Buying LLM Gateway

Buyer's guide.

Overview

An LLM gateway is the proxy that sits between application code and one or more model providers. Its real value is operational: model routing, retries, prompt caching, cost attribution, rate limit shaping, and a single audit log. Pick on operational features, not on the model list, since you can switch providers behind any decent gateway.

• Multi-provider routing. Anthropic, OpenAI, Google, Bedrock, plus self-hosted endpoints; failover and weighted routing across them.
• Caching and cost controls. Prompt caching, response caching, per-team budgets, and cost attribution by tag.
• Observability and audit. Per-request latency, token counts, full prompt/response logging with PII controls.
• Operational fit and exit cost. SDK compatibility (drop-in OpenAI client beats bespoke SDK), self-host option, and how easily you could leave.

The approach

Trial against your real prompts and your real volume. Vendor benchmarks use synthetic traffic; your prompts have caching opportunities and provider-specific quirks the benchmark misses.

• Volume and pattern baseline. Measure current requests per second, token mix, and cache-hit potential before vendor calls.
• SDK compatibility check. A gateway that speaks the OpenAI client protocol is a one-line code change; bespoke SDKs are not.
• Cost-control test. Set a per-team budget cap and confirm the gateway enforces it before the bill arrives, not after.
• Document the choice and the exit ramp. Capture rationale and the migration plan if pricing or model availability changed.

Why this compounds

The right gateway keeps paying back: every new feature inherits routing, caching, and observability for free; engineers stop writing per-call retry loops and per-team accounting code.

• Cost discipline at scale. Caching and routing keep token spend linear when usage doubles.
• Faster experimentation. Switching models for an evaluation becomes a config change, not a code change.
• Reduced platform tax. A vendor that owns retries, caching, and audit removes three or four in-house components.
• Decision trail for the next renewal. The trial data becomes the renewal scorecard, not a cold start.