Launch HN: Cerebrium (YC W22) – Serverless Infrastructure Platform for ML/AI

We are Michael & Jono, and we are building Cerebrium (https://www.cerebrium.ai), a serverless infrastructure platform for ML/AI applications - we make it easy for engineers to build, deploy and scale AI applications.

Initially, we’ve been hyper-focused on the inference side of applications, but we’re working on expanding our functionality to support training and data processing use cases—eventually covering the full AI development lifecycle.

You can watch a quick loom video of us deploying: https://www.loom.com/share/06947794b3bf4bb1bb21c87066dfcc66?...

How we got here:

Jono and I led the technical team at our previous e-commerce startup, which grew rapidly over a few years. As we scaled, we were tasked with building out ML applications to make the business more efficient. It was tough—every day felt like a defeat. We found ourselves stitching together AWS Lambda, Sagemaker, and Prefect jobs (this stack alone was enough to make me want to give up). By the time we reached production, the costs were too high to maintain. Getting these applications live required a significant upfront investment of both time and money, making it inaccessible for most startups and scale-ups to attempt. We wanted to create something that would help us (and others like us) implement ML/AI applications easily and cost-effectively.

The problem:

There are a ton of challenges to tackle to realize our vision, but we’ve initially focused on a few key ones:

1. GPUs are expensive – An A100 is 326 times the cost of a CPU, and companies are using LLMs like they’re simple APIs. Serverless instances solve this to an extent, but minimizing cold starts is difficult.

2. Local development – Engineers need local development environments to iterate quickly, but production-grade GPUs aren’t available on consumer hardware. How can we make cloud deployments feel as fast as just saving a file locally and retrying?

3. Cost to experiment - To run experiments we had to spin up EC2 instances each day, recreate our environment and run scripts. It was difficult to monitor logs, instance usage metrics as well as run large processing jobs or scale endpoints without a significant infrastructure investment. Additionally, we often forget to switch off instances which cost us money!

Our Approach

We have three core areas that we are focused on which we believe are the most important for any infrastructure platform:

1. Performance:

We have worked hard to get our added network latency <50ms and the cold start of our average workloads to 2-4 seconds. Here are a few things we did to get our cold starts so low:

- Container Runtime: We built our own container runtime that splits container images into two parts—metadata and data blobs. Metadata provides the file structure, while the actual data blobs are fetched on-demand. This allows containers to start before the full image is downloaded. In the background, we prefetch the remaining blobs.

- Caching: Once an image is on a machine, it’s cached for future use. This makes subsequent container startups much faster. We also intelligently route requests to machines where the image is already cached.

- Efficient Inference: We route requests to the optimal machines, prioritizing low-latency and high-throughput performance. If no containers are immediately available, we efficiently queue the requests through our task scheduling system.

- Distributed Storage Cache: One of the most resource-intensive parts of AI workloads is loading models into VRAM. We use NVME drives (which are much faster than network volumes), as close as possible to the machines and we orchestrate workloads to nodes that already contain the necessary model weights where possible.

2. Developer Experience

We built Cerebrium to help developers iterate as quickly a...