Helicone

Helicone is an open-source LLM observability and gateway platform. It works as a proxy — you point your OpenAI, Anthropic, or other LLM calls at Helicone (or use the SDKs), and it logs every request, tracks cost, caches responses, rate-limits, and provides dashboards and alerts. It's a YC-backed project with 8,000+ GitHub stars, used by 2,000+ companies.

Key Features

• Request Logging & Tracing: Every LLM API request logged with full traces.
• Cost Tracking: Per-request, per-model, and per-project cost analytics.
• Server-Side Caching: Cache LLM responses to cut cost and latency.
• Rate Limiting: Control request volume per user, key, or project.
• Retries: Automatic retry logic for failed requests.
• Custom Properties: Tag requests with custom metadata for analysis.
• Prompt Feedback: Collect user feedback on responses.
• Fine-Tuning Support: Helicone Fine-Tune — gather datasets from production traffic.
• A/B Testing: Compare prompt variants on real traffic.
• Self-Host or Cloud: Docker self-hosting or managed Helicone Cloud with a free tier.

Use Cases

Who Should Use This Tool?

• LLM Product Teams: Teams shipping LLM apps who want to see, control, and reduce API spend.
• Platform Teams: Teams that need a gateway layer for caching and rate limiting.
• Data-Driven Builders: Teams using production traffic to build fine-tuning datasets.

Problems It Solves

1. API cost visibility: Per-model and per-request cost tracking pinpoints spend.
2. Latency and cost reduction: Caching repeated requests cuts both.
3. Traffic control: Rate limiting and retries keep services stable.

Pricing

Advantages & Unique Selling Points

Compared to Competitors:

1. Gateway + observability: Not just logging — caching, rate limiting, and retries in one layer.
2. Provider-agnostic proxy: Works with OpenAI, Anthropic, Gemini, Mistral, and custom endpoints.
3. Fine-tuning pipeline: Turn production traffic into training datasets.

What Makes It Stand Out:

• YC-backed with 2,000+ companies and 8,000+ GitHub stars.
• Minimal integration: change base_url or one SDK line.
• Self-hostable with the same feature set as cloud.

Getting Started

Quick Start Guide

1. Deploy or sign up: Run the Docker image or create a Helicone Cloud account.
2. Get keys: Create a project and API key.
3. Point your calls at Helicone: Change the base URL of your OpenAI/Anthropic client to Helicone.
4. Monitor: Watch requests, costs, and latency in the dashboard.

Integration

Integrates with:

• OpenAI, Anthropic, Gemini, Mistral, and custom endpoints
• Python and TypeScript SDKs
• Any HTTP-capable LLM client

Frequently Asked Questions

How does Helicone intercept my LLM calls?

It works as a proxy — you change the base URL of your LLM client (or use the SDK) and all traffic flows through Helicone.

Is it really open source?

Yes — the core is open source with 8,000+ GitHub stars, self-hostable via Docker.

Does it support caching?

Yes — server-side caching is a core feature for cutting cost and latency.

Alternatives

If Helicone isn't the right fit, consider these alternatives:

• Langfuse: Open-source observability with 40+ framework integrations.
• LangSmith: LangChain's platform with deployment built in.
• AgentOps: Agent-focused session replay and execution graphs.

Tips & Best Practices

1. Turn on caching for repeated calls: High-traffic prompts benefit most from server-side caching.
2. Use custom properties: Tag requests by feature or user for better analysis.
3. Set rate limits early: Protect your backend from agent bursts.

Conclusion

Helicone combines LLM observability with a gateway — logging, cost tracking, caching, rate limiting, and fine-tuning pipelines in one open-source platform. If you want to see and control every LLM API call, Helicone is a battle-tested choice.