01
Add your knowledge
Start with your documents, datasets, text, or structured data.
SMALL MODELS. BIG POSSIBILITIES.
Build your own AI.
Turn your knowledge into a small, specialized AI model.
No ML team required.
Your knowledge. Your model. Wherever you want to run it.
YOUR NEXT AI STARTS HEREInteractive preview
LET’S MAKE IT YOURS
Product expert Draft
A little model. A whole lot of your knowledge.
Give your AI a starting point
The knowledge that makes your model, yours.
product-handbook.mdSample document · Product documentation
ExampleNext: turn your knowledge into useful training examples.
MEET YOUR FUTURE STUDIO
A preview of the workflow we’re building. Cloud access is coming soon.
FROM WHAT YOU KNOW TO WHAT YOU BUILD
Your expertise.
A model of its own.
Upload your knowledge. Let AI prepare the dataset. Train a smaller model. Evaluate it. Download it. Run it anywhere supported.
02
Give it a purpose
Tell your model what it should do. One clear job is a great start.
03
Prepare the data
A teacher model helps turn your knowledge into training examples.
04
Build. Test. Improve.
Fine-tune a small model and compare it against the original.
05
Take it anywhere
Export supported artifacts. Choose where your model runs.
The planned Studio workflow. Available models, runtimes, and export formats will be listed at launch.
SMALL IS A SUPERPOWER
Frontier models know a lot.
Your model should
know your thing.
You don’t always need a model that knows everything. Sometimes, you need one that understands your product, your process, or your way of working.
SLM Studio is being built to make specialization approachable — with a guided path from your knowledge to a model you can evaluate and take with you.
Find your starting point Your knowledge Small model
Your AIPurpose-built for your thing
FOCUSED BY DESIGNA guide, not a learning curve
A clear next step from dataset to evaluation.
Choose how you build
Designed for multiple teachers and open-model targets.
Know before you spend
Cost estimates and execution locations before approval.
A SMALL MODEL FOR YOUR BIG IDEA
What’s your thing?
Start with a task you know well.
Build an AI around what makes it different.
A model that speaks your customers’ language.
Resolve product questions using your support playbook.
Customer support modelPotential use case. Validate quality on your own evaluation set.YOUR COMPUTER HAS POTENTIAL, TOO
Cloud convenience. A path to local AI.
Local connectivity is part of the plan from day one. The upcoming Local Bridge will connect your workspace to approved runtimes on your computer, starting with Ollama.
In developmentTWO WAYS TO MAKE IT YOURS
Your Studio. Your choice.
Run it yourself, or let us handle the workspace.
Same vision: build here, take it anywhere.
Open Source
Coming soonFor builders who like the keys.
Free / self-hosted
Your infrastructure. Your compute. Your control.
View release details- Self-host on your own infrastructure
- Bring your storage and provider keys
- Modify and extend the Studio
- Choose your supported deployment target
Planned free software. You cover your hardware, infrastructure, and provider usage.
Open-source release status
The public repository and license have not been released yet. A verified repository link will appear here when available.
Studio Cloud
Coming soonLess setup. More making.
$19.99 / month + usage
An isolated workspace, managed for you.
- Managed Studio workspace and updates
- Storage and provider integrations
- Job tracking and cost estimates
- Evaluation and supported artifact exports
AI provider and compute usage are billed separately. Planned pricing; subscriptions are not open yet.
Need private deployment or organizational controls?
FIND YOUR FIT
A focused place to start.
There’s more than one way to build a model. Choose the workflow that fits your team and how much infrastructure you want to manage.
| Platform | Built around | Workflow | Infrastructure | Portability |
|---|---|---|---|---|
| SLM Studio Planned | Individuals and small teams | Guided knowledge-to-model workflow | Managed cloud or self-hosted option | Supported exports; no required destination |
| Hugging Face / DIY | Hands-on model builders | Tools and workflows you assemble | Choose and manage your environment | Depends on model license and tooling |
| Databricks | Data and ML teams | Within a broader data and AI platform | Platform-managed cloud workspace | Depends on the model and workflow |
| Microsoft Foundry | Teams building on Azure | Model and deployment tooling in Azure | Azure resources and configuration | Depends on model and tuning service |
| AWS SageMaker AI | ML builders and engineering teams | Tools across the ML lifecycle | AWS infrastructure and services | Depends on framework and artifacts |
| Managed fine-tuning APIs | Developers integrating a provider | Provider-specific API workflow | Training managed by the provider | Check provider export and hosting terms |
What else should I compare?
Check local or self-hosted availability, whether teacher-assisted dataset preparation is included, cost estimation before jobs, and your available deployment destinations. These vary by model, region, service, and configuration. Consult each provider’s linked documentation; no competitor pricing or universal capability rankings are implied.
SLM Studio’s planned approach includes a teacher-assisted workflow, explicit local/cloud execution boundaries, pre-job estimates, and supported portable artifacts.
A FEW GOOD QUESTIONS
Before you build.
What is a small language model?
A small language model has fewer parameters than larger models. It can be adapted for focused tasks and may require less compute, depending on the model, hardware, and workload. Smaller does not automatically mean better: evaluation matters.
Do I need to be an ML engineer?
The planned Studio experience guides you through dataset preparation, model selection, training configuration, and evaluation. You bring the knowledge and define what good results look like.
Can I use models running on my computer?
Local runtime connectivity is in development, starting with Ollama through a paired Local Bridge. Each workflow will identify where data is processed. Local hardware and electricity still have costs.
Is Studio Cloud private AI?
Studio Cloud is designed as a managed, isolated workspace. Cloud and external-provider workflows process data outside your machine. Data handling will be shown before execution; private deployment is a separate enterprise direction.
Can I start training today?
This site previews the product direction. Cloud accounts, billing, training, and the public open-source release are not available through this site yet.
LESS GENERAL. MORE YOU.Your knowledge.
Your knowledge.
Your model. Your AI.
Your next idea deserves a model of its own.