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.
An open-source dashboard that monitors your AI API usage — see costs, latency, and errors at a glance with zero-code proxy integration.
Helicone is an LLM observability platform and API gateway that provides cost analytics, request logging, caching, and rate limiting through a one-line proxy integration, with pricing starting free and scaling from $20/seat/month. It's designed for engineering teams running LLM applications in production who need cost visibility and operational controls without rewriting application code.
Helicone is built around a proxy-based architecture — you change your LLM provider's base URL to Helicone's gateway (e.g., replacing api.openai.com with oai.helicone.ai) and add a Helicone-Auth header. Every request is forwarded to the original provider, and Helicone captures full request/response metadata including token counts, latency, computed cost, and status codes. The proxy approach means there are no SDKs to install, no decorators to add, and no trace context to propagate — it works with any HTTP client library including requests, fetch, axios, or native SDKs from OpenAI, Anthropic, and others.
The platform provides a real-time analytics dashboard with cost breakdowns by model, user, custom property, and time period. Custom properties are attached via HTTP headers (Helicone-Property-*), allowing teams to segment LLM spend by feature, environment, business unit, or any arbitrary dimension. Budget alerts notify teams when spend exceeds configurable thresholds on a daily, weekly, or monthly basis, preventing cost surprises before they appear on the invoice.
At the gateway layer, Helicone provides operational controls that would otherwise require application code: request caching with configurable TTL reduces costs for repetitive queries, rate limiting prevents individual users or API keys from consuming entire provider quotas, and automatic retry logic with exponential backoff handles transient failures without retry storms. These features are enabled by adding the corresponding Helicone headers to your requests — no deployment or code changes needed beyond the headers.
For teams with strict data residency or compliance requirements, Helicone is fully open-source under the MIT license and can be self-hosted via Docker. The self-hosted deployment requires running the proxy gateway, a Supabase backend for metadata storage and authentication, ClickHouse for high-volume analytics, and optionally Redis for caching. This gives organizations full control over their data while retaining all observability features.
Helicone supports 20+ LLM providers including OpenAI, Anthropic, Azure OpenAI, Google Vertex AI, AWS Bedrock, Cohere, Mistral, Groq, Together AI, Fireworks AI, OpenRouter, and custom endpoints. OpenAI and Anthropic have dedicated proxy URLs for the simplest one-line integration, while other providers use the Helicone-Target-URL header pattern. The platform also offers an async logging mode that bypasses the proxy entirely — you send requests directly to your provider and POST the request/response pair to Helicone's logging endpoint afterward, eliminating any latency overhead for teams where every millisecond matters.
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Helicone stands out for its incredibly simple integration — a single-line proxy setup that requires no SDK or code changes. The cost tracking and rate limiting features are practical for production LLM applications. However, the feature set is narrower than LangSmith or Langfuse, lacking deep evaluation and prompt management capabilities. Best for teams wanting lightweight observability without committing to a full platform.
All LLM requests are captured by routing through Helicone's gateway with zero code changes. Supports OpenAI, Anthropic, Azure OpenAI, Google, Cohere, and Mistral. Logs include full request/response bodies, latency, token counts, and computed costs.
Use Case:
Adding complete LLM request logging to an existing production application in under 5 minutes by changing only the API base URL — no SDK installation or code modification needed
Dashboard showing real-time spend with breakdowns by model, user, custom property, and time period. Configurable budget alerts notify when spend exceeds thresholds per day, week, or month.
Use Case:
Discovering that your GPT-4 usage spiked 3x this week because a new feature accidentally calls it instead of GPT-4o-mini, before the monthly bill arrives
Identical requests return cached responses from Helicone's cache layer, controlled via cache headers with configurable TTL and bucket-based caching. Cache-hit rates are tracked in the dashboard.
Use Case:
Reducing API costs by 40% on a FAQ chatbot where many users ask similar questions that generate near-identical API calls
Attach arbitrary key-value metadata to requests via HTTP headers (Helicone-Property-*). Properties flow through to analytics for segmentation by user, feature, environment, or any custom dimension.
Use Case:
Segmenting LLM costs by product feature to determine which features are most expensive to operate and which need prompt optimization
Configurable rate limits per user or API key enforced at the gateway. Automatic retry with exponential backoff for failed requests, preventing application-level retry storms.
Use Case:
Preventing a single power user from consuming your entire OpenAI rate limit while ensuring failed requests are retried gracefully without application code changes
Track prompt variations and model experiments with statistical significance analysis, comparing latency, cost, and quality metrics across different configurations.
Use Case:
Testing whether GPT-4o-mini with a longer prompt produces comparable quality to GPT-4o with a shorter prompt at 1/10th the cost, with statistical confidence
$0/month
$20/seat/month
$200/month
Custom pricing
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Helicone has expanded session tracking and trace grouping in 2025, added experiment tracking with A/B testing for prompt variations with statistical significance analysis, broadened provider support to include AWS Bedrock, Groq, Together AI, and Fireworks AI, and introduced an AI Gateway product that unifies routing across providers with automatic fallback and key management. The platform also added prompt management with versioning and a template registry where teams can manage production prompts with full version history, an evaluation framework for systematic quality testing using LLM-as-judge scoring and custom evaluation functions, and the ability to create datasets from production logs for fine-tuning or evaluation workflows. Additional improvements include configurable alerting on cost thresholds, error rates, and latency spikes via webhooks, and deeper integrations with LLM frameworks including LangChain, LlamaIndex, CrewAI, and the Vercel AI SDK.
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