Modal Alternatives for Serverless GPU Inference

Teams outgrow their first serverless GPU platform for predictable reasons: cold-start latency on large models, a narrow GPU catalogue, thin region coverage, or a pricing model that does not fit bursty real-time traffic. If you are evaluating Modal alternatives for serverless GPU inference, the question that decides the migration is not which platform has the longest feature list. It is which one gets a container from cold to serving production traffic quickly, and keeps doing it at the tail.

This comparison uses numbers we can show our working for. Every figure attributed to Cerebrium below comes from a published benchmark, not a marketing page.

What should you compare when evaluating a serverless GPU platform?

Four properties decide whether a platform survives production inference:

• Cold-start time on your model, not on a hello-world container. A platform that starts a small CPU service in two seconds can take a minute or more to restore a multi-gigabyte CUDA process with weights resident in GPU memory.

• Tail behaviour. The worst-case restore time is what your p99 user experiences during a scale-up. A good average with a bad tail still drops requests.

• GPU breadth and regions. A low headline rate means nothing if that GPU is never available when you scale. Region coverage also sets your floor on network latency, and we have measured 150 to 250ms London to us-east-1 round trips collapse to 30 to 70ms with regional placement.

• Deployment model. Whether you ship Python or maintain Kubernetes manifests changes who on your team can deploy.

Cold start is the property that surprises teams most often, because the number quoted in docs is rarely the number you get with a real model. We wrote up why container image distribution is usually the bottleneck in rethinking container image distribution to eliminate cold starts.

How do serverless GPU platforms compare on cold starts?

The table below reflects our published provider comparison. Treat any vendor-reported figure, including ours, as a starting point for your own test rather than a guarantee.

Our own figure of 2 to 4 second cold starts is the same one we publish in our provider round-up, and it holds across our GPU catalogue and regions.

What did our benchmarks measure against Modal and Baseten?

We built a snapshot-and-restore path that checkpoints a running CUDA process, including model weights already resident in GPU memory, and restores it instead of cold-booting the container. The full methodology, six workloads with 100 cold-start requests each over 24 hours on matched GPU classes, is published in reducing GPU cold starts with memory snapshots.

Three results from that suite are the ones worth carrying into a platform decision:

Against our own baseline. In our testing, snapshots reduced cold starts by an average of 71 percent compared with the same workloads running without snapshots, and by as much as 88 percent on vLLM. Restoring from a 9 GiB checkpoint brought startup down to 2.25 seconds from S3 and 9 seconds from local NVMe, against a roughly 50 second full vLLM cold start on the same instance type.

Against Modal snapshots. Based on the same suite, Cerebrium recorded a lower p0 restore time on 4 of the 6 workloads, with an average p0 restore time around 21 percent lower. The tail result matters more for production: our benchmarks recorded a lower worst-case restore time on all 6 workloads, with an average maximum restore time around 27 percent lower. Consistency at the tail is what keeps a scale-up from timing out.

Against Baseten cached cold starts. According to the same benchmark, Cerebrium snapshots were 85 percent faster on average and up to 94 percent faster on vLLM. Baseten snapshotting was not self-serve at the time of the test, so cached cold start was used as the comparison path.

These are our numbers, run by us. Reproduce them on your own model before you migrate anything.

Which alternative fits which workload?

Real-time voice and interactive agents. This is the least forgiving case, because a cold start is audible. Sub-second perceived latency requires the whole pipeline to be budgeted, which we walk through in deploying a global scale AI voice agent with 500ms latency. Platforms in the 16 to 60 second cold-start band are generally a poor fit here unless you keep instances permanently warm, which removes the economic reason for going serverless.

Batch and asynchronous inference. Cold start matters far less. Optimise for GPU price per second and queue throughput instead. If you are moving off a self-managed queue, our comparison of Celery and Redis for ML workloads covers the tradeoffs, including the 100 to 500ms broker overhead a task queue adds per task.

High-throughput LLM serving. Runtime choice usually dominates platform choice. We benchmarked vLLM, SGLang and TensorRT-LLM head to head in benchmarking vLLM, SGLang and TensorRT for Llama 3.1, and the spread between runtimes on identical hardware was larger than the spread between most platforms.

Teams leaving a hyperscaler entirely. If the migration is off AWS, GCP or Azure rather than off a serverless peer, the calculus is different and we cover it in alternatives to AWS, GCP and Azure for deploying AI models.

How do you migrate without a rewrite?

Most migrations between serverless GPU platforms are a packaging change, not an application rewrite. The model code, weights and inference entrypoint are portable. What changes is how you declare hardware, scaling and dependencies.

A practical sequence:

1. Reproduce your current cold start on your real model, with a cold container and no warm pool. Record the median and the maximum, because the maximum is what breaks.

2. Deploy the same container on the candidate platform and re-run the identical test.

3. Test a scale-up, not just a single request. Provisioning behaviour under concurrency is where platforms diverge. Our own autoscale provisioning of new workers runs in the same 2 to 4 second band.

4. Check region placement against where your users are before you compare any latency numbers.

5. Model the cost on your real duty cycle. Per-second billing and idle behaviour matter more than headline GPU rates for bursty traffic, and you can check current rates on our pricing page.

For a broader view of what to weigh when choosing between platforms at global scale, see choosing the right serverless GPU platform. If you want to talk through a specific workload before testing, book a demo.

The short version

If cold start is why you are looking at Modal alternatives, test the tail and not the average, and test it on your real model. Our benchmarks put snapshot restore at 2.25 seconds for a 9 GiB checkpoint and recorded lower worst-case restore times across all six workloads in the suite. Those are the conditions under which a serverless GPU platform stops being a latency risk and starts being an operational advantage.