One governed LLM layer for the enterprise
LLM integration services — LLM API integration into your applications, data and workflows through a provider-agnostic gateway with routing, fallback, retrieval, guardrails and observability.
Quick answerLLM integration services connect large language models into your applications, data and workflows through a governed, provider-agnostic layer — an LLM gateway with routing and fallback, retrieval to your systems of record, prompt and policy management, guardrails, and observability for quality, latency and cost. It keeps ‘integration’ intent in one place, alongside our OpenAI and Claude services.
What a single LLM layer gives you
Routing & fallback
Each request goes to the cheapest capable model, with automatic fallback if a provider degrades.
Central prompt & policy
One place to manage system prompts, safety policy and structured-output schemas.
Cost control
Per-team quotas and budgets, caching, and cost-per-request and cost-per-user reporting.
Security & keys
Secrets held centrally, per-app authorisation, and one place to enforce logging and redaction.
Shared retrieval
A retrieval service any application can call to ground answers in enterprise data.
Observability
Latency, quality and error rates per model and per app, with alerting wired to ownership.
Direct provider calls compared with a gateway
| Concern | Direct calls | LLM gateway |
|---|---|---|
| Switch or A/B a model | Code change per app | Config change, no app change |
| Cost visibility | Scattered across accounts | Per-team, in one place |
| Provider outage | App breaks | Automatic fallback |
| Prompt / policy updates | Per app | Central |
| Security & logging | Duplicated, inconsistent | Enforced once |
A path to a governed LLM layer
Assess
Current LLM usage, providers, cost and pain points.
Stand up
The gateway with routing, caching, keys and logging.
Migrate
Applications onto the gateway, one at a time, no big bang.
Ground
Add the shared retrieval service for RAG use cases.
Optimise
Routing and caching tuned against real cost and quality data.
An LLM layer your platform team can own
Provider-agnostic
OpenAI, Claude, Gemini and open models behind one endpoint.
FinOps for AI
Quotas, budgets and cost-per-use reporting so spend is owned.
Security-first
Central secrets, per-app authorisation, redaction and audit logging.
Platforms behind the team
RLCatalyst and Spectra accelerate build and run.
Retrieval built in
A shared grounding service, not a per-app rebuild.
Maintainable
A clean layer your team can extend and operate.
- AWS
- DataStax
- Salesforce
- Snowflake
Provider-specific integration
For OpenAI and Claude specifically, see OpenAI & ChatGPT Integration and Claude & Anthropic Integration — both plug into the same gateway.
LLM platform insights
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Read MoreLLM Integration Services: frequently asked questions
Content last reviewed: September 2026
Scaling this across the enterprise?
Our Enterprise AI Services practice brings generative AI, agents and LLMs to production at scale — with the governance, platform and operating model to keep them reliable, compliant and cost-controlled.
LLM calls scattered across your apps?
Tell us your providers and pain points and we will design a governed gateway and a migration path.
Talk to an AI specialist
Tell us where you are on your AI journey — a use case, a proof of concept, or a platform decision — and an AI consultant will come back with next steps and a rough shape for the engagement.