backmesh
Backmesh is an open-source backend that protects LLM API keys and acts as an API Gatekeeper, providing JWT-based authentication, per-user rate limits, resource access controls, and instrumentation for LLM usage analytics to help apps safely call LLM APIs and reduce costs.
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Quick Overview
Best for: Security
What it does
Security software for decision-makers comparing workflow fit and alternatives.
Best fit
Security
Pricing snapshot
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backmesh
Backmesh is an open-source backend designed to keep LLM API secret keys out of client apps and prevent costly leaks. It functions as an API Gatekeeper that shields your LLM API key using what the site describes as military-grade encryption and enforces access controls so only authenticated users can call the LLM API through your application. Backmesh also instruments all LLM API calls to provide usage analytics so teams can identify patterns, reduce costs, and improve user satisfaction.
Open Source BaaS for AI apps to securely call LLM APIs without backend.
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JWT Authentication
Requests are verified with JWTs from the app's authentication provider so only your users have access to the LLM API via Backmesh.
Per-user Rate Limits
Configurable per-user rate limits to prevent abuse (site example: no more than 5 OpenAI API calls per user per hour).
API Resource Access Control
Sensitive API resources like Files and Threads are protected so only the users that create them can continue to access them.
LLM API Key Protection
Open-source backend that uses military-grade encryption to protect your LLM API key and act as an API gatekeeper.
LLM Usage Instrumentation
All LLM API calls are instrumented for analytics so you can identify usage patterns, reduce costs, and improve user satisfaction.
Open-source, battle-tested backend
The project is presented as open-source and thoroughly tested, with documentation and a trial available.
Pricing
Current pricing details are not available from the vendor source.
Use Cases
Prevent LLM API key leaks
Keep secret keys on a server-side backend rather than shipping them in client apps to avoid accidental exposure and unpredictable costs.
Throttle and prevent abuse
Apply per-user rate limits to control usage of downstream LLM APIs and prevent abusive or costly behavior.
Protect user-owned resources
Enforce resource-level access control so only creators can access sensitive items like Files and Threads.
Monitor and optimize LLM usage
Instrument LLM calls to gather analytics, identify costly patterns, and optimize for cost and user satisfaction.
Integrations
Supabase JWT Generator
Utility mentioned on the site to generate JWTs for Supabase-based authentication providers.
Firebase JWT Generator
Utility mentioned on the site to generate JWTs for Firebase-based authentication providers.
OpenAI
OpenAI is referenced in an example for rate limiting (e.g. limiting OpenAI API calls per user).
Benefits
Limitations
No verified limitations are available.
Frequently Asked Questions
No verified FAQs are available.
Getting Started
- 1 Start a trial or obtain the open-source backend from the Backmesh site.
- 2 Read the Documentation to learn integration steps and configuration.
- 3 Integrate Backmesh by verifying requests with JWTs from your app's authentication provider and configure per-user rate limits and resource access controls.
Support
documentation
Documentation is available from the site (linked as 'Documentation' and 'Docs').
community (GitHub)
Community and source code links are indicated (GitHub).
community (Discord)
Community chat is available via Discord as listed on the site.
trial / dashboard
Site provides access to a Dashboard and a Start trial flow.
API
Configurable per-user rate limits (example from site: no more than 5 OpenAI API calls per user per hour)
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