Managed inference vs DIY serverless GPU
Modal gives you flexible serverless GPUs for any workload. Fleek gives you optimized, managed inference at fixed rates. Modal is better for custom workloads, training, and full control. Fleek is better for production inference where you want simplicity and low cost.
Modal is a fantastic platform—genuinely well-designed serverless GPUs with a great developer experience. But it's a different product than Fleek. Modal gives you raw GPU compute that you orchestrate. Fleek gives you fully-managed inference.
The question isn't which platform is "better"—it's which model fits your needs. Do you want to deploy and manage your own inference stack? Or do you want inference-as-a-service where everything just works?
This comparison breaks down the architectural differences, pricing implications, and use cases where each excels.
| Model | Fleek | Modal | Savings |
|---|---|---|---|
| A100 80GB (Modal)Raw GPU compute | N/A | $3.30/hr | N/A |
| H100 (Modal)Raw GPU compute | N/A | $4.76/hr | N/A |
| Fleek InferenceManaged, optimized inference | $0.0025/GPU-sec | Varies | Varies |
| Llama 3.1 70B equivalentModal cost depends on your optimization | ~$0.20/M tokens | ~$0.40-0.60/M | 50-67% |
Modal charges for GPU time—your actual inference cost depends on how well you optimize your deployment. Fleek includes optimization in the price.
Fleek is inference-as-a-service. You call an API, we run the model, you get results. All the infrastructure, optimization, and scaling is handled for you. Pricing is simple: $0.0025 per GPU-second.
You don't manage containers, GPUs, or scaling. You don't optimize batch sizes or precision. You just call the API and pay for what you use.
Looking ahead, we're also building serverless GPU infrastructure—not just managed inference. Our GPU utilization work enables significantly faster cold starts than typical serverless GPU platforms. When that launches, you'll be able to run arbitrary workloads on Fleek's infrastructure, not just inference.
Modal is serverless GPU infrastructure. You write Python functions, Modal handles provisioning and scaling. You have full control over what runs on those GPUs—inference, training, fine-tuning, anything.
This flexibility is powerful but requires work. You're responsible for optimization, batching, precision tuning, and all the details that determine cost efficiency. Modal provides the compute; you provide the smarts.
Moving from Modal to Fleek means giving up control for simplicity. If you've built custom optimization logic, you're trusting Fleek to match or beat it. Test on your actual workload to verify.
It depends on your optimization skills. Modal gives you raw GPUs—your cost per token depends on how efficiently you use them. Well-optimized Modal deployments can match Fleek. Unoptimized ones will cost more.
No, Fleek is currently inference-only. For training, fine-tuning, or other GPU workloads, Modal or similar platforms are the right choice. However, we're expanding to serverless GPU compute soon.
For inference, Fleek is dramatically simpler—just an API call. Modal requires you to write and deploy code, manage containers, and handle scaling. Modal's DX is excellent for its complexity level, but it's still more work than a managed API.
Yes, many teams use Modal exactly this way. You'll need to handle optimization, scaling, and API design yourself. It's more work but gives you more control.
Yes, it's on the roadmap. Our GPU utilization work has produced significantly faster cold starts than typical serverless platforms. We're starting with managed inference, then expanding to general serverless GPU compute. If cold start times matter to your workload, stay tuned.
Coming soon. We're building support for any model—not just the open-source ones we showcase. Upload your fine-tuned weights or proprietary model, and we'll apply the same optimization. Same $0.0025/GPU-sec pricing, no custom model premium. Launching in the coming weeks.
Modal and Fleek aren't really competitors—they're different tools for different problems. Modal is infrastructure; Fleek is a service built on infrastructure.
If you have ML engineers and want control, Modal is excellent. If you want inference that just works at the lowest cost, Fleek handles everything for you.
The gap is narrowing though. We're building serverless GPU infrastructure with significantly faster cold starts than typical platforms. Eventually, you'll be able to run arbitrary workloads on Fleek, not just inference.
For now, many sophisticated teams use both: Modal for custom workloads and training, Fleek for production inference. The right tool depends on what you're building and who's building it.
Note: Fleek is actively expanding model support with new models added regularly. Features where competitors currently have an edge may become available on Fleek over time. Our goal is universal model optimization—supporting any model from any source at the lowest possible cost.
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