Hyperion
Week 23, 2026Real Time AI Gateway for Routing, Scaling & Cost - 250× Faster Than LiteLLM
About Hyperion
Gallery
Overview
Who is it for?
Hyperion is built for AI-first startups, enterprise engineering teams, and platform developers running production LLM workloads. It’s especially valuable for teams handling sensitive data, high-volume traffic, or multi-model architectures that need fine-grained control, security, and real-time optimization across every AI request.
Problem
Production AI systems are hard to manage: latency spikes, runaway costs, inconsistent outputs, and zero visibility across providers. On top of that, teams struggle with PII exposure, lack of governance, and limited control over how requests are routed, cached, or logged. The result is fragile systems that are expensive, opaque, and risky to scale.
Solution
Hyperion is a real-time AI gateway and control plane for all your LLM traffic. It enables intelligent model routing, semantic caching, and predictive optimization to minimize latency and cost. At the same time, it provides fine-grained controls like PII redaction, request/response filtering, rate limiting, and policy enforcement, giving teams full control over how AI is used in production. With unified observability, logging, and analytics, you can monitor, debug, and optimize every call from a single layer.
What makes it unique
Hyperion combines microsecond-level latency with a deep control and governance layer—something most AI gateways lack. It goes beyond simple routing with semantic caching, predictive decisioning, and real-time cost optimization, while also offering enterprise-grade safeguards like PII redaction, granular controls, and full visibility into every request. The result is a platform that doesn’t just connect to models, it actively manages performance, cost, and risk at scale.
Use cases
- 1Chatbots
- 2SaaS
- 3Healthcare
- 4Internal Tools
Built by
Details
Pricing
Keywords
Weekly rankings
12 products
Discussion3
I really like the design and idea.
great job guys. May be you should provide a .md copy of documentation so its easier to feed into agentic coding harnesses for faster knowledge and deployment time.
Great