cons
Pros
- ✓Proxy-based integration requires only a base URL change — genuinely zero-code setup for OpenAI and Anthropic users
- ✓Real-time cost analytics with per-user, per-feature, and per-model breakdowns are best-in-class for LLM spend management
- ✓Gateway-level request caching can reduce API costs 20-50% for applications with repetitive queries
- ✓Open-source with self-hosted option gives full data control for security-conscious teams
- ✓Built-in rate limiting and retry logic at the proxy layer eliminates operational code from your application
Cons
- ✗Proxy architecture adds 20-50ms latency per request, which compounds in latency-sensitive agent loops
- ✗Individual request-level visibility doesn't capture multi-step agent workflows or retrieval pipeline context natively
- ✗Session and trace grouping features are less mature than Langfuse or LangSmith's dedicated tracing capabilities
- ✗Free tier limited to 10,000 requests/month — production applications will quickly need the $20/seat/month Pro plan
Not sure which to pick?
🎯 Take our quiz →🔒 Security & Compliance Comparison
Scroll horizontally to compare details.
Price Drop Alerts
Get notified when AI tools lower their prices
Get weekly AI agent tool insights
Comparisons, new tool launches, and expert recommendations delivered to your inbox.