Helicone
AIOpen SourceOpen-source LLM observability platform and AI gateway for routing, monitoring, and optimizing LLM requests.
-(0 Reviews)
Free Available
Starting from Free
Premium plans available
Helicone
AIOpen Source
Open-source LLM observability platform and AI gateway for routing, monitoring, and optimizing LLM requests.
-(0 Reviews)
Free Available
Starting from Free
Premium plans available
About Helicone
Helicone is an open-source LLM observability and AI gateway platform that centralizes logging, monitoring, and management of language model requests. It provides request-level telemetry, cost and latency tracking, prompt management, and evaluation tools alongside an AI Gateway that routes, caches, rate-limits, and load-balances requests across dozens of model providers. Helicone is designed for engineers building production AI applications, offering a lightweight gateway (Rust-based), dashboards for debugging and prompt iteration, and enterprise deployment options (Docker/Helm) for scale and compliance.
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Key Features
Request Logging and Telemetry: Captures per-request inputs, outputs, metadata, and provider responses to enable debugging, auditability, and detailed traceability across LLM calls.
AI Gateway (Routing & Load Balancing): A Rust-based gateway that routes requests to 100+ supported models/providers, performs load balancing, provider fallback, and abstracts multiple model APIs behind one endpoint.
Caching and Rate Limiting: Built-in response caching and configurable rate-limiting at the gateway level to reduce costs, improve latency, and protect provider quotas.
Cost and Latency Tracking: Aggregates usage metrics, cost estimates, and latency statistics per-provider and per-endpoint to help teams monitor spending and performance.
Prompt Management & UI Iteration: UI-driven prompt experimentation and iteration tools that let teams test, refine, and compare prompts and model outputs without code changes.
Agent Tracing & Evaluations: Traces agent executions and provides evaluation tooling and dashboards for automated testing, scoring, and comparison of model behaviors and datasets.
Deployment & Enterprise Options: Support for quick local/docker deploys and production-ready Helm charts for enterprise customers, plus commercial support channels.
Request logging and full LLM request/response capture
Caching layer to reduce upstream calls and latency
Rate limiting and request routing via AI gateway/proxy
Cost and latency tracking and analytics
UI-based prompt iteration and prompt management
Agent tracing and multi-agent workflow visualization
Evaluation tooling, datasets management, and fine-tuning integration
One-line integration / header-based instrumentation and SDKs
Self-hosted deployment via Docker or Helm (production Helm chart for enterprise)
Multiple language repos and integrations (TypeScript, Rust, Go, n8n, SDK helpers)
Use Cases
Centralized Observability for LLMs: Capture and inspect every LLM request and response in production to troubleshoot hallucinations, regressions, and unexpected behaviors.
Multi-Provider Routing and Failover: Route traffic across OpenAI, Anthropic, AWS Bedrock, Google Vertex and others with load balancing and automatic fallbacks to ensure reliability.
Cost Optimization and Monitoring: Track per-request costs and latency to identify high-spend prompts or endpoints and apply caching or alternative routing to reduce expenses.
Prompt Engineering Workflow: Use the UI to iterate on prompts, compare outputs across models, and version prompt templates for faster prompt engineering cycles.
Agent and Pipeline Tracing: Monitor multi-step agent executions and workflows to visualize step-level latency, errors, and decision points for debugging and optimization.
Production Hardening: Add rate limits, caching, and provider failover at the gateway layer before exposing LLM functionality to end-users to increase reliability and reduce operational risk.
Evaluation and Benchmarking: Run evaluations against datasets and track model performance over time to validate changes and select optimal providers or models.
Centralized logging and observability for applications that call LLM providers (OpenAI, AzureOpenAI, etc.)
Add a lightweight proxy/gateway to handle caching, rate limiting, and routing between apps and LLM providers
Monitor and analyze LLM cost, latency, and usage patterns across teams and environments
Iterate on prompts through a UI and collaborate on prompt engineering and testing
Trace and debug multi-agent/chain-of-thought workflows and agent interactions
Self-hosted enterprise deployments with Kubernetes / Helm for production LLM telemetry
