Foundation
LLM gateway implementation: one controlled route to every model
The single OpenAI-compatible endpoint every AI call in your estate goes through. It routes, retries, caches, enforces budgets and records what everything cost — deployed in your own cloud, not ours.
Outcome: Every model call in the business becomes measurable, capped and re-routable without touching application code again.
Talk about llm gateway implementationThe problem is not the model. It is that nothing sits in front of it.
When every service holds its own provider key and calls the API directly, four things become impossible at once. You cannot change model without a deploy. You cannot cap a runaway feature without revoking a key other things depend on. You cannot fail over when a provider degrades. And you cannot attribute a single pound of the bill to a single team.
A gateway is a small piece of infrastructure that makes all four routine. One endpoint, OpenAI-compatible, so most applications integrate by changing a base URL and nothing else.
What the gateway gives you
Routing by policy, not by deploy
Which model serves which class of request becomes a versioned configuration file. Moving your summarisation traffic to a cheaper model is a policy change with an audit trail and an instant rollback, not a pull request in six repositories.
Budgets that actually stop spending
Per-project and per-key limits with alerts well before the ceiling. A misconfigured loop in a staging environment stops costing money at a number you chose, rather than at the end of the month when the invoice lands.
Caching where it is free
Exact-match and prompt-prefix caching handled centrally, so every team benefits from it without every team implementing it. Cache hit rate becomes a metric you can watch and improve.
Failover that degrades gracefully
When a provider slows or errors, traffic moves to a configured alternative under rules you set, and the retry accounting stays honest — a retried call is recorded as one task and two calls, which is the only way a cost-per-success number stays truthful.
Telemetry as a by-product
Because every call goes through one place, cost attribution stops being a project and becomes a property of the system. That is what makes the AI FinOps retainer cheap to run afterwards.
In your cloud, on open foundations
We build on LiteLLM, deployed into your own subscription — typically Azure Container Apps, though the pattern is portable. Your keys stay in your key vault. Your prompts and completions stay inside your network boundary. There is no Rise10x-hosted middleman holding your traffic, which is the answer most security reviews are looking for and the reason we chose the open component over a hosted proxy.
We are candid about the trade-off, because it is the whole bet: a gateway is one more hop in the request path and one more thing that can fail. It buys routing, budgets, caching and measurement. If your estate is a single application making a single kind of call, you may not need one yet, and we will tell you that rather than sell you one.
Deliverables
What you have at the end
- An OpenAI-compatible gateway deployed in your cloud account and network
- Per-project keys, budgets and hard spend caps with alerting before the cap
- Provider failover and retry policy that does not silently double-bill
- Response and prompt caching, with cache-hit reporting
- A versioned routing policy — changed by config, not by a deploy
- Cost, token and latency telemetry per call, per project, per customer
- Runbook, dashboards and a rollback path your on-call team can follow at 3am
Everything on that list lives in your repositories and your cloud account. Ending an engagement does not take the capability with it.
FAQ
LLM Gateway Implementation: common questions
Does an LLM gateway add latency?
A well-placed gateway adds single-digit milliseconds when it is deployed in the same region as both your application and the model endpoint. That is normally invisible next to the hundreds of milliseconds of model inference. It can also remove latency, because a cache hit never reaches the provider at all.
Is this a proxy you host and we pay per call for?
No. It runs in your cloud account under your control. We build, hand over and document it. If you later want us to keep operating it, that is the AI FinOps retainer, but the infrastructure remains yours either way.
We use Azure OpenAI, OpenAI and Anthropic. Can one gateway front all of them?
Yes — that is precisely the case it is built for. One OpenAI-compatible surface, many providers behind it, and the ability to move traffic between them by configuration when pricing, capacity or quality changes.
What happens if the gateway goes down?
It is deployed with the same availability posture as the rest of your production estate, and the runbook includes a documented direct-to-provider bypass. We test that bypass with your team before handover rather than discovering it during an incident.
Related
Where this leads next
Model Routing & Evaluation
The largest single lever in most AI bills is that one model serves every request. We segment your traffic by task, find the cheapest model that still passes, and prove it on your own traffic before it reaches a customer.
AI FinOps Retainer
Cost reductions decay. Prompts grow, traffic shifts, models get deprecated and new features ship without a budget. The retainer keeps the measurement running and the backlog of savings moving.
Azure AI Cost Optimisation
Azure-specific AI cost work: choosing between provisioned throughput and pay-as-you-go, getting deployment types and regions right, and wiring model spend into the Azure cost tooling your finance team already uses.
Find out what your AI actually costs per completed task.
Two weeks, a fixed fee, and a ranked savings plan with the quality risk of every move stated up front. If the numbers say an audit is not worth it for you, we will say so on the first call.