Build with models you own.
Train custom AI models. Deploy them wherever. On-device, local, or managed cloud.
Train your AI model. Visually.
How it works
From your data to a deployable model in three steps.
Upload your dataset
Upload your own JSONL, or import one from Hugging Face with a URL. No dataset yet? Build one from scratch in Data Craft, our guided dataset builder.
Fine-tune in Studio
Fine-tune several models in parallel on fast cloud GPUs, no expensive hardware needed. The visual canvas is where you iterate: compare runs, adjust your recipe, and decide what works for your product.
Deploy where your product runs
Export your fine-tuned model as GGUF and run it on-device or locally with llama.cpp, Ollama, or LM Studio. Deploy to your own managed cloud today, with one-click deployment on Ertas coming soon.
Inside the platform
Everything you need to fine-tune and ship custom AI.
Model Studio
Fine-tune popular open-source models on fast cloud GPUs. Supports Gemma 4, Llama 3.2, Qwen 3.5, and more. Visual canvas, no code required.
See supported models →
Why fine-tune your own model?
For a specific task, a custom model can be cheaper, faster, and easier to own than one more prompt wrapped around a generic API.
Models that understand your domain
Fine-tuned models outperform generic LLMs at your specific tasks, better than any prompt engineering.
Costs that don't scale with your users
Cloud APIs charge per token, and your bill grows with every user. On-device inference costs nothing at runtime, whether you have 1,000 users or 100,000.
Ship AI features without ML expertise
Ertas handles the training pipeline. You focus on your product, not infrastructure.
On-device: offline, instant, private
No network round-trip, no loading spinners, no data leaving the device. Your AI features work everywhere, even offline.
Built for app builders who ship AI-powered products: indie devs, startups, agencies, and solo builders.
"I fine-tuned a model on our product docs in under an hour. Now our support bot actually understands our domain instead of hallucinating."
Jamie K.
Indie Developer
"Replaced our $400/mo API bill with a fine-tuned model running locally. Better results, predictable costs. Exactly what we needed."
Maria R.
Startup Founder
Built for how you work
Start free. Scale as you grow.
Free
Try it out, 30 credits/mo
- 5 daily-refreshed credits/day (up to 30/mo)
- Models under 5B
- 5 GB model storage
- 250 MB dataset storage
- Manual testing sandbox
Builder
For indie devs and solo builders
- 100 credits/mo
- Up to 14B models
- 50 GB model storage
- 2 GB dataset storage
- Higher tier GPUs
- 1-month credit rollover
Pro
For power users and teams managing client work
- 200 credits/mo
- 100 GB model storage
- 5 GB dataset storage
- Priority GPU queue
- Preview access to new features
Business
For scaled teams and multi-client agencies
- 400 credits/mo
- 200 GB model storage
- 10 GB dataset storage
- Higher priority GPU queue
- Priority preview access to new features
Need compliance, on-prem, or frontier hardware? Talk to us about Enterprise
Frequently asked questions
Common questions about training and shipping custom models with Ertas.
No. Ertas handles the training workflow, GPU setup, model packaging, and export path. You still bring the product judgment: the task, the data, and what 'good' looks like for your app.
No. On-device deployment is one strong use case because it gives you offline support, privacy, and zero per-token inference cost. You can also train models for server-side inference, managed GPU deployment, internal tools, support bots, agents, and product workflows.
You get a fine-tuned model artifact that you can test, export, and integrate into your app. For local or on-device deployment, Ertas supports GGUF export. For teams that want hosted inference, managed deployment through Ertas Cloud is on the roadmap.
Ertas supports popular open models including Gemma, Qwen, Llama, Mistral, and Phi. We focus on models that builders can actually ship, from small models for local and on-device use to larger models for server-side or GPU deployment. Some additional model families may work experimentally, but official support starts with the models listed above.
Ertas stores your datasets so you can rerun experiments, compare training runs, and avoid uploading the same files again. You stay in control of what you upload and can delete datasets you no longer need from Data Craft. For teams with stricter data requirements, Ertas Vault adds encrypted storage, access controls, and audit trails.
Model Studio is the core product today: upload data, train models on cloud GPUs, test results, and export models for your app. Hub lets you manage your fine-tuned models in one place, with public discovery and sharing coming next. Cloud will add managed API deployment for teams that don't want to run their own hosting infrastructure.
Yes. The free plan starts immediately and does not require a credit card. You get monthly credits to train and test smaller models. Paid plans add more credits, larger model support, more storage, and higher priority access to GPUs.
Start building today.
Free to start. No card required.