Helicone is an open-source LLM observability platform and AI gateway that helps developers monitor, debug, and optimize production AI applications. With a simple base URL change, you get instant logging of requests, costs, latency, and token usage across 100+ models from providers like OpenAI, Anthropic, and Google. It combines observability with intelligent routing, caching, and prompt management, making it a fit for teams building chatbots, agents, and document processing pipelines who need visibility and cost control.

• Unified AI Gateway: Access 100+ LLM models through one API with 0% markup, plus automatic failover between providers to keep your app running during outages.
• One-Line Integration: Change your base URL and add an API key to start logging every request with latency, token counts, costs, and response content.
• Cost Tracking & Analytics: Real-time cost breakdowns by user, feature, model, or custom property so you can spot expensive workflows and optimize spend.
• Prompt Management: Version, test, and deploy prompts through the dashboard without code changes, including A/B testing on production data.
• Caching: Serve cached responses to duplicate requests to cut API costs and reduce latency for repetitive queries.
• Sessions & Tracing: Group multi-step workflows to debug agents, chatbots, and complex pipelines with session-level metrics.
• Alerts and Webhooks: Monitor LLM activity and trigger alerts or automated workflows based on usage, errors, costs, and other events.
• Flexible Deployment: Use the hosted cloud version or self-host with a single Docker command, with SOC 2 and GDPR compliance available.

• Trivial Setup: The proxy approach means you just swap the base URL, so most teams can start logging LLM calls in minutes without SDK-level instrumentation.
• Strong Cost Visibility: Per-user, per-feature, and per-model cost analytics are among the best in the space, with case studies showing up to 65% cost reductions through caching and routing.
• Multi-Provider Support: Route across 100+ providers from a single gateway, useful for experimenting or switching vendors based on cost and latency.
• Responsive Team: Users consistently praise the developer-focused team for fast responses, community engagement, and steady feature updates.
• Deployment Flexibility: Cloud, hybrid, and self-hosted options let you keep sensitive data in your own infrastructure.
• Generous Free Tier: 10,000 requests per month with no credit card required makes it easy to try in real projects.

• Additional Network Layer: Using Helicone as a proxy or gateway adds another service between your application and the model provider. While Helicone reports minimal latency in its own testing, the actual effect can vary depending on your integration and request path.
• Potential Need for Dedicated Agent Tracing Tools: Helicone supports sessions, traces, and multi-step workflow debugging, but teams with highly complex agent architectures may still prefer tools built specifically for deep agent tracing.
• Varying Cost Tracking by Integration: Helicone's AI Gateway uses its Model Registry for accurate cost calculations, whereas direct provider integrations rely on best-effort pricing data across supported models. Consequently, unsupported or newly added models may require pricing information to be added manually.

• Production LLM Monitoring: A SaaS team logs every OpenAI and Anthropic call to track cost per user and identify which features are driving spend. They catch a compromised API key within minutes instead of finding out from a shocking bill.
• Multi-Provider Routing: An AI startup uses the gateway to fail over between OpenAI, Anthropic, and Google Gemini during outages, keeping their chatbot online while competitors go down.
• Prompt Iteration: A product team runs A/B tests on prompt versions against production traffic, rolling back a regression instantly through the dashboard without a code deploy.
• Agent Debugging: Engineers building a document processing agent use Sessions to trace multi-step workflows, pinpointing where the agent loops or produces hallucinations.
• Cost Optimization Through Caching: A customer support chatbot caches common questions at the gateway level, cutting API bills significantly for repetitive queries.
• Fine-Tuning Data Prep: A data team exports request logs as JSONL datasets directly from Helicone, feeding them into OpenPipe or similar tools for fine-tuning custom models.