🔒 Enterprise-grade API gateway that helps you monitor and impose cost or rate limits per API key. Get fine-grained access control and monitoring per user, application, or environment. Supports OpenAI, Azure OpenAI, Anthropic, vLLM, and open-source LLMs.
Langtrace 🔍 is an open-source, Open Telemetry based end-to-end observability tool for LLM applications, providing real-time tracing, evaluations and metrics for popular LLMs, LLM frameworks, vectorDBs and more.. Integrate using Typescript, Python.
ezkl is an engine for doing inference for deep learning models and other computational graphs in a zk-snark (ZKML). Use it from Python, Javascript, or the command line.
[NeurIPS'24 Spotlight, ICLR'25, ICML'25] To speed up Long-context LLMs' inference, approximate and dynamic sparse calculate the attention, which reduces inference latency by up to 10x for pre-filling on an A100 while maintaining accuracy.
The simplest way to serve AI/ML models in production.
Key parameters and specifications
Key parameters and specifications
Parameter
Value
Language
Go
Category
Infrastructure
Subcategory
Gateway
Stars
1,230
Forks
97
Contributors
13
Created date
2023-07-18
Updated date
2026-10-13
Stars in 1 day
0
Stars in 7 days
0
Rank
11,049
Previous rank
11,002
Rank change
-47
7-day stars rank
12,865
Previous 7-day stars rank
12,883
7-day stars rank change
18
Total stars rank
9,500
Previous total stars rank
9,492
Total stars rank change
-8
README summary
**BricksLLM: AI Gateway For Putting LLMs In Production** [! TIP] A managed version of BricksLLM is also available! It is production ready, and comes with a dashboard to make interacting with BricksLLM easier. **BricksLLM** is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: Set LLM usage limits
for users on different pricing tiers Track LLM usage on a per user and per organization basis Block or redact requests containing PIIs Improve LLM reliability with failovers, retries and caching Distribute API keys with rate limits and cost limits for internal development/production use cases Distribute API keys with rate limits and cost limits for students Features [x] PII detection and masking [x] Rate limit [x] Cost control [x] Cost analytics [x] Request analytics [x] Caching [x] Request Retries
[x] Failover [x] Model access control [x] Endpoint access control [x] Native support for all OpenAI endpoints [x] Native support for Anthropic [x] Native support for Azure OpenAI [x] Native support for vLLM [x] Native support for Deepinfra [x] Support for custom deployments [x] Integration with custom models [x] Datadog integration [x] Logging with privacy control Getting Started The easiest way to get started with BricksLLM is through BricksLLM-Docker. Step 1 - Clone BricksLLM-Docker repository Step 2 - Change to
BricksLLM-Docker directory Step 3 - Deploy BricksLLM locally with Postgresql and Redis You can run this in detach mode use the -d flag: Step 4 - Create a provider setting Copy the from the response. Step 5 - Create a Bricks API key Use from the previous step as to create a key with a rate limit of 2 req/min and a spend limit of 25 cents. Then, just redirect your requests to us and use OpenAI as you would normally
. For example: Or if you're using an SDK, you could change its to point to us. Separated by , | | | | optional | Name for Postgresql DB. | | | required | Postgresql DB username | | | required | Postgresql DB password | | | optional | Postgresql SSL mode| | | | optional | The port that Postgresql DB runs on| | | | optional | Timeout for Postgresql read operations | | | | optional | Timeout for Postgresql write operations | | | | required | Host for Redis. Separated by , | | | | optional | Redis Password | | | optional | The port that Redis DB runs on | | | | optional | Timeout for Redis read operations | | | | optional | Timeout
for Redis write operations | | | | optional | The interval BricksLLM API gateway polls Postgresql DB for latest key configurations | | | | optional | "datadog" or Host:Port(127. | | | optional | Timeout for proxy HTTP requests. | | | | optional | Number of event message consumers that help handle counting tokens and inserting event into db. | | | | optional | It is for PII detection feature. | | | | optional | It is for using PII detection feature. | | | | optional | Timeout for amazon requests. | | | | optional | Timeout for amazon connection. | | | | optional | Simple password for the admin server. | Admin Server Swagger
Doc Proxy Server Swagger Doc
Frequently asked questions
How many stars and forks are recorded?
bricks-cloud/BricksLLM has 1,230 stars and 97 forks.
What is the repository rank?
bricks-cloud/BricksLLM is ranked 11,049.
What language and category are recorded?
bricks-cloud/BricksLLM has 1,230 stars, uses Go and belongs to Infrastructure.
Which repository is closest in rank?
bricks-cloud/BricksLLM is compared with NVIDIA/kvpress, ranked 11,046 with 1,230 stars.
README
BricksLLM: AI Gateway For Putting LLMs In Production
[!TIP]
A managed version of BricksLLM is also available! It is production ready, and comes with a dashboard to make interacting with BricksLLM easier. Try us out for free today!
BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM:
Set LLM usage limits for users on different pricing tiers
Track LLM usage on a per user and per organization basis
Block or redact requests containing PIIs
Improve LLM reliability with failovers, retries and caching
Distribute API keys with rate limits and cost limits for internal development/production use cases
Distribute API keys with rate limits and cost limits for students