Simple inference vs full MLOps platform
Baseten offers comprehensive MLOps with autoscaling and monitoring. Fleek offers simpler, 50-67% cheaper inference. Use Baseten if you need a full ML platform. Use Fleek if you just want cheap inference.
Baseten positions themselves as a full ML infrastructure platform. Model serving, autoscaling, monitoring, observability—they've built comprehensive tooling for teams that need enterprise-grade MLOps.
Fleek is more focused. We do inference, and we do it cheaply. No built-in monitoring dashboards, no fancy autoscaling rules—just fast, optimized inference at $0.0025/GPU-sec.
This comparison helps you understand when you need Baseten's full platform, and when Fleek's simpler approach makes more sense.
| Model | Fleek | Baseten | Savings |
|---|---|---|---|
| Llama 3.1 70B | ~$0.20/M tokens | ~$0.45/M tokens | 56% |
| Mistral 7B | ~$0.02/M tokens | ~$0.06/M tokens | 67% |
| Custom Model Hosting | $0.0025/GPU-sec | Varies by GPU | Varies |
Baseten pricing depends on GPU selection and scaling configuration. Their pricing can be complex with platform fees. Fleek is straightforward $0.0025/GPU-sec.
Fleek does one thing: optimized inference at $0.0025/GPU-sec. Call the API, get results. We handle optimization, scaling, and infrastructure. You don't configure autoscaling rules or monitoring—we manage all of that.
Simple, but limited. If you need complex deployment configurations or enterprise monitoring, you'll need to add those yourself.
Baseten is a full ML platform. Deploy models with custom autoscaling, set up monitoring dashboards, configure A/B tests, manage model versions. It's infrastructure for teams that need visibility and control.
Their Truss framework makes model deployment straightforward. Enterprise features include dedicated capacity, compliance certifications, and SLAs.
If you're using Baseten's platform features (autoscaling rules, monitoring), you'll need to replicate those elsewhere. If you're just using their inference, migration is straightforward.
Fleek handles scaling automatically—you don't configure it. Baseten lets you define custom autoscaling rules. If you need fine-grained control over scaling behavior, Baseten has more options.
Baseten has built-in monitoring dashboards and observability tools. Fleek provides basic usage metrics. For comprehensive ML monitoring, Baseten or external tools like Datadog are better options.
Yes, Baseten has SOC 2 certification for enterprise customers. Fleek's compliance certifications are still in progress. If SOC 2 is mandatory, Baseten currently has the edge.
Yes, both support custom model deployment. Baseten uses their Truss framework. Fleek supports custom deployments at $0.0025/GPU-sec. Baseten gives you more configuration options; Fleek is simpler.
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.
Baseten is an ML platform; Fleek is an inference service. Different products for different needs.
If you have ML engineers and need autoscaling rules, monitoring dashboards, and enterprise compliance, Baseten's platform makes sense. If you just want cheap, fast inference without the platform overhead, Fleek is simpler and 50-67% cheaper.
The honest answer: most small teams don't need Baseten's full platform. Most enterprises might. Evaluate based on your actual requirements, not features you might need someday.
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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