How Does Inference-as-a-Service Work and Which Providers Offer It?

GMI Cloud Blog | AI Infrastructure Guide | gmicloud.ai

Inference-as-a-service (IaaS) is a cloud delivery model where you run AI models on a provider's infrastructure instead of managing your own GPUs, engines, and serving stack. You send a request through an API, the provider's optimized infrastructure handles the computation, and you get a result back.

You pay per request or per GPU-hour, with no upfront hardware investment.

For enterprise technical leads evaluating build-vs-buy decisions and researchers seeking scalable compute, understanding how IaaS works and what differentiates providers is essential.

Providers like GMI Cloud offer both API-based inference through a 100+ model library and dedicated GPU instances.

This guide covers the architecture, delivery modes, provider landscape, and evaluation criteria. We focus on GPU-based inference services; edge inference platforms are outside scope.

How Inference-as-a-Service Works Under the Hood

IaaS runs on a three-layer architecture. As a user, you only interact with the top layer. The provider manages the rest.

Layer 1: Request Layer (User-Facing)

This is what you see: an API endpoint. You send a request (text prompt, image, audio), the system authenticates your credentials, meters usage for billing, and routes your request to the right model. Rate limiting and content validation happen here.

Layer 2: Engine Layer (Provider-Managed)

The inference engine (TensorRT-LLM, vLLM, or similar) receives your request, loads the target model, manages GPU memory allocation, applies optimizations (FP8 quantization, continuous batching), and executes the forward pass. Model management software handles version control and multi-model routing.

Layer 3: Hardware Layer (Provider-Managed)

GPU clusters with high-bandwidth memory (H100/H200 with HBM3/HBM3e), NVLink for inter-GPU communication, and InfiniBand for inter-node traffic. Pre-configured software stacks (CUDA, cuDNN, NCCL) eliminate setup overhead.

The user sends a request to Layer 1 and receives a response. Layers 2 and 3 are invisible. This architecture delivers two distinct deployment modes.

Two Deployment Modes

IaaS providers typically offer two ways to consume inference. The right choice depends on your control requirements and workload characteristics.

Serverless / API Mode

You call a pre-deployed model through an API. No GPU provisioning, no engine configuration, no infrastructure management. You pay per request. The provider handles scaling, batching, and optimization automatically.

Best for: Prototyping, variable-traffic workloads, teams without GPU infrastructure expertise, and multi-model evaluation. Most model library services operate in this mode.

Dedicated Instance Mode

You rent GPU instances by the hour and deploy your own models with your own engine configuration. You control precision, batching strategy, and framework choice. The provider supplies the hardware with a pre-configured software stack.

Best for: Custom models, strict latency SLAs, high-volume workloads where per-request pricing exceeds hourly GPU rental, and regulated environments requiring full infrastructure control.

Many teams start with serverless mode for validation, then migrate to dedicated instances when workloads stabilize. The mode you choose depends partly on the type of provider.

The Provider Landscape

Inference-as-a-service providers fall into three categories. Each has different strengths, trade-offs, and pricing models.

Hyperscalers

Major cloud platforms (AWS, Google Cloud, Azure) offer inference services as part of their broader cloud ecosystem. They provide the widest range of adjacent services (storage, networking, databases) and global reach.

Trade-offs: GPU availability can be constrained during high-demand periods. Pricing tends to be higher than specialists. Inference-specific optimizations may lag behind dedicated providers.

GPU Cloud Specialists

Providers focused specifically on GPU infrastructure for AI workloads. They typically offer competitive pricing, direct supply chain relationships for GPU availability, and pre-optimized inference stacks. Some hold strategic partnerships with NVIDIA, providing early access to latest-generation hardware.

Trade-offs: Narrower service scope (GPU compute, not full cloud ecosystem). Fewer adjacent services compared to hyperscalers.

Model API Platforms

Providers that offer API access to specific model families without exposing the underlying GPU infrastructure. You call a model; the provider handles everything. Pricing is purely per-request.

Trade-offs: No option to deploy custom models. Less control over precision, batching, and serving configuration. You're locked into the provider's model selection.

Beyond provider type, here's what to evaluate when selecting an IaaS provider.

Evaluation Criteria

Six factors differentiate IaaS providers beyond marketing claims.

Performance. Request latency (time-to-first-token for LLMs, generation time for image/video) and throughput (requests per second at target latency). Ask for benchmarks on your specific model and workload pattern.

Model coverage. How many models are available, and do they cover your task types (LLM, image, video, TTS, voice)? A library with 100+ models across categories provides more flexibility than one focused on a single model family.

Pricing model. Per-request pricing works for variable traffic. Per-GPU-hour works for steady, high-volume workloads. Compare total monthly cost, not just unit price.

Data sovereignty. Can the provider deploy in your required geographic region? Does data stay within that region during the entire inference pipeline (input, processing, output, logs)?

Supply stability. Can the provider deliver GPU capacity when you need it? Providers with direct supply chain relationships and pre-provisioned inventory handle demand spikes better.

Software stack. Is the inference engine pre-optimized? Are FP8 quantization, continuous batching, and monitoring included out of the box?

With the right provider, here's what IaaS looks like across real AI tasks.

IaaS in Practice: Models by Scenario

The serverless API mode is the fastest way to experience inference-as-a-service. Here are models across common tasks.

For image generation, seedream-5.0-lite ($0.035/request) delivers strong quality with efficient pricing. For image editing, reve-edit-fast-20251030 ($0.007/request) provides fast results. For exploration, bria-fibo-relight ($0.000001/request) offers a low-cost entry point.

For video, pixverse-v5.6-t2v ($0.03/request) handles text-to-video efficiently. Kling-Image2Video-V1.6-Pro ($0.098/request) delivers higher fidelity. Sora-2-Pro ($0.50/request) provides maximum quality for research.

For TTS, minimax-tts-speech-2.6-turbo ($0.06/request) is reliable. elevenlabs-tts-v3 ($0.10/request) delivers broadcast-quality output.

The question is whether IaaS is right for your situation.

IaaS vs. Self-Hosted: Decision Framework

Factor (IaaS (Serverless/API) / IaaS (Dedicated Instance) / Fully Self-Hosted)

• Setup time - IaaS (Serverless/API): Minutes - IaaS (Dedicated Instance): Hours - Fully Self-Hosted: Days to weeks
• Infrastructure expertise - IaaS (Serverless/API): None required - IaaS (Dedicated Instance): Some required - Fully Self-Hosted: Deep expertise needed
• Model flexibility - IaaS (Serverless/API): Provider's library - IaaS (Dedicated Instance): Any model - Fully Self-Hosted: Any model
• Cost model - IaaS (Serverless/API): Per request - IaaS (Dedicated Instance): Per GPU-hour - Fully Self-Hosted: Capex + operating
• Latency control - IaaS (Serverless/API): Provider-managed - IaaS (Dedicated Instance): You configure - Fully Self-Hosted: Full control
• Data control - IaaS (Serverless/API): Provider-dependent - IaaS (Dedicated Instance): High - Fully Self-Hosted: Complete
• Scaling - IaaS (Serverless/API): Automatic - IaaS (Dedicated Instance): Manual or scripted - Fully Self-Hosted: Manual
• Best for - IaaS (Serverless/API): Prototyping, variable traffic - IaaS (Dedicated Instance): Stable production - Fully Self-Hosted: Regulated, high-volume

Getting Started

Start by classifying your workload. If you're evaluating models or building a proof of concept, serverless API mode gets you running in minutes. If you need custom models or guaranteed latency, dedicated GPU instances give you full control.

Cloud platforms like GMI Cloud support both modes: a model library for serverless API inference, and GPU instances (H100 ~$2.10/GPU-hour, H200 ~$2.50/GPU-hour; check gmicloud.ai/pricing for current rates) for dedicated deployments.

Evaluate against the six criteria above and start with the mode that matches your current stage.

FAQ

When does serverless API mode become more expensive than dedicated GPUs?

Typically when your request volume exceeds ~10,000 requests per day for sustained periods. At that point, per-GPU-hour pricing with optimized batching usually beats per-request API pricing.

Can I switch from serverless to dedicated instances later?

Yes. Most providers support both modes. Start with serverless for validation, estimate your steady-state request volume, and migrate to dedicated instances when per-request costs exceed hourly GPU rental.

How do I evaluate a provider's inference performance?

Request benchmarks on your specific model with your typical input/output sizes. Generic benchmarks (like "X tokens per second") can be misleading because performance varies significantly by model, precision, batch size, and sequence length.

Does IaaS work for regulated industries?

Yes, but you need to verify the provider's data residency options, encryption policies, and compliance certifications. Dedicated instance mode with regional deployment typically meets most regulatory requirements. Serverless mode requires more scrutiny of the provider's data handling policies.

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