Mernit

Replicate was acquired by Cloudflare last fall [1]

If you're looking for an alternative, I work at beam.cloud which is another serverless GPU cloud with fast cold starts.

[1] https://www.cloudflare.com/press/press-releases/2025/cloudfl...

We initially wrote this for a less technical audience (where we spelled out MCP), then edited it to post here - it's not AI, it's just bad editing from my part. Fixed now.

The only Beam-specific part are the sandboxes, but those can easily be swapped out for the vendor of your choice. The architecture we described isn't exclusive to our product.

Beam is fully OSS BTW: https://github.com/beam-cloud/beta9

what is missing for you?

beam.cloud (YC W22) | Product Engineer, SWE, SRE, Infrastructure, DevOps, Networking | NYC | Onsite

Beam is an ultrafast AI inference platform. We built a serverless runtime that launches GPU-backed containers in less than 1 second and quickly scales out to thousands of GPUs. Developers use our platform to serve apps to millions of users around the globe.

We’re working on challenging problems, including:

* Low-level systems development: working with container runtimes, OCI image formats, and lazy-loading large files from content addressable storage

* Efficiently packing thousands of workloads into GPUs across multiple clouds

* Working with cutting-edge technologies, like GPU checkpoint restore and CRIU

You don’t need prior experience with AI/ML, only an interest in working on hard problems and shipping quickly.

Email us at founders<at>beam<dot>cloud, or apply here: https://www.ycombinator.com/companies/beam/jobs/

You should look into beam.cloud (I'm the founder, but it's pretty great)

It lets you quickly run long-running jobs on the cloud by adding a simple decorator to your Python code:

  from beam import function

  # Some long training function
  @function(gpu="A100-80")
  def handler():
    return {}

  if __name__ == "__main__":
    # Runs on the cloud
    handler.remote()

Fully open-source also.

You should checkout beam.cloud (I'm the founder). It's a modern FaaS platform for Python, with support for REST endpoints, task queues, scheduled jobs, and GPU support.

The major clouds don't support serverless GPU because the architecture is fundamentally different from running CPU workloads. For Lambda specifically, there's no way of running multiple customer workloads on a single GPU with Firecracker.

A more general issue is that the workloads that tend to run on GPU are much bigger than a standard Lambda-sized workload (think a 20Gi image with a smorgasbord of ML libraries). I've spent time working around this problem and wrote a bit about it here: https://www.beam.cloud/blog/serverless-platform-guide

beam.cloud | Founding Software Engineer, Infrastructure | Full-time | REMOTE | New York, NY USA

Beam is building a cloud runtime for running remote containers on GPUs. We’re used by thousands of developers for powering their generative AI apps, including companies like Coca-Cola, and we’re backed by great investors like YC and Tiger.

We’re building gnarly low-level distributed systems. You’ll have a major impact on the product and ship features directly to users. If working on a new Pythonic cloud runtime sound exciting, you might really like it here.

Apply here -> https://www.ycombinator.com/companies/beam/jobs/9fKNUsT-foun...

Yes we are hiring internationally

beam.cloud | Founding Software Engineer, Infrastructure | Full-time | REMOTE | New York, NY USA

Beam is building a cloud runtime for running remote containers on GPUs. We’re used by thousands of developers for powering their generative AI apps, including companies like Coca-Cola, and we’re backed by great investors like YC and Tiger.

We’re building gnarly low-level distributed systems. You’ll have a major impact on the product and ship features directly to users. If working on a new Pythonic cloud runtime sound exciting, you might really like it here.

Apply here -> https://www.ycombinator.com/companies/beam/jobs/9fKNUsT-foun...

There are a number of good options here. The different axes are cost of GPUs, performance, and ease of use / developer experience. You might consider beam.cloud (I'm one of the founders), which is oriented strongly on the performance and developer experience angle.

You should checkout https://beam.cloud (I'm the founder), it'll give you access to plenty of cloud GPU resources for training or inference.

Right now it's pretty hard to get GPU quota on AWS/GCP, so hopefully this is useful for you.

Cloudflare AI and Replicate are great for running off-the-shelf models, but anything custom is going to incur a 10+ minute cold start.

For running custom fine-tuned models on serverless, you could look into https://beam.cloud which is optimized for serving custom models with extremely fast cold start (I'm a little biased since I work there, but the numbers don't lie)

Serverless only works if the cold boot is fast. For context, my company runs a serverless cloud GPU product called https://beam.cloud, which we've optimized for fast cold start. We see Whisper in production cold start in under 10s (across model sizes). A lot of our users are running semi-real time STT, and this seems to be working well for them.

Sharing GPUs only really makes sense for GPUs that are large enough to share. MIGs can work for 80Gi A100s but won't work with smaller cards like T4s. It also adds latency to the GPU operations. Unfortunately there's not yet a silver bullet for this stuff.

What are you using for K8s autoscaling? We initially tried a few standard K8s scaling mechanisms and found that they didn't work well for GPU workloads. For example, if we were serving a low-RAM Huggingface model on GPU, it wouldn't trigger autoscaling. But since the GPU can only process one request at a time, the system would get bottlenecked while it waited to process requests one-by-one.

We wrote a bit about this here, if anyone is interested: https://www.beam.cloud/blog/serverless-autoscaling