Show HN: Rate limiting, caching and request prioritization for AI apps

10 points

2 years ago

Generative AI applications pose a unique challenge in production. They are computationally intensive and orders of magnitude slower than traditional data-intensive applications. Scaling these applications is further complicated by expensive hardware requirements and GPU shortages. Consequently, developers are scrambling to implement home-grown caching and rate-limiting solutions, which are error-prone and difficult to get right.

FluxNinja Aperture delivers a production-grade experience with a purpose-built load management platform that provides rate & concurrency limiting, caching, and request prioritization for generative AI applications. Developers can wrap their workloads with Aperture SDKs and define load management policies on business attributes such as user tier, request type, priority, etc.

Features:

- Global Rate Limiting: Prevent abuse by filtering traffic based on user, service, and tier levels, among other granular options.

- Request Prioritization: Boost application performance by prioritizing critical requests while queueing less urgent ones.

- Serverless Caching: Reduce costs and alleviate system load by caching frequently requested data.

- Manage External Limits: Manage API rate limits from third parties (OpenAI, GitHub, Shopify, etc.) with client-side rate limits and prioritization.

SDKs are available in Typescript, Python, Go, etc. The solution also integrates with API gateways and service meshes with an in-cluster deployment option.

We'd love to hear your feedback!

Links:

Open-source: https://github.com/fluxninja/aperture

Use-cases:

Manage OpenAI rate limits with request prioritization: https://blog.fluxninja.com/blog/coderabbit-openai-rate-limit...

Building cost-effective generative AI applications with rate limiting and caching: https://blog.fluxninja.com/blog/coderabbit-cost-effective-ge...