MLOps & AI Infrastructure
Outcomes
Deployment stops being an event
Versioned, tested, repeatable releases instead of a manual process only one person understands.
You find out when quality degrades
Drift detection, evaluation on live traffic, and alerting on the business metric — not just on uptime. Models fail quietly; infrastructure failures are the easy case.
Cost becomes visible and controllable
Token and compute spend attributed per feature, with routing and caching to manage it. Unmonitored AI spend grows in a way that surprises finance.
Rollback when something breaks
Version pinning and traffic splitting, so a bad release is a five-minute problem.
What we build
Deployment pipelines with CI/CD for models and prompts alike — prompts are production code and deserve the same versioning, review and rollback.
Serving infrastructure sized to real load, batch or real-time, with autoscaling and sane failure behaviour.
Monitoring and observability. Request tracing, latency, error rates, token spend, output quality sampling, drift detection on inputs and predictions.
Evaluation in production. Continuous scoring against held-out sets and live sampling, so quality is a number on a dashboard rather than an impression.
Experimentation infrastructure. A/B and shadow deployment, so changes are validated on real traffic before full rollout. Essential given how often model providers update models beneath you.
Cost management. Model routing by task, caching, batching, and spend attribution per feature and team.
Retraining pipelines with data validation, automated evaluation, and a promotion gate that a human approves.
How it works
Weeks 1–2 — Assessment. Current state of your AI systems, deployment process, monitoring gaps and cost visibility. Most organisations discover here that they cannot answer basic questions about their production models.
Weeks 2–4 — Foundation. Deployment pipeline, versioning, environments, secrets and access.
Weeks 4–6 — Observability. Tracing, metrics, quality sampling, drift detection, alerting with meaningful thresholds rather than noise.
Weeks 6–8 — Evaluation and experimentation. Continuous evaluation harness, shadow and A/B infrastructure, promotion gates.
Weeks 8–10 — Handover. Runbooks, documentation, and training your team to operate it independently.
Ongoing. Optional managed operation where you would rather not staff it internally.
Technology
Orchestration and serving: Kubernetes where the scale justifies it, managed services where it does not. vLLM and TGI for self-hosted inference; provider APIs where hosted is appropriate.
Observability: OpenTelemetry-based tracing, Prometheus and Grafana, plus LLM-specific tooling such as LangSmith or Braintrust for prompt and output tracking.
Model and prompt management: registries, version pinning, and prompt versioning under source control.
Pipelines: your existing CI/CD extended rather than replaced, plus Airflow or Dagster for scheduled retraining.
Cloud: AWS, GCP or Azure, aligned to what you already run.
Where this applies
Necessary once you have anything in production, and urgent once you have more than one thing.
Premature if you have a single prototype and no users. Ship it first; the operational layer should follow real usage rather than anticipate it.
How we scope and price
Fixed scope, quoted after an assessment. Cost is driven by how many systems are in scope, your existing infrastructure maturity, and whether self-hosted inference is involved. Where you would rather not build an internal capability, ongoing managed operation is available as a retainer.
Frequently asked questions
Overlapping but not identical. Traditional ML centres on training pipelines, feature stores and retraining. LLM systems centre on prompt versioning, evaluation, cost control and handling provider-side model updates. Most organisations now need both.
Continuous evaluation against a held-out set, sampling of live output, drift detection on inputs, and tracking the business metric the system exists to move. Absent these, you find out from a complaint.
Yes, and it is one of the most common reasons people engage. Attribution first — you cannot control spend you cannot see — then routing, caching and batching.
Behaviour can shift. Version pinning where supported, an evaluation suite that runs as a regression test, and shadow deployment before switching. Routine, provided the infrastructure exists.
Yes, as an ongoing retainer, or we build it and hand it to your team with runbooks and training. Both are common.
Usually not. Managed services handle most workloads at lower operational cost. We recommend Kubernetes when scale or existing standards justify it, not by default.
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