Helicone

Key features

• OpenAI-compatible AI gateway OpenAI-compatible AI gateway: route OpenAI, Anthropic, Google, Vertex, Groq, and more than 100 other models through one base URL with TypeScript, Python, and cURL clients.[1]

• One-line request logging One-line request logging: change the client base URL and every request appears in the dashboard within seconds, with no separate instrumentation library to adopt.[1]

• Session, user, cost, and latency observability Session, user, cost, and latency observability: follow multi-turn sessions and per-user behavior with segments, filters, and dashboards that expose spend and slow steps.[1]

• HQL request queries HQL request queries: slice request history with the Helicone query language instead of exporting logs to a separate analytics tool.[1]

• Versioned prompts and playground Versioned prompts and playground: manage prompt versions centrally and test changes in a playground linked back to the traces they produced.[1]

• Datasets and experiments Datasets and experiments: freeze evaluation sets, then run repeatable comparisons of prompts and models before rollout.[1]

• Caching, rate limits, alerts, and fallbacks Caching, rate limits, alerts, and fallbacks: cut cost and latency with cached responses, guard spend with rate limits and alerts, and stay up with automatic provider fallbacks.[1][2]

• Cloud, credits, and Enterprise deployment Cloud, credits, and Enterprise deployment: start on managed Cloud with credits billing or bring provider keys, and step up to Team compliance or Enterprise SAML SSO with on-prem deployment.[1][2][3]

Who will love it

• Teams debugging LLM apps in production: gateway-level logging with session, cost, and latency context isolates failing model and retrieval steps without retrofitting instrumentation.[1][2]

• Teams standardizing on many models: one OpenAI-compatible endpoint with credits billing and fallbacks removes per-provider keys, contracts, and switchover code.[1]

• Organizations needing a compliance path: Team-tier SOC-2 and HIPAA coverage with Enterprise SAML SSO and on-prem deployment addresses regulated procurement.[1]

Who should avoid it

• Non-technical teams wanting turnkey AI: gateway configuration, API keys, and plan setup assume engineers who own code and infrastructure.[1][2]

• Teams assuming self-hosting is fully documented: the codebase is Apache 2.0 and Enterprise lists on-prem, but a community self-host procedure was not verified in the inspected docs, so license-sensitive adopters must confirm the path first.[1][2]

• Budget-capped teams ignoring usage metering: paid tiers add usage-based charges on top of plan prices, and credits or provider spend still meters every token, so costs scale with traffic.[1]

Alternatives and compatibility