Serverless vs Dedicated Inference: What’s Right for Your AI Product?

Building an AI product today is not just about choosing the right model. It is now more about choosing the right way to run that model in production. Do you need something quick, lightweight and pay-as-you-go? Or do you need more power, performance guarantees and full control over your infrastructure? 

That’s when you start to decide whether to choose Serverless or Dedicated Inference. Picking the right one can make or break your AI experience, especially when you move from prototypes to production-scale deployments.

Let’s explore both approaches and see which one’s right for your AI product.

Serverless vs Dedicated Inference: Quick Comparison

What is Serverless Inference?

Serverless inference is a method of deploying machine learning models, where the platform automatically handles the infrastructure. You don’t spin up servers, you don’t configure GPUs and you don’t have to worry about scaling.

Instead, you focus on your model. The platform takes care of the rest. So, it means:

• No provisioning or scaling of GPU resources
• No need to manage inference endpoints
• You pay only for what you use
• The platform handles everything else

What is Dedicated Inference?

Dedicated inference refers to using reserved or specialised infrastructure and resources to run the intensive process of making predictions from a trained model at scale. So, it means:

• Reserved infrastructure allocated only to your workloads
• Higher and more consistent and reliable performance
• Production-grade isolation in dedicated environments
• Ability to handle larger workloads and longer request times
• Reduced operational burden of managing hardware

When to Choose Serverless Inference?

Serverless inference is ideal for:

• Ad-hoc or unpredictable workloads: If your traffic is bursty like sometimes zero or sometimes spiking, serverless inference saves you from paying for idle GPUs.
• Early-stage testing: When you’re experimenting with different models or features, you don’t want to burn the budget on unused infrastructure.
• Non-performance-critical workloads: For workloads where ultra-low latency or strict SLAs are not mandatory, serverless inference is cost-efficient.

When to Choose Dedicated Inference?

Dedicated inference is perfect for:

• Production rollouts at scale: If your customers demand consistent and low-latency responses, dedicated servers deliver.
• Customer SLAs: You cannot afford jitter when an enterprise customer requires a specific response time guarantee.
• High-volume, performance-heavy workloads: Running a large LLM with constant queries? Dedicated and powerful GPUs like the NVIDIA H100 SXM, NVIDIA H100 PCIe and NVIDIA A100 ensure no downtime or delays.
• Data isolation and compliance: Critical when you need private infrastructure for sensitive workloads.

Serverless vs Dedicated Inference: What’s Right for Your AI Product?

Now comes the real question: which is right for you? The answer depends on where you are in your AI journey.

1. Start Small with Serverless

When you’re testing new models, experimenting with features or serving lightweight workloads, serverless inference is your best friend. So, you can:

• Leverage an endpoint in seconds.
• Pay only when you call it.
• No wasted cost during downtime.

You can think of it as a trial mode for infrastructure, great for quick proofs of concept.

2. Scale Confidently with Dedicated

Once your AI product moves into production with growing traffic and customer commitments, it is time you switch gears. Dedicated inference gives you:

• Performance guarantees to meet your SLAs.
• Control to choose GPU type, environment and scaling rules.
• Security with fully private, isolated servers.

Why Not Both?

Here’s the beauty: you don’t have to choose one forever. Many teams start with serverless inference to validate use cases, then transition to dedicated inference for stable rollouts. This hybrid approach keeps costs under control while ensuring you’re ready for scale.

For example:

• Phase 1: Prototype your chatbot on serverless inference, pay while you test with beta users.
• Phase 2: Once your product goes live with thousands of daily queries, move to dedicated GPUs for reliable and predictable performance.

Running Inference on AI Studio

AI Studio is a full-stack Gen AI platform built on Hyperstack’s high-performance infrastructure. Instead of going through multiple tools and managing them for building a market-ready AI product, you get everything in one place. Go from raw dataset and LLM evaluation to inference and deployment, much faster.

Deploy Models Your Way

You can run inference on the Hyperstack AI Studio based on your project needs:

Serverless Inference

This option is ideal for ad-hoc testing, unpredictable traffic and cost savings. You can also run inference on OpenAI’s latest open-source gpt-oss-120b model directly in AI Studio, making it easier than ever to test and deploy without additional setup. Even better, gpt-oss-120b is among the most cost-effective options for inference on AI Studio.

Check out the pricing below:

Dedicated Inference (Coming Soon)

With dedicated inference on AI Studio, you’ll be able to configure your own dedicated servers with options including NVIDIA H100 and NVIDIA A100 GPUs. This setup allows you to run workloads in fully isolated, private environments with no shared compute and vendor lock-in.

If you’re in the early stages of experimenting with your AI product, our serverless inference is a perfect way to get started quickly with a budget. And if you’re looking for more control and guaranteed performance, stay tuned as we’ll be launching dedicated inference very soon. 

Subscribe to our product newsletter below to be the first to know when it goes live.

FAQs

What is serverless inference?

Serverless inference is a deployment method where the platform automatically manages the infrastructure needed to serve predictions from your model. You don’t have to set up servers or GPUs and you only pay when your model is running, making it ideal for testing and unpredictable workloads.

What is dedicated inference in AI?

Dedicated inference uses reserved or specialised infrastructure, such as NVIDIA H100 or A100 GPUs to run AI models. Unlike shared environments, these resources are allocated exclusively to your workloads, offering higher performance, isolation and reliability for production-scale AI applications.

When should I choose serverless inference?

Serverless inference is best when you are in the early stages of development, testing new models or dealing with ad-hoc and unpredictable workloads. It’s cost-efficient and allows you to experiment without committing to permanent infrastructure.

When should I choose dedicated inference?

Dedicated inference is ideal for production environments where you need guaranteed performance, strict SLAs and the ability to handle high-volume or latency-sensitive workloads. It’s also the right choice if your use case demands data isolation or compliance.

What is the main difference between serverless and dedicated inference?

The main difference lies in infrastructure and performance. Serverless inference is automatically managed, scales on demand, and is cost-efficient for smaller workloads, while dedicated inference uses private infrastructure for consistent performance, reliability and large-scale deployments.