Startups building AI products today are under pressure to move fast, ship with fewer resources, and deliver high-performance user experiences—often with unpredictable traffic patterns and tight budgets. But traditional cloud providers like AWS, GCP, and Azure were never designed with these constraints in mind. Their pricing models, infrastructure complexity, and DevOps overhead create friction that slows teams down and drives costs up.

Cerebrium is a serverless AI infrastructure platform that allows engineering teams to build and scale data/AI workloads without the infrastructure headache. It allows teams to run high performing, low latency with minimal setup while only paying for the time they use compute resources.

Here’s how Cerebrium can help your startup stay fast, lean, and scalable.

• Only Pay for the Resources You Use: You’re billed only while your code is running and only for the exact CPU, memory fraction, or GPU you consume. For example, you can use 2 CPUs and 2 GB of an H100 and pay just for that slice.

• Infrastructure That Scales to Zero: With 2-4 cold starts, your workloads spin up only when requests arrive and shut down immediately after (configurable by you). No more idle instances or wasted buffers—just on-demand performance. This can scale to hundreds of GPUs within this SLA. This leads to high compute utilization.

• Zero DevOps & Maintenance Overhead: Forget managing Kubernetes clusters, CI/CD pipelines, load balancers etc —Cerebrium handles deployment, autoscaling, monitoring, and routing out of the box. Your team can focus entirely on building features, not infrastructure.

• Limit GPU Supply: GPUs are in limited supply worldwide, so if you suddenly go viral, you may not be able to support the growing demand. Cerebrium is built across a variety of clouds across a variety of regions in order to access a variety of supply.

• No Capacity Reservations Needed: Tap into high-end GPUs (H200,H100, A100s) on demand without committing to expensive reservations that sit idle.

• Batching for Maximum Efficiency: Group multiple inference requests together on larger CPUs/GPUs to boost throughput and lower cost per request. Intelligent batching means fewer wasted cycles and better overall utilization.

• Global Deployments Without the Global Cost: Model instances only run in the region where traffic originates, eliminating idle resources across other zones. Enjoy low-latency, on-demand inference anywhere in the world—without duplicating infrastructure.

As your usage grows, you can unlock even deeper cost savings through short or long-term pricing commitments starting at a minimum of 3 months.

Startups shouldn’t have to compromise between cutting costs and delivering a world-class AI experience. Cerebrium removes that trade-off—giving your team instant access to powerful GPUs, global deployment, built-in autoscaling, and transparent usage-based pricing. No idle instances, no infrastructure headaches, and no wasted engineering time.

Give Cerebrium a try today and get $30 of free credits in order to test your use case