Foundation models and the platforms that host, serve and fine-tune them.
Explore foundation models and the platforms that host, serve and fine-tune them. Compare pricing models, latency, model choice and deployment options to pick the right way to run AI inside your own product.
Run open-source machine learning models with a cloud API Run AI with an API . Run and fine-tune models. Deploy custom models. All with one line of code. Thousands of models contributed by our community.
This category covers the models themselves and the infrastructure that runs them: hosted APIs, inference platforms, model catalogues and the tools for fine-tuning and serving your own.
What are AI Models tools?
The choice usually comes down to how much you want to operate. A hosted API is the fastest way to ship; running your own gives control over cost, privacy and latency at the price of managing infrastructure.
What matters most when comparing tools
Pricing model. Per token, per second of compute, or per hour of hardware. Each suits a different workload.
Cold starts and latency. A model that takes ten seconds to wake up is unusable in a live product.
Model breadth. A wide catalogue lets you switch as better or cheaper models appear.
Deployment options. Cloud, VPC or on-premises matters as soon as sensitive data is involved.
Match the tool to the job
Most teams get the best results by picking a tool built for the kind of design they do most often:
Free plans in AI Models are usually enough to test a tool and handle occasional work. Paid plans typically lift usage limits, unlock the more advanced models or exports, and add the team features you need once more than one person is involved. Prices on this page come from each tool's own pricing page, so confirm on their site before you buy.
Tips for better results
Benchmark on your own prompts, not on public leaderboards; rankings rarely match a specific task.
Measure cost per completed task, not per token. A cheaper model that needs three attempts is not cheaper.
Keep the model behind an interface in your code, so swapping providers later is a config change.
Watch cold starts on anything user-facing, and keep a smaller model warm as a fallback.
The bottom line
Start with the kind of work you do most, shortlist two or three tools built for it, and try their
free plans on a real project. Compare how editable the results are, how well they fit your existing
workflow, and what the exports look like before you pay for a year.
Ready to pick a tool? Browse all 11 AI Models tools with pricing and verified details.
Browse tools
Start with a hosted API. Move to your own infrastructure when cost at volume, data residency or latency gives you a concrete reason to, not before.
How is inference usually billed? +
Either per token for language models, or per second of hardware time for image, video and audio models. Some platforms mix the two, so check which applies to the model you want.
Can I fine-tune a model on my own data? +
Most platforms support it. Before you do, try better prompting and retrieval over your documents, which solves a surprising number of problems more cheaply.
Which AI Models tools are listed on Ai Product Index? +
11 AI Models tools are listed, including Replicate, Fal.ai, LM Studio, Civitai, AssemblyAI, Hume AI. Verified listings show full features, pricing, and pros and cons.