One API for OpenAI, Anthropic, Google, and compatible models you route to.
BerryRouter gives teams a self-hosted LLM gateway with provider routing, BYOK, budgets, rate limits, prompt retention, ClickHouse analytics, and request logs built for debugging real production traffic.
Last 24 hours
Usage and request logs
Everything you need to ship with confidence
Track every request, route across providers, enforce budgets, and inspect the exact data flowing through the gateway.
Route by health, price, policy, or explicit provider.
The gateway evaluates API keys, budgets, rate limits, guardrails, model capabilities, provider health, and fallback policy before it calls upstream.
Keep your OpenAI-style client. Change the base URL.
Point supported chat, response, image, audio, moderation, and video calls at BerryRouter, then let the gateway handle provider selection, telemetry, logging, and controls.
from openai import OpenAI
client = OpenAI(
base_url="https://gateway.yourcompany.com/v1",
api_key="br_live_..."
)
response = client.chat.completions.create(
model="openai/gpt-4.1-mini",
messages=[
{"role": "system", "content": "Use concise answers."},
{"role": "user", "content": "Summarize this trace."}
],
stream=True
)
for chunk in response:
print(chunk.choices[0].delta.content or "", end="")Common questions
The gateway is backend-first, but the dashboard is where usage, logs, and control-plane operations become visible.
Built for teams that need control and auditability.
Run locally with Docker, deploy with Postgres, Redis, ClickHouse, and S3-compatible storage, then manage the gateway through the control plane.
Start routing requests through your own gateway.
Create an internal API key, configure provider keys, then watch usage and logs light up request by request.