Serverless AI

Serverless AI is a deployment model where AI inference runs on cloud infrastructure that you do not manage. You send requests and receive responses without provisioning, configuring, or maintaining any servers. The cloud provider handles scaling, and you pay only for the compute you actually consume.

How serverless AI differs from traditional deployment

In traditional AI deployment, you provision GPU servers, install dependencies, load models, configure networking, and manage scaling. You pay for those servers whether they are processing requests or sitting idle.

In serverless AI, you interact through an API. The provider handles everything behind the scenes: allocating GPU resources when a request arrives, processing it, returning the result, and releasing the resources. You pay per request or per token.

The appeal

• No infrastructure management: No servers to patch, monitor, or scale.
• Pay-per-use: Zero cost when there are no requests. Perfect for variable or unpredictable workloads.
• Automatic scaling: Handles spikes in demand without manual intervention.
• Fast time to market: Start using AI in minutes rather than spending weeks on infrastructure.

Serverless AI options

• AI API providers (OpenAI, Anthropic, Google): The purest form of serverless AI. You call an API and get a response.
• Serverless GPU platforms (Modal, Banana, Replicate): You deploy your own model, but the platform manages the infrastructure and scales to zero when idle.
• Cloud functions with AI (AWS Lambda, Google Cloud Functions): Run lightweight AI tasks in serverless compute functions.
• Managed inference endpoints (Hugging Face, AWS SageMaker): Deploy models with minimal configuration and automatic scaling.

When serverless works well

• Prototyping and early-stage products where usage is low and unpredictable.
• Applications with bursty traffic patterns — high demand sometimes, low demand others.
• Small teams without dedicated infrastructure engineers.
• Use cases where speed of deployment matters more than per-unit cost optimisation.

When serverless falls short

• High-volume, steady workloads: When you are processing requests constantly, reserved GPU instances are cheaper than per-request pricing.
• Low latency requirements: Serverless functions may have cold-start delays when scaling from zero.
• Custom model requirements: If you need full control over model configuration and optimisation.
• Data residency: When data must stay within specific geographic regions or on-premises.