Helicone provides an AI gateway that sits between applications and LLM providers, delivering real‑time observability, routing, and control over model calls. It logs each request with latency, token usage, and response data, offering searchable dashboards, caching, rate limiting, and automatic fallbacks to improve reliability and reduce costs for production AI applications.
Funding
$1.5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
1OFounders
Product
Problem
Developers of production AI applications often struggle with unreliable LLM integrations, lacking visibility into request performance, cost, and errors, which leads to downtime, debugging overhead, and uncontrolled spending.
Solution
Helicone offers an AI gateway that sits between applications and LLM providers, providing end‑to‑end observability, routing, and control over model calls. The platform logs every request, captures latency, token usage, and response data, and makes this information searchable via a query language and dashboards. Built‑in features such as caching, rate limiting, automatic fallbacks, and configurable retention reduce latency, lower costs, and improve reliability. Developers can test prompts, monitor usage, set alerts, and export data for deeper analysis, all without changing existing code. The service scales from individual hobbyists to enterprise teams, with usage‑based pricing that aligns cost to actual request volume.
Target Audience
Primary users are developers and engineering teams building production‑grade AI applications, including startups, SaaS providers, and large enterprises that require reliable LLM integration and observability.
Features
- API gateway that proxies calls to any LLM provider with passthrough billing
- Real‑time request logging, latency tracking, token usage, and response storage
- Query language (HQL) and searchable dashboards for debugging and performance analysis
- Built‑in caching of LLM responses to reduce token consumption and latency
- Configurable rate limits and automatic fallback routing to alternative models
- Alerting and reporting on error rates, cost spikes, and SLA breaches
- Data retention controls (7 days to forever) and export capabilities
- Self‑hosting option via Docker for on‑premise deployments