Gateway & provider routing
Configure supported provider interfaces, authentication, request limits, and routing policies. Test fallbacks for feature compatibility and regional constraints.
08 / Infrastructure
LLM Gateways, Caching & Token Management
Bring model routing, cache strategy, usage budgets, and cost visibility into one operational layer.
The opportunity
Put performance and spend under control.
As AI usage grows, scattered provider calls become difficult to manage. We build an LLM gateway or proxy layer for routing, authentication, quotas, and visibility, and select caching strategies that fit your workloads and data boundaries.
From scope to system
What we build with you.
A focused implementation, shaped around your systems, data, and operating requirements.
Prompt & inference-cache optimisation
Structure reusable prompt prefixes for provider caching. For self-hosted inference, evaluate engine-level KV and prefix caching against memory and throughput constraints.
Semantic response caching
Reuse sufficiently similar responses only where appropriate, with tenant and permission boundaries, expiry, invalidation, and quality checks.
Usage governance & observability
Track tokens and cost by application or team, set budgets and quotas, and monitor latency, errors, cache hits, and effective savings.
An example in practice
Shared model access with a clear cost owner.
An illustrative gateway routes requests from several internal assistants, applies team-level budgets, and reuses eligible prompt prefixes. Dynamic or sensitive responses bypass semantic caching unless explicitly allowed.
ILLUSTRATIVE USE CASE
01Application request
02Policy & cache check
03Route to model
04Meter & observe
Define success early
Measure the work.
Improve the outcome.
We agree on relevant baselines and acceptance criteria before implementation. Depending on your scope, these may include:
- End-to-end latency
- Effective cache savings
- Cost per completed task
- Budget, quota, and error rates
Are KV caching, prompt caching, and semantic caching the same?
No. KV caching reuses attention computation inside an inference engine. Provider prompt caching can reuse eligible shared prefixes under provider rules. Semantic caching reuses responses for similar requests and needs separate relevance, permission, and freshness controls.
Can a proxy enable KV caching for every model API?
No. A proxy cannot control a provider’s internal KV cache. It can route requests and help use supported prompt-cache features. Engine-level cache settings are available only when the serving engine exposes them, such as in suitable self-hosted deployments.
Will caching always lower our costs?
No. The outcome depends on repetition, cache write and read pricing, expiry, storage, and invalidation overhead. We measure the effective cost per task and answer quality against an uncached baseline.
From ambition to action
Your next advantage
starts here.
Bring us your requirements. We’ll help turn them into a clear scope and a practical path forward.