An LLM gateway is a specialized proxy that understands generative-AI semantics: token costs, streaming, embeddings, prompt injection, and model quality differences. It’s built specifically for LLM workloads, unlike generic REST API gateways.
When to use an LLM gateway vs direct provider calls
Prototype Stage
Direct SDK calls are okay for early development and validating core ideas.
Production with SLAs
You must use a gateway for failovers, caching, and rate limiting.
Cost-Sensitive / Multi-Provider
A gateway is essential for budget cutoffs and dynamic smart model routing.
Compliance / On-Prem
A self-hosted gateway is highly recommended for PII redaction and audit logs.
Common Capabilities
- OpenAI-compatible API surface: Instantly works with existing LangChain/LlamaIndex code.
- Provider abstraction: Support for OpenAI, Anthropic, Google, and local open-source models natively.
- Semantic Cache + TTL tiers: Layered caching to eliminate redundant token generation.
- Model Routing: Direct traffic based on complex cost, latency, or quality policies.
- Streaming & Partial Results: Flawless handling of Server-Sent Events (SSE).
- Audit Logs, RBAC, SSO: Enterprise security wrappers around public AIs.
Migration Checklist
Migrating from direct calls to Hyperion takes minutes, but verifying production stability takes a few days:
Replace hardcoded provider SDK endpoints with the Hyperion URL and Virtual Key.
Enable Semantic Caching for your read-like or repeated queries.
Configure team budgets, anomaly alerts, and per-key spend limits.
Run traffic in A/B/Shadow mode (Hyperion vs direct) for 2–7 days to observe latency baselines.
Flip the final switch and enable Active-Passive auto-failover to alternative providers.
LLM Gateway FAQs
No: an LLM gateway handles token economics, streaming and prompt risks in addition to routing.
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Hyperion provides instant, out-of-the-box active-passive failover and circuit breaking for all major model providers without changing your application code.