What if developers could access multiple AI providers through a single API?
The AI ecosystem is becoming increasingly fragmented: OpenAI, Google, Together AI and other providers offer different models, pricing structures, capabilities and API interfaces.
For developers building AI-powered products, this creates an operational problem: you might want one provider for a particular task, another for a different workload and a third as a fallback.
Managing all of this independently creates authentication overhead, divergent request structures and inconsistent error formats. This is one of the problems an AI API gateway is designed to solve.
What is an AI API gateway?
An AI API gateway acts as an intermediary between an application and multiple AI providers.
Instead of integrating directly with several provider APIs, developers communicate with one unified endpoint. The gateway handles the provider-specific work behind the scenes:
“Developer application → API gateway → Provider adapter → AI provider → Response”
The gateway can also introduce essential capabilities:
- API key management
- Usage tracking
- Provider routing
- Rate limiting
- Request logging
- Billing and credit management
- Error handling
The gateway isn't another AI model: it's infrastructure that makes working with AI providers manageable and production-ready.
The challenge of building a unified provider layer
Consider two providers offering text-generation capabilities. Their APIs differ in authentication, request parameters, model identifiers, response structures, and token usage reporting.
A better approach introduces provider adapters. Each adapter translates provider-specific requirements into a common internal interface:
- OpenAI adapter: Handles OpenAI-specific authentication, streaming, and response parsing.
- Gemini adapter: Handles Google's provider-specific API requirements.
- Together AI adapter: Handles open-source model parameters and inference endpoints.
The gateway communicates with these adapters through a consistent abstraction. However, a unified interface should not pretend every model supports identical capabilities—it must preserve provider-specific features where necessary.
API key management is more important than it looks
Managing API keys is more than generating random strings. A production system must handle:
- Secure key generation
- Hashed key storage
- Key revocation
- Workspace ownership
- Usage permissions
- Rate limits
- Key rotation
- Audit history
Provider API keys should remain securely managed on the server side. A customer-facing API key and a provider's secret key serve different purposes and should never be treated interchangeably.
Usage tracking and billing
This is where an AI gateway becomes a commercial product rather than simply a routing layer.
A simplified request lifecycle:
- Authenticate the customer's API key.
- Identify the associated workspace.
- Check available credits or spending limits.
- Determine the requested provider and model.
- Forward the request through the appropriate adapter.
- Receive and process the provider response.
- Extract available usage information.
- Calculate the applicable charge.
- Record the transaction in an append-only ledger.
- Return the response to the customer.
The usage ledger needs to be designed with consistency and traceability in mind.
The challenge of billing in local currencies
In emerging markets, paying international AI providers with foreign credit cards can be difficult.
A gateway can introduce a local billing layer. For example, a Nigerian-focused service could allow customers to fund accounts in Naira while the underlying gateway handles provider billing and usage accounting:
- Payment confirmation
- Credit allocation
- Exchange-rate management where applicable
- Pricing and markup
- Refund handling
- Reconciliation
The system must distinguish between money received, credits issued and actual provider consumption.
Why we explored SwitchRouter
SwitchRouter came from a practical observation: accessing global AI infrastructure shouldn't have to mean managing a different integration and payment experience for every provider.
The engineering exploration included:
- A NestJS backend.
- PostgreSQL for persistent application data.
- Redis for supporting usage and operational workloads.
- Docker-based development infrastructure.
- Provider adapters for OpenAI, Gemini and Together AI.
- Workspace-based billing boundaries.
- API key management.
- Payment and usage architecture.
Treating workspace billing as a distinct boundary provides a natural place to associate API keys, usage, billing balances and spending controls.
Final thoughts
Building an AI API gateway is an example of how infrastructure products emerge around new technology.
“The next generation of AI products won't just depend on better models. They'll depend on better infrastructure around those models.”
At Starterslab, we enjoy working on precisely these kinds of problems: turning complex technical capabilities into coherent, dependable products.