AI Platform Engineering: Building the Internal Infrastructure Your AI Teams Actually Need

Here's a pattern we see in almost every mid-to-large UK business that's been experimenting with AI for more than a year: dozens of teams running independent AI projects, each with their own API keys, their own prompt libraries, their own evaluation methods, and their own deployment pipelines.

The result is predictable. Duplicated effort, inconsistent quality, spiralling costs, zero knowledge sharing, and — most critically — no way to move from experiment to production at scale.

AI platform engineering solves this by building the shared internal infrastructure that AI teams need to ship reliably. It's the same discipline that platform engineering brought to DevOps, now applied to the unique challenges of AI development and deployment.

Why AI Needs Its Own Platform Layer

Traditional software development already has mature platforms: CI/CD pipelines, container orchestration, observability stacks, shared libraries. Developers don't each build their own deployment pipeline from scratch.

But AI development has characteristics that make general-purpose platforms insufficient:

Non-Deterministic Outputs

Software returns the same output for the same input. AI models don't. This means testing, evaluation, and quality assurance need fundamentally different approaches — and those approaches should be standardised across the organisation, not reinvented by each team.

Rapid Model Evolution

When OpenAI, Anthropic, or Google release a new model every few months, every AI application needs to evaluate whether to upgrade. Without a platform layer, this becomes dozens of independent migration projects. With one, it's a centralised evaluation followed by a coordinated rollout.

Cost Proportional to Usage

Traditional software has relatively fixed infrastructure costs. AI applications have per-token costs that scale linearly with usage. Without centralised cost management, a single team's runaway prompt can generate thousands in unexpected charges overnight.

Regulatory and Compliance Requirements

The EU AI Act, the UK's evolving AI regulatory framework, and industry-specific requirements (FCA for financial services, NHS Digital for healthcare) demand consistent governance. One team's compliance failure becomes the entire organisation's problem.

The Five Layers of an AI Platform

A mature AI platform engineering practice builds five interconnected layers:

Layer 1: The AI Gateway

The foundation. Every AI API call from every team routes through a centralised gateway that provides:

• Unified authentication and key management — no more API keys in environment variables or, worse, committed to Git repositories
• Cost tracking and allocation — every token charged to the right team, project, and cost centre
• Rate limiting and quotas — prevent any single application from consuming the entire budget
• Model routing — automatically direct requests to the most appropriate (and cost-effective) model based on task complexity
• Fallback and resilience — if one provider goes down, automatically route to an alternative
• Audit logging — every request and response logged for compliance, debugging, and optimisation

Tools in this space: LiteLLM, Portkey, Helicone, or custom-built gateways using Kong or Envoy with AI-specific plugins.

UK-specific consideration: Data residency requirements may mandate that certain requests route only to EU-hosted model endpoints. Your gateway should enforce this automatically.

Layer 2: The Prompt Library and Registry

Prompt engineering is software engineering. Treating prompts as disposable text that lives in application code is like storing SQL queries as string literals — it works until it doesn't.

A prompt registry provides:

• Version-controlled prompt templates with semantic versioning
• A/B testing infrastructure for comparing prompt variants
• Performance metrics tied to specific prompt versions
• Shared, audited system prompts for common tasks (summarisation, extraction, classification)
• Governance controls — who can modify production prompts, and what review process applies

This is where organisations unlock compound returns. When one team discovers that a particular extraction prompt works brilliantly on financial documents, that prompt becomes available to every other team immediately — not rediscovered six months later through accident.

Layer 3: Evaluation and Testing Framework

You cannot improve what you cannot measure. Most AI teams evaluate their systems informally: "does this look right?" That's not engineering.

A platform-level evaluation framework provides:

• Standardised evaluation datasets curated by domain experts
• Automated regression testing — when models update, automatically re-run evaluations
• Human-in-the-loop evaluation workflows for subjective quality assessment
• Metrics dashboards showing accuracy, latency, cost, and quality trends over time
• Red-teaming tools for adversarial testing before production deployment

The critical insight: evaluation infrastructure is expensive to build but cheap to share. One investment serves every team. Without the platform, each team either skips proper evaluation (dangerous) or builds their own (wasteful).

Layer 4: Deployment and Orchestration

Getting an AI feature from "works on my laptop" to "running reliably in production" requires more than just containerising a Flask app. AI deployments need:

• Gradual rollout mechanisms — canary deployments where 5% of traffic goes to the new version while monitoring quality metrics
• Feature flags for AI capabilities — toggle AI features without redeploying the entire application
• Multi-model orchestration — many production AI systems chain multiple models together; the platform manages these pipelines
• Caching layers — identical requests should return cached responses, not burn tokens
• Queue management — handle burst traffic gracefully when model APIs have rate limits

Layer 5: Observability and Governance

The top layer provides visibility and control across everything below:

• Cost dashboards with trend analysis, anomaly detection, and budget alerts
• Quality monitoring with automated drift detection — is the AI getting worse?
• Compliance reporting — automated generation of documentation required by regulators
• Usage analytics — which teams use AI most effectively? Where are the bottlenecks?
• Incident management — when an AI system produces harmful output, the platform enables rapid response

Building vs Buying: The Pragmatic Approach

You don't need to build all five layers from scratch. The smart approach:

Start with the Gateway (Month 1-2)

This delivers immediate value: cost visibility, security, and resilience. Open-source options like LiteLLM can be deployed in a day. The return on investment is almost instant — most organisations discover they're spending 30-50% more than they thought once they have visibility.

Add Evaluation Next (Month 2-4)

Start simple: create shared evaluation datasets for your most critical use cases. Build automated pipelines that run evaluations on model updates. This prevents quality regressions and gives leadership confidence that AI systems are being properly governed.

Prompt Library Third (Month 3-5)

Begin by cataloguing what already exists across teams. You'll find enormous duplication. Consolidating into a shared library with proper versioning immediately reduces duplication and improves quality.

Deployment and Observability (Month 4-8)

Build on your existing DevOps infrastructure. Most of the deployment layer is extensions to systems you already have. The AI-specific additions (canary deployments based on quality metrics, caching layers) can be added incrementally.

Staffing an AI Platform Team

The ideal team combines:

• Platform engineers with experience in developer tooling, APIs, and infrastructure-as-code
• ML engineers who understand model evaluation, fine-tuning, and optimisation
• A product manager who treats internal teams as customers and ruthlessly prioritises based on adoption impact
• A security/compliance specialist (can be part-time or shared) who ensures the platform meets regulatory requirements

For UK mid-market companies (200-2,000 employees): Start with 2-3 engineers and a part-time PM. This team can build and maintain the gateway and evaluation layers, with the prompt library as a side project. Scale to 4-6 as adoption grows.

For smaller companies (50-200): You probably don't need a dedicated team. Instead, designate an "AI platform owner" — a senior engineer who manages the gateway, maintains evaluation standards, and curates the prompt library. Budget 20-40% of their time.

The ROI Case

AI platform engineering typically delivers:

• 20-40% cost reduction from centralised model routing, caching, and quota management
• 3-5x faster time-to-production for new AI features (shared infrastructure vs building from scratch)
• Measurable quality improvements from standardised evaluation and regression testing
• Reduced compliance risk from centralised governance and audit trails
• Higher developer satisfaction — engineers spend time on business problems, not infrastructure plumbing

Common Mistakes

Over-engineering from day one. Don't build a comprehensive platform before you have users. Start with the gateway, prove value, and let adoption drive investment.

Ignoring developer experience. If the platform is harder to use than raw API calls, teams will route around it. Every friction point reduces adoption. The platform must be easier than the alternative.

Centralising too aggressively. The platform should enable teams, not control them. If every prompt change requires a pull request reviewed by a central committee, you've created a bottleneck, not a platform.

Forgetting about cost allocation. The single most politically valuable feature of an AI platform is accurate cost attribution. When every team can see exactly what they're spending, behaviour changes overnight.

Getting Started This Week

1. Audit your current state. How many teams use AI? How many API keys exist? What's the total monthly spend?
2. Deploy an AI gateway. LiteLLM or Portkey, behind your existing API gateway. Route all AI traffic through it.
3. Create a shared evaluation dataset. Pick your most important AI use case. Build 50-100 test cases with expected outputs.
4. Set a monthly review cadence. Review costs, quality metrics, and adoption monthly. Adjust the platform roadmap based on what teams actually need.

The companies that treat AI infrastructure as a first-class engineering discipline — rather than an afterthought — are the ones turning AI experiments into competitive advantages. The platform is how you get there.