Helicone is an AI gateway and observability platform for monitoring, routing, debugging, and analyzing LLM requests.

Who should use Helicone?

It is best for developers and AI teams that need production visibility into model usage, cost, latency, errors, and requests.

Can Helicone track AI costs?

Yes. Helicone can track usage and spending across model requests so teams can identify expensive users and workflows.

Is Helicone open source?

Yes. Helicone provides an open-source option alongside managed services and production features.

Can Helicone route models?

Yes. Its gateway can sit between applications and providers to manage model traffic and related controls.

Do I need Helicone for a prototype?

Not necessarily. It becomes more valuable when an AI application has real traffic, multiple providers, or meaningful reliability and cost requirements.

Helicone

Name: Helicone
Brand: Helicone

Open-source AI gateway and observability platform for LLM applications.

Visit Website

What is Helicone?

TL;DR

Helicone is an open-source AI gateway and observability platform for routing, monitoring, debugging, and analyzing LLM requests, usage, latency, costs, errors, and production AI behavior across providers.

AI gateway for routing and monitoring LLM requests
Tracks latency, cost, usage, and errors
Prompt debugging and production analytics

Explore with AI

About

Helicone is an open-source AI gateway and LLM observability platform for developers building AI applications. It helps teams route, monitor, debug, and analyze model requests across providers such as OpenAI, Anthropic, Azure, LiteLLM, and other AI infrastructure tools. The platform is useful for production AI products because it gives visibility into the behavior and cost of LLM calls. Developers can track latency, usage, token spend, errors, request metadata, and user-level patterns instead of treating model calls as a black box. This is especially important for SaaS founders and technical teams shipping AI features where cost and reliability can quickly become difficult to manage. Helicone can act as a gateway layer between an application and model providers. That makes it useful for logging, rate limiting, debugging prompts, comparing providers, improving reliability, and understanding which users or workflows generate the most AI spend. Teams can use it during prototyping and continue using it as an observability layer after launch. For FutureStack, Helicone is a strong fit for developers and founders building AI apps with real users. It is best positioned as a practical infrastructure tool for LLM monitoring, cost control, and debugging. It helps builders understand what is happening inside their AI systems before small issues become expensive production problems.

Use Cases

LLM Observability

Track model requests, latency, tokens, spend, errors, and metadata across production AI applications.

AI Gateway

Route requests through a gateway layer that adds logging, provider controls, and operational visibility.

Cost Monitoring

Understand which users, workflows, models, and applications generate the most AI spend.

Prompt Debugging

Inspect requests and responses to diagnose unexpected outputs and improve AI application behavior.

Key Features

AI gateway for routing and monitoring LLM requests
Tracks latency, cost, usage, and errors
Prompt debugging and production analytics
Works with OpenAI, Anthropic, Azure, LiteLLM, and more

Featured Tools

Wispr Flow

Voice-First Dictation

We may earn commissions from links to support our work. Learn more.