AI Cost Gate is a self-hosted, metadata-only LLM cost gateway that attributes every request by project, agent, and model, with budgets that auto-stop runaway spend.
Why it matters
API Available
overview
AI Cost Gate is a self-hosted AI gateway tool developed by CostBrake that enables AI Developers, MLOps Engineers, Engineering Managers, and Finance/Cost Control Teams to track, attribute, and manage LLM costs. It acts as a middleware, logging detailed request metadata and costs into a local SQLite or Postgres database, ensuring data privacy by not transmitting prompts or completions.
features
AI Cost Gate provides a suite of features designed for comprehensive AI cost management and visibility, focusing on privacy and granular attribution.
use cases
AI Cost Gate is designed for organizations and individuals requiring stringent control and visibility over their AI expenditures, particularly those with multi-agent, multi-provider LLM workloads and strict data privacy requirements.
how to use
AI Cost Gate operates as a self-hosted middleware, intercepting LLM API calls to log metadata and enforce policies. Users deploy the gateway within their own infrastructure and configure their AI agents to route requests through it.
pricing
AI Cost Gate offers a paid 'Pro access' model, licensed via a private repository. Specific pricing figures for the 'Pro access' are not publicly disclosed, requiring direct contact for details. The usage pricing for specific models is provided as examples.
Pricing Page
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AI Cost Gate differentiates itself in the AI cost management landscape primarily through its self-hosted, local-first, and metadata-only approach, prioritizing data privacy and direct control over infrastructure.
An open-source SDK for LLM observability, providing tools to instrument LLM applications for tracing, monitoring, and cost analysis, with options for self-hosting the data backend.
OpenLLMetry is an SDK for instrumenting your application directly, rather than a gateway that sits in front of your LLM calls. It focuses on observability and cost tracking through telemetry, which means you'd integrate it into your code, and it would send data (potentially including parts of prompts for detailed traces) to your self-hosted backend, differing from AI Cost Gate's gateway approach.
Involves writing custom code to intercept LLM API calls, extract only necessary metadata (project, agent, model, token counts), log it to a local database (SQLite/Postgres), and implement custom budgeting logic.
This option perfectly matches AI Cost Gate's 'metadata-only' and 'prompts never leave your machine' promise, as you control exactly what data is logged. The trade-off is the significant development effort required to build and maintain it, lacking the out-of-the-box features and user interface of a dedicated tool.
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