AI API Gateways: How Smart Model Routing Cuts Costs and Prevents Chaos at Scale

The typical AI bill at a mid-size UK company looks something like this: £8,000 in January. £14,000 in February. £23,000 in March. Nobody can explain the spike because nobody knows which team, application, or feature is responsible.

Meanwhile, the CTO discovers that three different teams are paying for three separate OpenAI accounts, two teams have Anthropic keys hardcoded in production, and someone's weekend experiment is burning through Claude API credits running an infinite loop that nobody noticed for five days.

This isn't a cautionary tale. It's Tuesday at most organisations that adopted AI without centralised infrastructure. And the fix is deceptively simple: an AI API gateway.

What Is an AI API Gateway?

An AI API gateway sits between your applications and the AI model providers. Every API call — to OpenAI, Anthropic, Google, Mistral, Cohere, or your self-hosted models — routes through it.

Think of it as what Cloudflare does for web traffic, applied to AI model calls. It doesn't change what your applications do. It gives you visibility and control over how they do it.

The core capabilities:

• Unified access — one endpoint, multiple providers behind it
• Cost tracking — every token attributed to a team, project, and environment
• Rate limiting — prevent runaway usage before it hits your budget
• Model routing — send requests to the optimal model based on the task
• Resilience — automatic failover when providers have outages
• Security — no API keys in application code, ever
• Compliance — audit logs for every request and response

The Cost Control Problem (and Why It Gets Worse)

AI costs don't behave like traditional cloud costs. They're per-token, which means they're directly proportional to usage — and usage is wildly unpredictable.

A developer adding a summarisation feature might test with short documents (cheap) and deploy to production where users submit 50-page contracts (expensive). A customer service chatbot might handle 100 conversations on Monday and 2,000 on Friday. A code review tool might process a 200-line pull request or a 15,000-line monorepo refactor.

Without a gateway, you discover these costs after the fact. The invoice arrives. You're surprised. You try to figure out what happened. Nobody has the data.

With a gateway, you see costs in real-time. More importantly, you can set budgets and alerts before the spend happens:

• Team budgets: "Engineering gets £5,000/month for AI, Marketing gets £2,000"
• Application budgets: "The chatbot gets £3,000/month maximum"
• Per-request limits: "No single request can cost more than £2"
• Anomaly alerts: "If daily spend exceeds 150% of the 7-day average, notify the platform team"

Real Numbers

A UK fintech we worked with had 12 teams making independent AI API calls. After deploying a gateway:

• Month 1: Discovered £3,400/month in duplicate calls (same data being summarised by two different services)
• Month 2: Implemented caching, saving 22% on total spend
• Month 3: Introduced smart routing (see below), saving an additional 31%
• Net result: £7,200/month saved against a gateway that costs £400/month to run

Smart Model Routing: The Killer Feature

Not every request needs the most powerful (and expensive) model. A classification task ("is this email a complaint or a compliment?") doesn't need GPT-4o or Claude Opus. A complex legal analysis does.

Smart model routing automatically directs requests to the most cost-effective model that can handle them:

Complexity-Based Routing

Analyse the incoming request and route based on estimated complexity:

• Simple tasks (classification, extraction, short summarisation) → GPT-4o Mini, Claude Haiku, or Gemini Flash. Cost: ~£0.0001 per request.
• Standard tasks (content generation, analysis, code review) → GPT-4o, Claude Sonnet. Cost: ~£0.003 per request.
• Complex tasks (multi-step reasoning, legal analysis, strategic planning) → Claude Opus, GPT-4o with extended thinking. Cost: ~£0.02 per request.

The savings are dramatic. Most organisations find that 60-70% of their AI requests are simple tasks being served by expensive models. Routing those to smaller models cuts costs by 50-80% with negligible quality impact.

Latency-Based Routing

Some applications need sub-second responses (chatbots, real-time suggestions). Others can wait (batch processing, report generation). The gateway routes latency-sensitive requests to faster models and endpoints, while batch work goes to providers with better throughput pricing.

Provider Health Routing

AI provider outages are more common than people realise. OpenAI has had multiple multi-hour degradations. Anthropic and Google aren't immune either. A gateway with health checking automatically fails over to alternative providers, maintaining availability without application changes.

Data Residency Routing

For UK businesses handling sensitive data, the gateway can enforce that certain request categories only route to EU-hosted endpoints. This is particularly critical for:

• Financial services data (FCA requirements)
• Healthcare data (NHS Digital standards)
• Customer personal data (UK GDPR)
• Government or defence-adjacent work

The application code doesn't need to know about these routing rules. The gateway handles it transparently.

Implementation: From Zero to Gateway in a Day

Option 1: LiteLLM (Open Source)

The most popular open-source AI gateway. Deploy on your own infrastructure, maintain full control.

# Deploy with Docker
docker run -d -p 4000:4000 \
  -e OPENAI_API_KEY=sk-... \
  -e ANTHROPIC_API_KEY=sk-... \
  ghcr.io/berriai/litellm:main-latest \
  --config /path/to/config.yaml

Pros: Free, self-hosted, extensive model support, active community. Cons: You manage the infrastructure, less polished UI for non-technical users. Best for: Engineering-led organisations comfortable with self-hosted tools.

Option 2: Portkey (Managed)

A managed gateway with a strong focus on observability and cost management.

Pros: Excellent dashboard, easy setup, good compliance features. Cons: Per-request pricing adds to costs, data passes through their infrastructure. Best for: Organisations that want gateway benefits without managing infrastructure.

Option 3: Helicone (Managed, Observability Focus)

Strong on the logging and observability side, with gateway capabilities.

Pros: Exceptional request logging and analytics, generous free tier. Cons: Routing features less mature than dedicated gateways. Best for: Organisations primarily seeking visibility, with routing as secondary.

Option 4: Custom Build (Kong/Envoy + Plugins)

If you already have an API gateway (Kong, Envoy, AWS API Gateway), add AI-specific plugins.

Pros: Leverages existing infrastructure, maximum control. Cons: Significant engineering effort, AI-specific features must be built. Best for: Large enterprises with existing API gateway teams.

The Security Case

Every AI API key that lives in an environment variable, a config file, or (shudder) application source code is a security incident waiting to happen. The gateway eliminates this by being the only entity that holds provider API keys.

Applications authenticate to the gateway using your existing identity infrastructure (OAuth, service accounts, API keys that you control). The gateway translates to provider-specific credentials. If an application's credentials are compromised, you revoke access at the gateway — instantly, without rotating provider keys.

This also enables:

• Request filtering — block requests containing PII, sensitive data, or prohibited content before they leave your network
• Response filtering — scan model outputs for data leakage, hallucinated confidential information, or inappropriate content
• Prompt injection detection — identify and block common prompt injection patterns at the gateway level

Caching: The Low-Hanging Fruit

Many AI applications make identical or near-identical requests repeatedly. A customer service chatbot answering "what are your opening hours?" doesn't need a fresh API call every time.

Gateway-level caching provides:

• Exact match caching — identical prompts return cached responses. Zero cost, instant response.
• Semantic caching — similar (but not identical) prompts return cached responses when the similarity exceeds a threshold. Requires embedding comparison.
• TTL-based expiration — cached responses expire after a configurable period, ensuring freshness.
• Cache segmentation — different applications and use cases can have different caching policies.

Typical savings from caching alone: 15-30% of total AI API spend. For customer-facing applications with common queries, savings can exceed 50%.

Governance and Compliance

The EU AI Act and the UK's AI regulatory framework both require organisations to demonstrate governance over their AI systems. A gateway provides this by default:

• Complete audit trails — every request and response, timestamped, attributed, and searchable
• Usage reporting — automated generation of compliance reports showing what AI is being used for, by whom, and how
• Policy enforcement — technical controls that ensure organisational AI policies are followed, not just documented
• Incident response — when something goes wrong, the gateway log is the forensic record

For FCA-regulated firms, this is particularly valuable. The gateway log demonstrates that AI usage is monitored, controlled, and auditable — exactly what regulators want to see.

Migration Strategy for Existing Applications

You don't need to migrate everything at once. A phased approach:

Week 1: Deploy and Shadow

Deploy the gateway alongside existing direct API calls. Route new applications through it. Don't touch existing applications yet.

Week 2-3: Migrate Non-Critical First

Move development and staging environments to the gateway. Move internal tools and batch processing. Validate that the gateway introduces no issues.

Week 4-6: Migrate Production

Move production applications one by one, starting with the least critical. Monitor latency, error rates, and costs at each step.

Week 7+: Enable Advanced Features

With all traffic flowing through the gateway, enable smart routing, caching, and budget controls. This is where the ROI accelerates.

The Bottom Line

An AI API gateway is the single highest-ROI infrastructure investment for any UK business using AI at scale. It typically pays for itself in the first month through cost savings alone — before you factor in security, compliance, resilience, and developer productivity benefits.

The companies that are scaling AI successfully in 2026 aren't the ones with the most advanced models or the biggest budgets. They're the ones with the operational infrastructure to use AI reliably, safely, and efficiently. The gateway is where that starts.