AI API Gateways & Model Routers: Managing Multi-Provider AI Without the Chaos

Here's a pattern we see constantly: a UK business starts with one AI provider. OpenAI, probably. They build a few integrations, maybe a customer service chatbot, some document processing, a coding assistant for the dev team.

Then reality sets in. Claude is better at analysis. Gemini handles long documents more cheaply. Open-source models running on-premise tick the data sovereignty box. GPT-4o is still the default for quick tasks.

Before they know it, they're managing four different API keys, four different billing dashboards, four different rate limit policies, and four different error handling strategies. Their code is littered with provider-specific logic. When one provider has an outage, everything breaks.

This is the multi-provider AI management problem. And in 2026, it's almost universal.

The Problem Isn't Choice — It's Coordination

Having access to multiple AI models is genuinely valuable. Different models excel at different tasks:

• Complex reasoning and analysis: Claude excels here — fewer hallucinations, better at following nuanced instructions
• Speed and cost for simple tasks: GPT-4o Mini or Gemini Flash handle routine queries at a fraction of the cost
• Long document processing: Gemini's million-token context window makes it a natural fit
• Sensitive data processing: On-premise models via Ollama keep everything behind your firewall
• Code generation: Claude and GPT-4o trade leadership depending on the language and task

The problem isn't having options. It's that managing them directly creates operational overhead that eventually outweighs the benefits. Every provider has slightly different API formats, error codes, rate limits, and pricing structures. Multiply that across an organisation with multiple teams, and you've got chaos.

Enter the AI API Gateway

An AI API gateway sits between your applications and your AI providers. It provides a single, unified interface that handles the complexity of multi-provider management behind the scenes.

Think of it like an API gateway in traditional software architecture — but specifically designed for the quirks of AI model APIs.

What a Good AI Gateway Does

Unified API: Your applications call one endpoint with one format. The gateway translates requests to whichever provider you've configured. Switch from OpenAI to Anthropic? Change a config setting, not your application code.

Automatic failover: When OpenAI has an outage (and they will), the gateway automatically routes requests to a fallback provider. Your users never notice.

Cost management: Set spending limits per team, per project, per model. Get alerts before you hit them. Automatically route to cheaper models for tasks that don't need frontier capabilities.

Rate limiting and queuing: Instead of hitting provider rate limits and failing, the gateway queues requests intelligently. Burst traffic gets smoothed out instead of crashing.

Logging and observability: Every request logged centrally. Which teams are using which models? What's the average latency? Where are the errors? One dashboard instead of four.

Caching: Identical requests get cached responses. For many business use cases, this alone can cut costs by 20-40%.

The Major Players in 2026

LiteLLM

The open-source option that's become the default for teams that want control. LiteLLM provides a unified API across 100+ model providers using the OpenAI API format.

Best for: Teams with engineering capacity who want full control over their AI infrastructure. Self-hosted, customisable, no vendor lock-in.

How it works: Deploy as a proxy server. All your applications point to LiteLLM instead of individual providers. It handles translation, routing, and logging.

Your App → LiteLLM Proxy → OpenAI / Anthropic / Google / Ollama

Key strengths:

• Open source — audit the code, modify it, self-host it
• Budget controls and team-level spending limits
• Automatic retries and fallbacks
• Works with every major provider and most minor ones
• Active development community

Considerations: Requires engineering resource to deploy and maintain. Not a managed service (though hosted options exist).

OpenRouter

A managed service that provides access to hundreds of models through a single API. OpenRouter handles the provider relationships, so you don't have to.

Best for: Businesses that want simplicity without infrastructure overhead. Pay per token, access everything.

How it works: Sign up, get an API key, point your applications at OpenRouter. They handle routing, billing, and provider management.

Key strengths:

• Access to models you couldn't easily access otherwise (custom fine-tunes, research models)
• Dynamic pricing — models compete on price
• Simple billing — one invoice, one dashboard
• No infrastructure to manage

Considerations: You're adding another dependency. Slightly higher latency than direct provider calls. Pricing includes a margin.

Portkey

The enterprise-focused option with emphasis on reliability, observability, and governance.

Best for: Larger organisations that need enterprise features — audit trails, compliance controls, advanced analytics.

Key strengths:

• Production-grade reliability features (automatic failover, load balancing)
• Advanced analytics and cost attribution
• Governance controls suitable for regulated industries
• Prompt management and versioning

Considerations: Enterprise pricing. May be overkill for smaller teams.

Helicone

Focused on observability and cost management rather than routing. Helicone gives you deep visibility into how your organisation uses AI.

Best for: Teams that have their routing sorted but need better visibility and cost control.

Key strengths:

• Detailed request-level analytics
• Cost tracking and attribution
• Prompt experimentation and A/B testing
• Lightweight integration (often just a URL change)

Routing Strategies That Actually Work

Having a gateway is step one. Configuring it intelligently is where the real value lives.

Cost-Based Routing

Route requests to the cheapest model that meets quality requirements. Customer FAQ responses don't need Claude Opus — GPT-4o Mini at 1/50th the price handles them fine.

Set up model tiers:

• Tier 1 (Premium): Claude Opus, GPT-4o — complex analysis, important customer communications
• Tier 2 (Standard): Claude Sonnet, GPT-4o — general tasks, content generation
• Tier 3 (Economy): Gemini Flash, GPT-4o Mini — classification, extraction, simple queries

Tag requests with their tier. The gateway routes accordingly.

Latency-Based Routing

For real-time applications (chatbots, autocomplete), route to whichever provider is currently responding fastest. AI provider latency varies significantly throughout the day and across regions.

Capability-Based Routing

Some tasks need specific capabilities. Long documents go to Gemini. Vision tasks go to GPT-4o or Claude. Code generation gets routed based on the programming language. The gateway makes these decisions automatically based on request metadata.

Compliance-Based Routing

For UK businesses handling personal data, GDPR compliance matters. Route sensitive data processing to EU-hosted models or on-premise deployments. Keep non-sensitive tasks on cheaper cloud providers.

Implementation for UK SMEs

You don't need a massive infrastructure team to benefit from an AI gateway. Here's a practical approach:

Phase 1: Start with LiteLLM (Week 1-2)

Deploy LiteLLM as a Docker container on your existing infrastructure. Configure your primary provider (probably OpenAI) and one fallback (Anthropic or Google). Point your existing applications at the proxy.

Immediate benefits: centralised logging, automatic failover, unified billing view.

Phase 2: Add Routing Logic (Week 3-4)

Analyse your usage patterns from the logs. Which requests are expensive but don't need to be? Set up cost-based routing. Add a budget ceiling.

Typical savings: 25-40% reduction in AI API costs.

Phase 3: Expand and Optimise (Month 2+)

Add caching for repeated requests. Enable on-premise models for sensitive data. Set up per-team budgets and usage dashboards.

Cost Reality Check

For a typical UK SME spending £2,000-5,000/month on AI APIs:

• LiteLLM (self-hosted): Free (plus your infrastructure costs, typically £50-100/month)
• OpenRouter: ~5-10% markup on model costs
• Portkey: Starts around $49/month, scales with usage
• Helicone: Free tier available, paid plans from $20/month

The gateway typically pays for itself within the first month through cost optimisation alone.

Common Mistakes to Avoid

Over-routing too early. Start with simple failover before adding complex routing logic. Get data first, then optimise.

Ignoring latency overhead. Every gateway adds some latency. For most use cases, 50-100ms extra is imperceptible. For real-time applications, measure carefully.

Not setting budget alerts. A misconfigured loop can burn through thousands in API credits overnight. Set hard limits, not just alerts.

Vendor lock-in at the gateway level. Use an open-source gateway or ensure your gateway provider doesn't require proprietary formats. The whole point is avoiding lock-in.

Forgetting about caching. Many AI requests are functionally identical. Semantic caching (returning cached responses for similar, not just identical, requests) can dramatically reduce costs.

The Strategic View

AI API gateways aren't just an infrastructure convenience. They're a strategic asset.

When you can switch providers in minutes instead of weeks, you have genuine negotiating power. When you can route sensitive data to on-premise models automatically, compliance becomes a configuration change instead of an architecture rewrite. When every team has a budget dashboard, AI spending becomes predictable and accountable.

The businesses that will use AI most effectively in the next few years won't be the ones with the best single-provider relationship. They'll be the ones with the best orchestration layer — the ones who can use the right model for the right task at the right price, automatically.

That starts with putting a gateway between your applications and the growing universe of AI providers.

Getting Started

1. Audit your current AI usage. How many providers? How many API keys? Who's spending what?
2. Deploy LiteLLM as a proof of concept. Point one application at it. Watch the logs for a week.
3. Identify your routing opportunities. Which requests could use cheaper models? Which need better reliability?
4. Set budgets before expanding. Every new model you add is a new cost centre. Manage them proactively.
5. Review monthly. The AI provider landscape changes constantly. New models, new pricing, new capabilities. Your routing strategy should evolve with it.

The multi-model future isn't coming — it's here. The question is whether you manage it deliberately or let it manage you.