Serverless vs. Dedicated AI Inference: Choosing the Right Friendli Endpoint for Your Workload

When you need to run model inference for your application, a common question comes up: How should we deploy it?

Should we use a fully managed, shared setup or reserve a fully managed deployment with dedicated GPUs? The answer depends on latency tolerance, traffic predictability, and how much control and model coverage you need.

At FriendliAI, these two approaches are offered as Serverless API (Serverless Endpoints) and Dedicated Endpoints, each designed to support different AI inference requirements. Both are fully managed by FriendliAI, so teams don’t need to operate GPU infrastructure themselves. Serverless runs on shared GPUs for fast, elastic access, while Dedicated reserves GPUs exclusively for your workloads to provide stronger guarantees and broader deployment flexibility.

This guide walks through how they differ and how to choose the right one for your workload today, with a clear path to scale tomorrow.

Core Difference: Shared vs. Reserved GPUs

At the foundation, the difference between Serverless and Dedicated Endpoints comes down to how GPU resources are allocated.

This architectural distinction directly affects performance predictability, cost efficiency, SLAs, and model coverage.

Performance Predictability & SLAs

Different applications have different tolerance levels for latency variability.

If your application requires strict performance guarantees or supports mission-critical user experiences, Dedicated Endpoints are the right fit.

Cost Efficiency at Different Scales

Cost efficiency depends more on traffic patterns and workload characteristics.

In practice, many teams use Serverless early on and transition to Dedicated as workloads become steady and predictable.

Model Coverage & Deployment Flexibility

Beyond performance and cost, endpoint choice also affects which models you can run and how much control you have over them.

For teams that need ownership over their models or broad coverage, Dedicated Endpoints is essential.

When to Use Which

Use Serverless Endpoints if you are:

• Exploring or evaluating popular open-source models
• Running low, bursty, or unpredictable traffic
• Serving production workloads with relaxed latency constraints
• Prioritizing speed of iteration and simplicity

Use Dedicated Endpoints if you are:

• Serving workloads that require SLAs
• Operating at scale with steady, high traffic
• Deploying fine-tuned, private, or proprietary models
• Requiring broader model coverage and deeper control

A Natural Migration Path

Many teams start with Serverless Endpoints to move quickly, then transition to Dedicated Endpoints as requirements evolve.

With Friendli Inference, this transition is seamless. You don’t need to switch platforms, rewrite integrations, or adopt a new serving stack—just switch the deployment model as your workload grows.

Choosing the Right Endpoint, Today and Tomorrow

Serverless and Dedicated Endpoints are not competing options. They are complementary tools designed for different inference needs.

The right choice depends on your latency sensitivity, traffic patterns, performance requirements, and model coverage needs. Whether you’re serving lightweight inference or operating at scale, Friendli Inference lets you start fast and scale confidently without locking you into a single deployment model.

Get started with Serverless Endpoints in minutes, or choose Dedicated Endpoints when your workload calls for predictable performance, deeper control, and expanded model coverage.