--- title: LLMOps & AI Platform Engineering source: https://rubradigital.com/services/llmops-platform site: Rubra Digital description: The infrastructure under your AI features: gateways, prompt versioning, tracing, cost controls, caching and pipelines that let several teams ship safely. updated: 2026-06-10 duration: 6–12 weeks starting_price: from €55,000 team: One platform engineer plus a fractional architect --- # LLMOps & AI Platform Engineering **Summary:** Rubra builds the platform layer beneath AI features: a model gateway with failover, prompt and config versioning, distributed tracing, per-team cost attribution and budgets, semantic caching, and deployment pipelines with evaluation gates. It is what turns three separate AI prototypes into a capability the whole organisation can build on. The first AI feature ships from a notebook. The third one reveals that three teams have three sets of provider credentials, nobody can explain last month's invoice, and the prompt that runs in production exists only in a deployed container image. The platform layer is what stops that. ## What I build ### A gateway in front of every model call One path to every provider, with routing, failover, rate limiting and credential management in one place. It gives you a point of control for redaction and audit logging, and it makes switching providers a configuration change rather than a migration. ### Prompts as versioned artefacts Prompts, model selection, temperature and retrieval parameters belong in version control with review and staged rollout. Not hard-coded, and not in a database row someone can edit at 5pm on a Friday. Every production response should be traceable to the exact configuration that produced it. ### Tracing that spans the whole request A single user request may touch retrieval, reranking, two model calls and three tools. When it goes wrong, you need the whole path in one view: inputs, outputs, latency and cost at each hop. I build this on OpenTelemetry so it lands in the observability stack you already run. ### Cost as a first-class metric Per-request cost attributed to team, feature and customer; budgets with alerting before the invoice arrives; and the data to answer whether a feature is worth what it costs. Most organisations discover at this point that a small number of requests account for most of the spend, and that a large share of it is avoidable. ### Deployment with evaluation gates Changes flow through CI, run against the evaluation set, and are blocked on regression. Canary rollout for anything touching a live surface. This is the piece that lets you move quickly without the quality of the system depending on who happened to review the pull request. ## When to build it Not for your first AI feature, which would be premature. The right moment is when a second or third team starts building, when spend crosses roughly €3,000 a month, or when your first provider incident makes it clear that a hard-coded endpoint is a single point of failure. In practice most organisations reach that point about six months after their first successful pilot. ## What you get - Model gateway with routing, failover and rate limiting - Prompt and configuration versioning with staged rollout - OpenTelemetry-based tracing across the full request path - Cost attribution, budgets and alerting per team and feature - Semantic and exact-match caching layers - CI/CD with evaluation gates and canary deployment ## Outcomes - One governed path to production for every team building with models - Cost visible and attributable per team, feature and customer - Model changes and provider outages absorbed without incidents ## Frequently asked questions ### What is LLMOps and how does it differ from MLOps? LLMOps is the operational layer for systems built on foundation models you did not train: gateways, prompt versioning, evaluation gates, tracing, caching and cost control. Classical MLOps centres on training pipelines, feature stores and model registries, which mostly do not apply when the model is an API call. The shared ground is deployment discipline and monitoring; the differences are that your prompts are now the artefact under version control, your costs are variable per request, and your dependency is a third party who can change behaviour without warning. ### Do we need a model gateway? Once more than one team calls a model, yes. Without a gateway you get provider credentials scattered across services, no way to attribute spend, no central place to enforce rate limits or PII redaction, and a painful migration every time you want to change providers. The gateway is usually two weeks of work that saves months later. ### How much can caching reduce LLM costs? For assistant-style workloads with repetitive queries, exact-match plus semantic caching commonly removes 25 to 45 percent of calls. Combined with prompt-prefix caching where the provider supports it and routing simple requests to smaller models, I have taken client inference bills down by 60 to 80 percent without a measurable quality change. The caveat is that this must be verified with evaluation, because routing to a cheaper model quietly degrades some task types. ### Can this run entirely within our own cloud? Yes. The platform layer is standard infrastructure. It deploys into your AWS, Azure, GCP or Cloudflare account, and works equally well pointed at hosted model APIs, at models in your own VPC, or at both with routing between them. For clients with data residency constraints this is usually the whole point. --- Source: https://rubradigital.com/services/llmops-platform Rubra Digital. Independent LLM and RAG consulting for regulated and document-heavy organisations in Europe and North America. Contact: hello@rubradigital.com