Category
AI Infrastructure
Serve models fast, without owning GPUs.
27 tools·22 with free plan·13 with free trial
Read the editor's roundup →Run and serve AI models — inference APIs, GPU hosting, and model deployment platforms.
27 tools reviewed22 with a free plan13 with a free trial26 with an API11 self-hostable5 paid-only
Where to start
Not enough community votes to rank this category yet — this is a shortlist, not an order of merit.
Managed Ray for scaling AI and Python workloads
Anyscale is the most polished way to run Ray in production, and it is genuinely valuable if your team already lives in the Ray ecosystem and wants to avoid cluster babysitting. The catch is that its value is tightly coupled to Ray adoption, and the pending Nscale acquisition adds near-term uncertainty. Because Ray is free and open source, teams comfortable with infrastructure work can replicate much of the core functionality themselves; Anyscale is worth paying for when engineering time is more expensive than the platform fee.
freemium·Pay-as-you-go compute; free tier / credits available
Deploy and scale ML models in production inference.
Baseten is aimed at teams putting real models into production: package with Truss, deploy to autoscaling GPUs, and get the performance engineering and observability that serious inference needs. Its strengths are dedicated deployments, fast cold starts, and support for custom and fine-tuned models across modalities. It is best for companies with genuine production traffic and specific performance requirements. Caveats: it is more infrastructure than turnkey API, there is no permanent free plan (though trial credits exist), and GPU-based pricing means you should model costs carefully at scale. Overkill for hobby projects; well suited to production ML teams.
paid·Usage-based GPU compute; free trial credits
Serverless GPU runtime for AI inference, training, and sandboxes
Best for quickly spinning up serverless GPU functions and Python workloads with minimal setup, much like Modal. The honest limitation is that it is smaller and less battle-tested, so expect a thinner ecosystem and fewer integrations.
freemium·Pay-as-you-go
All AI Infrastructure
Small specialised on-device models for speech, text, and vision, one per task
#on-device models#edge ai#speech recognition#privacy 1 views
Fast, production inference for open and custom models.
#inference#llm-api#fine-tuning#enterprise 1 views
Open-access AI cloud and decentralized GPU marketplace
#gpu-cloud#inference#open-models#decentralized 1 views
OpenAI's developer API for text, image, audio, and embedding models
#openai#llm api#gpt#ai infrastructure
One API for hundreds of AI models across providers.
#llm-api#model-routing#aggregator#multi-provider 22 views
Inference, fine-tuning, and GPU clusters for open models.
#inference#fine-tuning#gpu-cloud#open-source 4 views
Other AI tool categories
Frequently asked questions
- What's the best ai infrastructure tool?
- Anyscale currently leads in community votes. Anyscale is the most polished way to run Ray in production, and it is genuinely valuable if your team already lives in the Ray ecosystem and wants to avoid cluster babysitting. The catch is that its value is tightly coupled to Ray adoption, and the pending Nscale acquisition adds near-term uncertainty. Because Ray is free and open source, teams comfortable with infrastructure work can replicate much of the core functionality themselves; Anyscale is worth paying for when engineering time is more expensive than the platform fee.
- Are there free options?
- 22 tools in this category offer a free plan. Another 13 have a free trial.
- How do you rank these tools?
- Tools are ranked by a combination of community upvotes, editorial review, and feature breadth. Our editors review pricing and capabilities quarterly.
- Can I suggest a tool we're missing?
- Yes — submit it here. Our team reviews submissions weekly.