BricksLLM

It sits between applications and LLM providers (OpenAI, Anthropic, Azure OpenAI, and others), enforcing cost limits, rate limits, and usage quotas per API key or userpreventing runaway spend from unexpected usage spikes or compromised credentials.

Teams use it to implement the access control and observability layer that the LLM providers themselves don't offer at the granularity enterprise deployments require.

The gateway handles key management with fine-grained policy configuration: different keys can be granted different model access, token budgets, and rate limits, with automatic key rotation and expiry.

All requests and responses are logged with token usage, latency, model, and key metadata, providing the audit trail needed for cost attribution across departments and the anomaly detection needed to catch abuse.

BricksLLM also supports prompt caching and semantic caching to reduce redundant API calls and their associated costs.

Platform teams managing LLM API access across multiple internal teams, companies deploying AI features to end users where per-user cost control is essential, and organizations with compliance requirements around AI usage auditing use BricksLLM to add the governance layer that direct LLM API access lacks.

The self-hosted deployment model keeps all prompt and response data on the organization's own infrastructurea significant consideration for enterprises whose data privacy policies prohibit routing sensitive query content through third-party logging services.