Stop Burning Money on LLM Development

Save up to 70% on LLM API costs with smart caching. Track spending per feature and user. Debug production issues with full request logs. Set up in 5 minutes.

Get Started Free View Documentation

quickstart.py

# 2 lines to get started
from proxle import OpenAI

client = OpenAI(
    api_key="sk-...",
    proxy_key="pk_live_..."
)

LLM providers supported

~70%

cost reduction with caching

<10ms

proxy overhead

2 lines

to get started

LLM Development Shouldn't Be This Painful

Common frustrations, solved.

Repeat API calls waste money

Smart caching saves up to 70% during development

No idea which features cost what

Cost attribution by feature and user

Debugging LLM issues is a nightmare

Full request/response logs with search and replay

How It Works

Get started in minutes, not days.

Install the SDK

Replace your OpenAI or Anthropic import with Proxle's drop-in replacement. Two lines of code.

Requests Flow Through Our Proxy

Your API calls are routed through Proxle. We log, cache, and calculate costs — then forward to the provider.

See Everything in Your Dashboard

Costs per feature, cache hit rates, full request/response logs. All in real time.

Everything You Need for LLM Observability

Built by developers, for developers.

Smart Caching

Cache identical requests automatically. Configurable TTL, per-project settings, and cache invalidation.

Cost Tracking

Per-request cost calculation with attribution by feature and user. Know exactly where your money goes.

Request Logging

Full payload capture with search, filters, and detailed inspection. Never lose context on what happened.

Request Replay

Re-send any logged request for debugging. Compare original and replayed responses side by side.

Multi-Provider

Works with OpenAI, Anthropic, Cohere, Google Gemini, and Azure OpenAI. One dashboard for all providers.

Drop-in SDKs

Two-line integration for Python and Node.js. Replace your import, add a proxy key, and you're done.

Two Lines to Get Started

Replace your import, add a proxy key. That's it.

from proxle import OpenAI

client = OpenAI(
    api_key="sk-...",
    proxy_key="pk_live_..."
)

response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Hello!"}],
    metadata={
        "feature": "chat_assistant",
        "user_id": "user_123"
    }
)

The metadata parameter enables cost attribution by feature and user in your dashboard.

Works with all major LLM providers