An API gateway is a managed entry point for exposing and governing APIs and the backend services behind them. An AI gateway applies the same gateway idea to model traffic and may add model-aware features: routing across providers, prompt inspection, token and cost tracking, caching and guardrails. The two categories overlap, and a conventional API gateway can proxy a request to an LLM. The real question is whether you need controls that understand prompts and models, not whether the label says “AI”.
What an API gateway does
A traditional API gateway sits between clients and backend services. It exposes endpoints, authenticates callers, applies rate limits and ordinary request policies, and produces traffic logs and metrics. AWS describes Amazon API Gateway as a service for creating and deploying REST and WebSocket APIs that access AWS services, other web services, and data stored in AWS. That is the classic role, and it does not rule out AI features in other API gateway products.
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What an AI gateway adds
“AI gateway” is a product-category label, and implementations differ. Capabilities that vendors commonly highlight include:
- Routing across model providers and request formats
- Request or prompt inspection and transformation
- Caching and rate controls
- Usage, token, latency and cost observability
- Safety and data-leak guardrails
- Failover and load balancing between models
Availability varies by product, so confirm each item against current documentation.
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Can an API gateway handle AI traffic?
Yes, in the narrow sense that it can forward HTTP requests to an LLM endpoint. What you do not get automatically is prompt-aware or model-specific behavior. Kong’s AI Gateway documentation makes this distinction, stating: “If you just add an LLM’s API behind Kong Gateway, you can only interact at the API level with internal traffic.” That is a vendor statement rather than a neutral standard, but it captures the practical gap: a plain proxy sees requests and responses as opaque traffic, while an AI layer can read prompts, count tokens and apply policies to them.
Side-by-side comparison
| Axis | API gateway question | AI gateway question |
|---|---|---|
| Traffic and routing | Which API protocols and backend services can it expose? | Which model providers and formats can it route across? |
| Policy depth | Can it apply authentication, rate limits and ordinary request policies? | Can it inspect or transform prompts and responses, apply guardrails, enforce model-specific controls? |
| Operations | What API traffic metrics and logs are available? | Can teams track model and token usage, latency and cost, and use caching or failover? |
| Security and credentials | How are clients authenticated and APIs protected? | How are provider credentials stored, injected, scoped and rotated? What data controls apply to prompts? |
| Deployment and billing | Where does the gateway run and who operates it? | Where does AI traffic flow, which providers are supported, and how are model and gateway charges handled? |
These are evaluation questions, not claims that every product supports every feature.
Two vendor examples
Kong: AI features on top of an API gateway
Kong’s current documentation lists AI Gateway functions including centralized provider credentials, model access and token budgets, prompt templates, metering and billing, semantic cache, prompt compression, guardrails, data sanitization, failover and load balancing, and token, latency and cost observability. Its getting-started guide sits within the same product family, which shows that “AI gateway versus API gateway” is not always a choice between two separate infrastructure layers. In Kong’s Konnect-managed setup, data planes run in your own environment (self-hosted, cloud or Kubernetes) and connect to Konnect for configuration and observability. Do not assume other vendors host things the same way.
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Cloudflare: a unified REST API for models
Cloudflare’s AI Gateway REST API documentation (last updated September 17, 2026) describes calling Cloudflare-hosted or third-party models through the same Cloudflare API, with logging, caching and rate limiting applied. It lists endpoints for several formats, including /ai/run, OpenAI-compatible chat completions, the Responses API and Anthropic-schema messages, with support depending on the model. Authentication for /accounts/{account_id}/ai/* requires an account API token with the relevant Workers AI permission. That requirement belongs to this endpoint; do not generalize it to other gateways.
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How to choose
- List your traffic. If you mainly expose conventional REST or WebSocket APIs, an API gateway covers the job. If you call several model providers, model-aware routing becomes relevant.
- Check provider and format coverage. Confirm the providers and request schemas you use are supported, including per-model exceptions.
- Test policy depth. Decide whether you need prompt inspection, guardrails or data sanitization, or only authentication and rate limits.
- Review credential handling. Establish where provider keys live and how they are scoped and rotated.
- Look at observability and cost controls. Token-level usage and budgets matter only if you need to attribute or cap spend.
- Confirm deployment and billing. Know where prompts travel and what the gateway itself charges, separate from model fees.
Cautions
- Caching prompts is not always safe. Cached content may include sensitive data, so check how and where it is stored.
- A gateway’s presence alone does not guarantee privacy, compliance, reliability or lower costs.
- The product descriptions above are vendor documentation, not independent tests, and no comparative benchmarks are cited here. Provider lists, endpoints, permissions and pricing change, so verify against current docs.
The Bottom Line
Pick by capability, not label. If you only need to publish and protect APIs, a conventional API gateway is enough. If you need to govern prompts, providers, tokens and model spend, look for AI-specific functions, either in a dedicated AI gateway or as an extension of the API gateway you already run.
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