What is AI Gateway?
An AI gateway is an infrastructure layer that sits between applications and AI models, managing routing, authentication, rate limiting, cost tracking, and failover to provide centralised control and visibility over all AI model interactions across an organisation.
What Is an AI Gateway?
An AI gateway is a centralised infrastructure component that manages how applications within an organisation access and interact with AI models. It functions as a single entry point, similar to an API gateway in traditional software architecture, but specifically designed for the unique requirements of AI workloads.
As organisations adopt multiple AI models from different providers, such as OpenAI, Google, Anthropic, and open-source alternatives, an AI gateway provides a unified layer for managing access, controlling costs, ensuring reliability, and maintaining security across all of these interactions.
For businesses in Southeast Asia navigating the rapidly expanding landscape of AI model providers, an AI gateway brings order to what can quickly become a complex and expensive web of integrations.
How an AI Gateway Works
An AI gateway sits between your applications and the AI models they call. When an application needs an AI prediction or generation, the request passes through the gateway, which provides several critical functions:
Request Routing
The gateway directs requests to the appropriate model based on configurable rules. For example:
- Cost optimisation: Simple queries are routed to cheaper, faster models, while complex queries are sent to more capable, expensive models
- Failover: If the primary model provider experiences downtime, the gateway automatically routes requests to a backup provider
- Geographic routing: Requests from Southeast Asian users can be routed to models hosted in regional data centres for lower latency
Authentication and Access Control
The gateway manages API keys and access permissions centrally, rather than distributing credentials across every application. This means:
- Individual teams do not need direct access to provider API keys
- Access can be granted or revoked from a single control plane
- Usage can be tracked and attributed to specific teams or projects
Rate Limiting and Cost Control
AI model API calls can be expensive, and runaway usage can generate unexpected bills. The gateway enforces:
- Rate limits: Per-team or per-application limits on requests per minute or per day
- Budget caps: Hard spending limits that prevent any team from exceeding their allocated AI budget
- Usage tracking: Detailed dashboards showing which teams, applications, and use cases are consuming the most AI resources
Caching and Optimisation
Many AI requests are repeated or similar. The gateway can cache responses to identical prompts, reducing both latency and cost. It can also optimise prompts and manage token usage to minimise spending.
Logging and Compliance
Every request and response passing through the gateway is logged, creating a complete audit trail. This is essential for:
- Compliance: Demonstrating how AI is being used within the organisation
- Debugging: Tracing issues back to specific requests and responses
- Quality monitoring: Identifying patterns of poor model performance
Why an AI Gateway Matters for Business
The business case for an AI gateway becomes clear as organisations move beyond a single AI use case:
Cost Visibility and Control
Without centralised management, AI costs can spiral as different teams independently call expensive model APIs. An AI gateway provides CFO-friendly visibility into exactly where AI spending is going and the tools to control it. Organisations that implement AI gateways typically report 20-40% reduction in AI API costs through caching, routing optimisation, and elimination of wasteful usage.
Vendor Independence
By abstracting the connection between applications and model providers, an AI gateway allows organisations to switch between providers without changing application code. If a new model offers better performance or pricing, the switch happens at the gateway level, not across dozens of individual applications. This is strategically important as the AI model market evolves rapidly.
Reliability and Uptime
AI model providers experience outages. An AI gateway with failover capabilities ensures that your business-critical AI features continue working by automatically routing to backup providers. For customer-facing applications in e-commerce, fintech, and logistics across Southeast Asia, this reliability is essential.
AI Gateway Solutions
Several solutions are available:
- Portkey: Purpose-built AI gateway with advanced routing, caching, and observability
- LiteLLM: Open-source proxy that provides a unified interface to 100+ model providers
- Kong AI Gateway: Enterprise API gateway extended with AI-specific capabilities
- Cloudflare AI Gateway: Edge-based gateway with global presence including Asia-Pacific
- Custom solutions: Built on standard API gateway infrastructure like NGINX or Envoy
For SMBs in Southeast Asia, LiteLLM offers a strong open-source starting point, while Cloudflare AI Gateway provides a managed option with strong regional presence.
Implementing an AI Gateway
A practical implementation path includes:
- Audit current AI usage: Catalogue all applications calling AI models, which providers they use, and what they spend
- Choose a gateway solution based on your scale, technical capabilities, and provider diversity
- Start with a single use case: Route your highest-volume AI application through the gateway first
- Configure cost controls: Set budget caps and rate limits before expanding access
- Add failover routes: Configure backup providers for critical workloads
- Expand gradually: Migrate additional applications to the gateway as you validate its reliability
An AI gateway is not required for organisations with a single AI use case, but for any business running multiple AI-powered features or planning to scale AI adoption, it becomes essential infrastructure.
An AI gateway directly addresses the two biggest concerns CEOs and CTOs have about AI adoption: cost control and reliability. As organisations scale from one or two AI features to dozens, the complexity of managing multiple model providers, API keys, and usage patterns becomes a significant operational and financial risk.
For business leaders in Southeast Asia, an AI gateway also provides strategic flexibility in a rapidly evolving market. The ability to switch between AI model providers without rewriting application code means your organisation can always use the best available model for each use case, whether that is a global provider like OpenAI or a regional model optimised for Southeast Asian languages and contexts.
The cost control benefits alone often justify the investment. Organisations typically discover that 20-40% of their AI API spending is wasted on redundant calls, cacheable requests, or over-powered models being used for simple tasks. An AI gateway with intelligent routing and caching captures these savings automatically. For a company spending $10,000 per month on AI APIs, that represents $2,000-4,000 in monthly savings, which quickly covers the cost of implementing and maintaining the gateway.
- Implement an AI gateway before AI costs become significant. It is much easier to establish cost controls and usage patterns early than to retrofit them after teams have built habits around unmanaged API usage.
- Use the gateway to enforce model selection policies. Route simple tasks to cheaper models automatically, reserving expensive models for complex requests that justify the cost.
- Configure failover between at least two model providers for any business-critical AI feature to ensure uptime during provider outages.
- Leverage caching aggressively. Many AI applications send identical or nearly identical requests repeatedly, and caching can reduce costs and latency dramatically.
- Centralise API key management through the gateway. Distributing provider API keys across teams and applications creates security risks and makes cost attribution impossible.
- Use gateway logging for compliance purposes, particularly if your AI applications handle customer data subject to ASEAN data protection regulations.
- Evaluate whether a managed gateway service or open-source solution best fits your team capabilities and scale.
Frequently Asked Questions
When does a business need an AI gateway?
A business should consider an AI gateway when it has more than two or three applications calling AI model APIs, when AI spending exceeds $2,000-3,000 per month, or when multiple teams are independently managing their own AI provider integrations. If you are using a single model in a single application, a gateway adds unnecessary complexity. But as AI usage scales, the cost control, reliability, and governance benefits quickly become essential.
How does an AI gateway differ from a regular API gateway?
A regular API gateway manages traffic between clients and backend services with features like rate limiting, authentication, and load balancing. An AI gateway adds capabilities specific to AI workloads: intelligent model routing based on request complexity, semantic caching that recognises similar but not identical prompts, token usage tracking and optimisation, model failover between different providers, and AI-specific observability including response quality monitoring. Some organisations extend their existing API gateway with AI plugins rather than deploying a separate solution.
More Questions
Yes, significantly. AI gateways reduce costs through several mechanisms: caching responses to identical or similar requests, routing simple requests to cheaper models while reserving expensive models for complex tasks, enforcing rate limits and budget caps to prevent waste, and providing visibility into usage patterns so teams can optimise their AI consumption. Organisations typically report 20-40% cost reductions after implementing an AI gateway, with the savings increasing as AI usage scales.
Need help implementing AI Gateway?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how ai gateway fits into your AI roadmap.