What Is Helicone for LLM Monitoring?
Helicone is an open-source LLM monitoring and observability platform. Learn how Helicone tracks costs, traces requests, and routes model calls.
Teams building applications on large language models quickly run into questions that are hard to answer without good tooling: how much is this costing, why is a request slow, and what exactly did the model receive and return. Helicone is an open-source platform built to answer these questions through LLM monitoring and observability. This article explains what Helicone does and how it fits into an LLM application stack.
What Helicone Is
Helicone is an open-source observability platform for applications built on large language models. Its defining characteristic is how little it asks of you to get started: in many cases you can begin monitoring requests by changing your API base URL or adding a single line of integration code, without rewriting your application logic.
Once connected, Helicone records the requests flowing between your application and language-model providers. It captures metrics automatically, giving teams insight into performance, cost, and usage patterns without extensive manual instrumentation. Because the core platform is open source, teams that prefer to self-host can run it on their own infrastructure.
Monitoring and Tracing
At its core, Helicone provides visibility into every request. You can observe and inspect traces and sessions for agents, chatbots, document-processing pipelines, and other workloads, following how a single interaction moves through multiple steps. This is particularly helpful for multi-step agentic applications where a single user action triggers a chain of model calls.
The platform tracks metrics such as cost, latency, and usage, and aggregates them into dashboards. This makes it straightforward to spot which requests are expensive, which are slow, and how usage changes over time. For teams that already use other analytics tools, Helicone supports exporting data to external dashboards.
The AI Gateway and Optimization
Beyond passive monitoring, Helicone includes a gateway layer that actively sits between your application and model providers. This gateway enables features aimed at reliability and cost control. Caching can serve repeated requests from a stored response rather than calling the model again, reducing both latency and spend. Rate limiting helps prevent runaway costs from unexpected traffic.
The gateway can also route requests across multiple providers, with load balancing and automatic failover so that an outage at one provider does not take down your application. Some prompt-management features let teams version and deploy prompts through the gateway without changing application code.
Pricing and Compliance
Helicone offers a free tier suitable for getting started and evaluating the platform, along with paid plans for teams that need higher volumes and additional features. Pricing and limits change over time, so confirm current details on the official site before committing. For organizations with compliance requirements, Helicone has pursued recognized security and privacy standards, and the self-hosting option appeals to teams that need data to stay within their own environment.
Frequently Asked Questions
Is Helicone open source?
Yes. Helicone's core platform is open source, and teams can self-host it on their own infrastructure if they prefer to keep data in-house rather than using the managed service.
How hard is Helicone to integrate?
Integration is designed to be minimal. In many setups you can start monitoring by changing your API base URL or adding a single line of code, without restructuring your application.
What does the AI Gateway add beyond monitoring?
The gateway sits between your application and model providers, enabling caching, rate limiting, load balancing across providers, and automatic failover, which together help with both cost control and reliability.