API Gateway Comparison: What Matters for AI Workloads

AI traffic breaks assumptions most gateways were designed around. This guide covers what to verify, drawing on FISTA Solutions' web and mobile and AI enablement infrastructure work.

What should be checked for AI traffic?

Six things beyond conventional gateway criteria.

Concern	What to verify	Why it matters
Streaming	Passes through without buffering	Perceived latency
Timeouts	Configurable to minutes	Long generations
Rate limits	Per consumer and route	Protects other traffic
Connection handling	Many long-lived connections	Resource limits
Observability	Token-aware, not request counts	Cost visibility
Payload limits	Large prompts and responses	Request rejection

Why verify streaming first?

Because a gateway that buffers silently destroys the streaming experience.

Streamed responses must pass through chunk by chunk. A gateway collecting the full response before forwarding produces a client that waits for everything and then receives it at once.

This fails quietly: the request succeeds and the experience is worse. Test it explicitly by timing the first chunk through the gateway. See streaming UI patterns for AI apps.

What timeout configuration is needed?

Per-route timeouts measured in minutes for AI paths.

Default gateway timeouts are typically tens of seconds, which is fine for conventional APIs and wrong for long generations or agent tasks.

Check whether timeouts are configurable per route, so AI paths get longer limits without loosening them everywhere. Also check idle timeouts, which can terminate a stream during a slow generation.

How should rate limiting work?

Per consumer and per route, with token awareness where possible.

A global limit protects your infrastructure and does nothing about one application consuming everything. Per-consumer limits keep a misbehaving client from degrading the rest.

Token-aware limiting is better still, since request counts poorly represent load for AI traffic. Few gateways support it natively, but the limit can be applied in your own layer. See rate limiting for LLM APIs.

What about connection handling?

Many long-lived connections is a different profile from many short ones.

Streaming AI responses means connections held open for the duration of generation. At concurrency, that is a large number of simultaneous open connections.

Check connection limits and resource consumption under that pattern rather than under a conventional load test. Some gateways handle it well and some do not.

What observability is needed?

Token-aware, correlated with your application traces.

Request counts and latencies are necessary and insufficient. For AI routes you want token volume, cost, and the ability to correlate a gateway log with the application trace and the model call.

Check whether correlation identifiers propagate, and whether the gateway can emit structured events into your existing stack. See observability tools for AI comparison.

Do conventional criteria still apply?

Entirely. Authentication, authorisation, routing, availability, and operational maturity all matter as they always did.

The AI-specific concerns are additional checks, not replacements. A gateway that streams beautifully and cannot do authentication properly is not a candidate.

Assess it as a gateway first, then apply the AI-specific tests.

How do you run your own comparison?

Put a real streaming AI endpoint behind each candidate and measure time to first chunk through the gateway against direct. Any meaningful difference indicates buffering.

Then run a long request to confirm the timeout, and a concurrency test with held connections to check resource behaviour.

What does switching cost later?

Moderate. Gateway configuration is usually portable in concept and not in format, so a migration means re-expressing routes, limits, and policies.

Keeping configuration in version control and as declarative as possible makes that translation easier.

What do people get wrong here?

Buffering discovered after launch. Default timeouts left in place. Global rather than per-consumer limits. Observability by request count. And load testing with conventional short requests.

Is this the same as an LLM gateway?

No — they sit at different layers. An API gateway handles your clients calling your services; an LLM gateway handles your services calling model providers.

Many organisations need both, and confusing them produces a design where neither job is done properly. See LLM gateway comparison.

Which should you choose?

Verify streaming pass-through, per-route timeouts, and per-consumer limits before assessing anything else. Then apply conventional gateway criteria, which still decide most of the choice.

What should you do first?

Time the first chunk of a streamed response through your current gateway against calling the service directly. A large difference means buffering.

How FISTA Solutions helps

FISTA Solutions builds and operates production AI systems through AI agents, AI enablement, and forward deployed engineering: gateways verified for streaming pass-through and per-route timeouts before conventional criteria, with token-aware observability rather than request counts, decisions documented with their reasoning, and handover that leaves your team able to maintain what was delivered. The record is 150+ projects for 50+ companies across 12+ countries.

To run this comparison against your own workload, message FISTA on WhatsApp, or read LLM gateway comparison.