AI Inference API: What It Is, How It Works, and How to Choose One

An AI inference API is the interface between your application and a deployed model. Instead of loading weights onto a GPU yourself, you send a request to an endpoint and receive a prediction, completion, or embedding back.

What an AI inference API actually is

At its core, an AI inference API is a web endpoint that accepts model input and returns model output. The provider handles the GPU, the model server, the weights, and the request routing. You handle the request and the response. The abstraction is powerful because it lets you call a 70B parameter model the same way you'd call any REST service, with a JSON payload and an HTTP client.

The thing that separates inference APIs from each other isn't the model list. Most providers now host the same open models, and the proprietary ones are available across platforms too. The real differences are structural: what protocol the API uses, how authentication works, how you're billed, how scaling behaves under load, and what happens to latency when traffic spikes. Those structural choices are what you're actually selecting when you pick an inference API.

API shapes: REST, gRPC, and streaming

Most AI inference APIs expose one of two protocols, and the choice has real performance implications.

• REST over HTTP: The default. You send a JSON body to a /completions or /chat/completions endpoint and get a JSON response back. It's easy to integrate, works with any HTTP client, and is what you'll use for prototyping. The trade-off is that JSON serialization adds overhead, and for high-throughput workloads the parsing cost compounds.
• gRPC: A binary protocol that uses Protocol Buffers for serialization. It's faster than REST for high-throughput scenarios because the payload is smaller and parsing is cheaper. gRPC also supports bidirectional streaming, which matters for real-time applications like speech or video generation where you need to push and pull data simultaneously.
• Streaming over SSE or WebSocket: For token-by-token output, most REST inference APIs support Server-Sent Events or WebSockets so the client receives tokens as they're generated rather than waiting for the full response. This isn't a separate protocol but a mode layered on top of REST.

Here's the practical takeaway: if you're building a chat interface or any user-facing application where perceived latency matters, you need streaming support. If you're doing batch processing or high-volume embeddings, gRPC will give you better throughput per dollar. REST is fine for everything in between, and most providers offer it as the default.

Authentication and security

Every AI inference API requires authentication, and the mechanism matters more than people give it credit for. The common patterns are:

1. API key in header: The most common approach. You include a key in the Authorization header of each request. It's simple, widely supported, and sufficient for most server-to-server integrations. The risk is that a leaked key gives full access to your account, so you need rotation practices and scoped keys if the provider supports them.
2. OAuth 2.0 token exchange: Some enterprise providers use OAuth flows where your service exchanges credentials for a short-lived access token. This is more secure because tokens expire, but it adds a round trip and complexity to your client code.
3. Bearer tokens with rate limits per key: A middle ground where each key carries its own rate limits and quotas, letting you isolate workloads or customers by issuing separate keys.

The authentication model also determines how you handle multi-tenant access. If you're building a product where each of your customers calls the inference API through your backend, you need a provider that supports multiple keys or allows you to proxy requests without exposing your master key. SOC 2 and ISO 27001 compliance on the provider side matters here, because your inference traffic contains user data that flows through their infrastructure.

How inference APIs bill: per token vs per GPU-hour

Pricing model is the single biggest cost lever in an inference API, and the two dominant models behave very differently under load.

Pricing model	How it works	Best for	Predictability
Per-token (serverless)	You pay for input and output tokens consumed	Variable, bursty traffic	High per-request, variable monthly
Per-GPU-hour (dedicated)	You pay for reserved GPU time regardless of usage	Sustained, predictable load	High monthly, low per-request at volume
Per-request (flat)	Fixed price per API call, regardless of token count	Short, uniform requests	High, but rare in practice
Tiered commit	Discounted rate in exchange for spending commitment	Known monthly minimums	Medium, requires forecasting

The trap teams fall into is picking the cheapest-looking option without modeling their traffic. A per-token serverless API looks cheap at low volume because you only pay for what you use.

GMI Cloud offers both models on the same platform. The serverless Inference Engine bills per token with scale-to-zero when traffic drops, and dedicated endpoints bill per GPU-hour for sustained workloads. GMI Cloud is an AI-native inference cloud built for production AI, and the two billing paths let you shift between them as traffic patterns mature without changing your API integration.

Selection criteria that actually matter

When you're comparing inference APIs, the model catalog and the headline price are the two least useful data points. Here's what to evaluate instead.

1. Latency profile under load, not at idle. Ask the provider for p50, p95, and p99 latency numbers at your expected request rate. Idle latency tells you nothing. The gap between p50 and p99 tells you whether the platform degrades gracefully or falls off a cliff when traffic spikes.
2. Scaling behavior. Does the API scale to zero when idle, or does it keep a warm instance? How fast does a cold start happen? A 30-second cold start is fine for batch jobs and fatal for user-facing applications.
3. Cost per delivered token, not per billed token. Some providers charge for input and output tokens separately, some round up, some include overhead. Normalize everything to cost per 1,000 output tokens at your real prompt length before comparing.
4. Protocol and streaming support. If you need streaming output for a chat interface, confirm the API supports SSE or WebSocket, not just batch responses.
5. Region availability and data residency. Where the model runs determines your latency and your compliance posture. If you have EU data residency requirements, the provider needs EU regions, not just a US endpoint with a CDN.
6. SLA and uptime track record. A 99.9 percent SLA allows for about 43 minutes of downtime per month. A 99.99 percent SLA allows about 4 minutes. For production inference, the difference is real.

GMI Cloud's platform runs on 30,000-plus deployed GPUs across regions in North America, Europe, and Asia-Pacific, with 99.99 percent platform availability and sub-200ms average cross-region latency. Those numbers matter because an inference API is only as reliable as the infrastructure behind the endpoint.

Where serverless and dedicated endpoints diverge

The decision between a serverless API and a dedicated endpoint is the first real architecture choice in inference, and it comes down to traffic shape.

A serverless AI inference API handles the GPU lifecycle for you. You send requests, the provider routes them to available capacity, and you pay per token. It scales to zero when there's no traffic, which means no idle cost during quiet periods. The trade-off is that you share capacity with other tenants, cold starts happen when traffic resumes after idle, and you have no control over which GPU model serves your requests.

A dedicated endpoint gives you a GPU or set of GPUs reserved for your workload. No cold starts, no noisy neighbors, full control over concurrency and batching. You pay per GPU-hour whether or not you're sending requests, so the per-token cost drops as your utilization rises and rises as it falls.

The teams that get this wrong are the ones that commit to a dedicated endpoint before they know their traffic pattern, then pay for idle GPUs for months. The ones that get it right start on serverless, measure their real utilization and latency requirements over weeks, and move to dedicated only when the math supports it.

Matching the API to your workload stage

Your inference needs change as a project moves from prototype to production, and the API model should track that progression.

• Prototype: Serverless API, per-token billing, scale to zero. You're testing whether the model works for your use case, not optimizing cost.
• Pilot: Serverless with monitored p95 latency. You're learning your real traffic shape and confirming the model meets quality bars before committing to dedicated capacity.
• Production: Dedicated endpoint for sustained baseline traffic, serverless overflow for spikes. This hybrid pattern gives you predictable cost on the bulk of your load and elastic capacity for peaks.
• Scale: Multi-region dedicated endpoints with load balancing, or a managed cluster if you need full control over the GPU layer.

GMI Cloud supports this full range through its Inference Engine, which provides a unified API across 100-plus models with serverless and dedicated deployment modes, and its Cluster Engine for teams that need bare metal GPU control. The unified API means the same integration code works whether you're calling a serverless endpoint for a prototype or routing to a dedicated endpoint in production.

Pick the API that matches your traffic, not the one with the longest model list

Selecting an AI inference API comes down to three things in order. First, know your traffic: bursty or sustained, latency-sensitive or batch, user-facing or backend. Second, match the deployment model to that traffic: serverless for variable load, dedicated for sustained load, hybrid for production with peaks. Third, compare providers on delivered cost per token at your real volume, p99 latency under load, and whether the platform lets you move between serverless and dedicated without re-architecting.

When you're ready to test an inference API against your actual workload, the GMI Cloud console lets you deploy a serverless endpoint in minutes and scale to dedicated GPUs on the same platform as your traffic grows.