Calling a language model API is a few lines of code. Running one in production is not. The gap is filled with rate limits, timeouts, partial failures, token accounting, prompt versioning, caching and the day your provider has an outage during business hours. We build that layer so an LLM behaves like any other dependency in your system: monitored, bounded and replaceable.

Who this is for

Teams with a working prototype that now needs to handle real traffic. Companies already paying for an LLM feature and unable to explain the invoice. Anyone who has been locked to one provider by hard-coded SDK calls scattered through a codebase.

Problems we solve

Provider lock-in. Vendor SDK calls spread across the application, so switching means a refactor.
No cost visibility. A single monthly figure with no attribution to feature, customer or endpoint.
Rate limits in production. Traffic spikes into a 429 with no queue and no backoff.
Blocking calls. Long generations tying up web workers instead of running on a queue.
Untracked prompts. Prompts edited in place, so nobody can tell which version produced last week's bad output.

What we build

A provider abstraction so OpenAI, Claude, Gemini and Ollama are interchangeable by configuration
Streaming responses over server-sent events, with cancellation when a user navigates away
Retry with exponential backoff, request queueing and graceful fallback to a secondary provider
Response caching on repeated inputs — usually the largest single cost saving available
Per-feature, per-tenant token accounting and hard spend ceilings
Versioned prompts kept in source control and traceable from any output
Secure key handling, request logging and PII redaction before anything leaves your network

How we work

We put every model call behind one internal interface, so the rest of the application never imports a vendor SDK. Long-running generation moves onto a queue rather than a web request. Cost is instrumented from day one, because a feature you cannot measure is a feature you cannot optimise. Where local inference is viable we benchmark it honestly against hosted models rather than assuming either is better.

Technologies we use

OpenAI, Anthropic Claude, Google Gemini, and Ollama for self-hosted inference. Laravel queues and Node.js workers for async execution, Redis for caching and rate limiting, and structured logging with request tracing across the whole call path.

Business benefits

Model choice becomes a business decision rather than an engineering project
Caching and right-sized models typically remove a large share of running cost
A provider outage degrades the feature instead of taking the product down
Spend is attributable, so unit economics per customer are actually knowable

Where it pays off

Taking a proven AI prototype to production traffic
Cutting the cost of an LLM feature that already works
Adding a second provider for redundancy
Moving sensitive workloads onto self-hosted models
Adding streaming to a feature that currently makes users wait

Common questions

Which provider is best?

They trade places regularly, which is the argument for the abstraction. We benchmark candidates on your actual task and evaluation set rather than on public leaderboards, and build so the answer can change.

Can we run models on our own servers?

Yes, with Ollama and comparable runtimes. It removes per-token cost and keeps data internal, at the price of hardware and a lower quality ceiling. We will benchmark both against your task before you commit.

How much can caching save?

It depends entirely on input repetition. Support and documentation workloads repeat heavily and cache extremely well; genuinely unique inputs do not. We measure your traffic before promising a number.

Running an LLM feature you cannot explain the cost of? That is usually the first thing worth fixing.

LLM Integration