Production AI Platform Engineering
Production-ready Kubernetes foundations for AI inference workloads - deployment patterns, GPU governance, reliability controls, security boundaries, and cost-aware scaling. We don't build models. We make them run properly in production.
The problem we solve
Your platform team is being asked to support AI workloads. The clusters exist. The pressure from leadership exists. What doesn't exist is a safe, repeatable, production-grade path that doesn't compromise everything you've already built.
Most organisations discover this gap when the first inference workload hits production and immediately creates problems the platform wasn't designed to handle - GPU contention, unpredictable latency, cost blowouts, and no clear operating model for who owns what.
Typical workstreams
- Platform readiness assessment for AI inference workloads
- Inference runtime architecture and deployment patterns
- GPU and accelerator scheduling, quota, and tenancy policy
- Reliability engineering for model-serving services
- Observability and cost attribution for inference endpoints
- Security, compliance, and policy controls for model serving
- Self-service interface and golden paths for inference
What you get
- Production-ready Kubernetes foundation for inference workloads
- Standardised deployment patterns for online inference
- GPU governance model with quota, isolation, and right-sizing
- Latency, throughput, and cost baselines with SLOs
- Security and compliance controls for model serving
- Self-service operating model with clear ownership boundaries
- Knowledge transfer and team capability uplift
Not covered by this engagement
- Model development, selection, or fine-tuning
- Training pipelines and feature engineering
- Data science workflows and notebook environments
- Prompt engineering or RAG application development
Best suited for
Enterprise platform teams under pressure to support AI inference workloads on existing Kubernetes infrastructure. Typically triggered when leadership has committed to AI initiatives but the platform team has no established, governed path to production for inference services.
Related capabilities
Talk to us about AI inference on Kubernetes
Most engagements start with a short call. We'll confirm scope and the right shape of engagement.
Frequently Asked Questions
Do you build AI models?
No. We do not build, fine-tune, or select models. We make sure the platform underneath model serving is production-grade - reliable, governed, observable, and cost-controlled.
Can you help if we have not started AI inference yet?
Yes - that is often the best time. Building inference platform foundations before the first production model deployment is far cheaper than retrofitting after. We help platform teams design GPU governance, deployment patterns, and operating models so inference workloads land on a platform designed for them.
How does this fit alongside our existing platform?
Inference workloads do not warrant a separate platform team - they should land on the same Kubernetes substrate as everything else, with additional patterns for GPU scheduling, latency-sensitive workloads, and cost attribution. We extend your existing platform rather than building parallel infrastructure.
What is not covered by this engagement?
Model development, selection, or fine-tuning. Training pipelines and feature engineering. Data science workflows and notebook environments. Prompt engineering or RAG application development. We focus on the platform layer that hosts production inference.