Helicone — LLM Observability and Prompt Management

What it is

Helicone is an open-source LLM observability platform that sits between your application and any LLM provider. With a single line of code, you get request logging, cost tracking, caching, rate limiting, and prompt versioning. It works with OpenAI, Anthropic, Azure, and any OpenAI-compatible API.

Helicone targets AI engineers, product teams, and startups who need visibility into their LLM usage without building custom logging infrastructure. It answers questions like: how much are we spending, which prompts perform best, and where are our latency bottlenecks.

How it saves time or tokens

Helicone's caching layer stores identical request-response pairs so repeated queries hit the cache instead of the LLM. This directly reduces token consumption and API costs. The cost dashboard breaks down spending by model, user, and prompt template, letting you identify expensive queries and optimize them.

Prompt versioning tracks every change to your prompts with A/B comparison metrics. Instead of guessing which prompt version works better, you compare them side by side with real production data.

How to use

1. Sign up at Helicone or self-host via Docker. Get your Helicone API key from the dashboard.
2. Replace your LLM base URL with the Helicone proxy URL. For OpenAI: change https://api.openai.com to https://oai.helicone.ai and add your Helicone auth header.
3. All requests now flow through Helicone. View logs, costs, latency, and prompt analytics in the dashboard.

Example

import openai

client = openai.OpenAI(
    api_key='your-openai-key',
    base_url='https://oai.helicone.ai/v1',
    default_headers={
        'Helicone-Auth': 'Bearer your-helicone-key',
        'Helicone-Cache-Enabled': 'true',
    }
)

response = client.chat.completions.create(
    model='gpt-4o',
    messages=[{'role': 'user', 'content': 'Explain caching'}]
)

Two header additions turn on logging and caching. No SDK changes, no wrapper functions.

Related on TokRepo

• AI gateway providers — Compare LLM proxy and gateway solutions
• Helicone on TokRepo — Detailed Helicone integration guide

Common pitfalls

• The proxy adds a small latency overhead (typically under 50ms). For latency-critical applications, test the impact before deploying to production.
• Caching is based on exact request matching by default. Slight variations in prompt text result in cache misses. Use prompt templates to maximize cache hit rates.
• Self-hosting requires PostgreSQL and ClickHouse. The managed cloud version avoids this operational complexity.