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Home/Categories/LLMOps & AI Tooling/Helicone
icon of Helicone

Helicone

Open source alternative to Arize AI, LangSmith and Confident AI

  • 6.2K stars
  • Apache-2.0
  • Active this week
  • Last commit 2 days ago

Last verified Oct 21, 2026

Helicone is an open source LLM observability platform that proxies your OpenAI and Anthropic calls to log requests, track costs, detect anomalies, and run prompt experiments in production. Apache 2.0.

Visit websiteGitHub6.2K starsDeploy on Hostinger
image of Helicone
TL;DR

Helicone is an LLM observability platform for tracing requests, costs, latency, prompts, and user-level model behavior. It replaces proprietary AI observability tools for teams that want insight into OpenAI, Anthropic, and other model traffic with a self-hostable path. Apache-2.0 licensed.

Read the full breakdownHide the full breakdownWho it is for, how it works, trade-offs and the FAQ
who it's for

Who Helicone is for#

AI product teams watching model spend

Use Helicone when product and engineering need to connect LLM cost, latency, and quality to users, features, and prompts.

Skip if:

Your app only makes occasional internal model calls and raw logs already answer your debugging questions.

Developers debugging prompt regressions

Use Helicone when prompt changes, model switches, or user behavior create production issues that need request-level traces.

Skip if:

You need full-stack APM first and LLM tracing is only a minor part of your observability needs.

the problem

The problem it solves#

LLM apps fail in ways normal web analytics do not explain. Teams need to know which prompts cost the most, which users trigger slow requests, where outputs degrade, and how model changes affect production behavior.

Without request-level observability, debugging becomes guesswork. Engineers inspect logs manually, product teams lack usage visibility, and finance teams cannot connect model spend to features or customers.

how Helicone solves it

How it solves it#

LLM request tracing

Helicone captures model requests and responses so teams can inspect prompts, latency, status, usage, and production behavior from one observability surface.

Cost and usage analytics

Track token usage and spend across models, users, and features, giving teams a clearer view of where AI costs come from.

Prompt and user debugging

Request metadata helps developers connect model behavior to application users, prompts, and workflows instead of searching raw logs.

strengths · trade-offs

Strengths and trade-offs#

Strengths

  • Purpose-built for LLM appsHelicone focuses on model traffic rather than generic logs, making it easier to answer AI-specific questions about prompts, tokens, latency, and outputs.
  • Self-hostable observability optionTeams with privacy or compliance constraints can evaluate a self-hosted path instead of sending all model telemetry to a closed observability vendor.

Trade-offs

  • -Adds an observability integration layerHelicone still needs to sit in the model request path or application instrumentation. Teams should validate latency, privacy, and retention requirements before routing production traffic.
tech stack · detected from GitHub

What it's built on#

Languages
PythonTypeScript
Frameworks
LangChainNext.jsReact
frequently asked

FAQ#

What does Helicone monitor?

Helicone monitors LLM requests, prompts, responses, latency, status, token usage, and cost so teams can debug AI application behavior.

Is Helicone a LangSmith alternative?

Helicone can replace LangSmith for LLM request observability and usage analytics. LangSmith remains stronger for teams already committed to the LangChain ecosystem.

Can Helicone be self-hosted?

Helicone documents self-hosting options, which helps teams keep model telemetry under their own infrastructure policies.

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Hostinger

Run Helicone on your own server

Self-host Helicone on a Hostinger VPS.

Deploy on Hostinger

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