Ertas
Summary
The moment you commit to fine-tuning a model for a client, you face a wall: ML infrastructure setup, GPU provisioning, and training pipelines that assume your team has a machine learning engineer on staff. Ertas AI is built for the teams that don't.
Ertas positions itself as a no-ML-expertise fine-tuning platform — upload your documentation, configure a training run on a canvas, and export a model you can ship in a mobile app or SaaS product. The vendor targets indie developers and agencies who need domain-specific models without the overhead of managing training infrastructure themselves. The self-hosted option does not exist, which means your training data transits Ertas servers — a hard stop for regulated industries. The export-and-run-local story works for offline mobile use cases, but the platform has no API, so integration is a manual file-transfer workflow rather than a pipeline.
Bottom line: Pick Ertas when you need one fine-tuned model for a client chatbot and have no ML engineer to call — plan a different stack when your customer's legal team asks where training data goes, or when you need programmatic model delivery across ten tenants.
Pricing Plans
SubscriptionLast verified 2 days ago- Price
- $25/mo
- Free Tier
- 5 daily-refreshed credits/day (up to 30/mo), Models under 5B, 5 GB model storage, 250 MB dataset storage, Manual testing sandbox
Free
For anyone exploring fine-tuning
- 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 client project teams
- 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
Enterprise
For enterprise needs
- Custom credit allocation
- Frontier GPUs (H100+), up to 70B+
- Ertas Vault (encryption + audit)
- On-premise deployment
- SSO / SAML
- Dedicated CSM + SLA
View full pricing on ertas.ai →
Pricing may have changed since last verified. Check the official site for current plans.
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Pros
Sign in to edit- Canvas-based training configuration requires no ML engineering background, so teams without a data scientist can produce a domain-specific model without writing training code or managing GPU infrastructure.
- Exported models run locally on-device, which means inference costs drop to zero after training and offline mobile AI features work without a network dependency.
- Freemium entry point lets you validate whether fine-tuning improves your use case before committing budget, so you avoid paying for training runs on a hypothesis that hasn't been tested.
- Domain-specific fine-tuning on your own documentation produces a model that stays on topic and reflects your product's terminology, reducing the hallucination surface compared to a general-purpose hosted model answering questions it wasn't trained for.
Cons
Sign in to edit- No self-hosted option means all training data — including customer documentation, proprietary content, or anything sensitive — is processed on Ertas infrastructure. Teams handling HIPAA, GDPR-restricted, or contractually confidential data hit this wall before they finish the sign-up form and move to a self-hosted fine-tuning stack like Axolotl or a managed service that offers a VPC deployment.
- No API means every model update, retraining run, and model delivery to a new tenant is a manual operation. A multi-tenant SaaS shipping per-customer models at scale will accumulate operational overhead that a file-transfer workflow cannot absorb — teams managing more than a handful of tenants typically end up rebuilding the delivery layer themselves or switching to a platform with programmatic model management.
- The platform is not agentic and has no tool-calling or workflow execution capability, so if your use case evolves past a static chatbot into anything that needs to take an action — query a database, send a notification, fetch live data — Ertas is not part of that architecture and you are adding a separate system alongside it.
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About
- Platforms
- Web-based platform; exports to iOS, Android, desktop, and web apps
- API Available
- No
- Self-Hosted
- No
- Last Updated
- 2026-06-01T19:05:13.252Z
Best For
Who it's for
- Indie developers and solopreneurs building AI-powered apps
- Startups wanting to reduce API infrastructure costs
- Teams building domain-specific models without ML expertise
- Agencies shipping client work with custom models
- Mobile app developers implementing offline AI features
What it does well
- Domain-specific support chatbots fine-tuned on product documentation
- Offline mobile apps with custom AI features
- Reducing API costs by running local models instead of cloud APIs
- Building multi-tenant SaaS with per-customer fine-tuned models
- Privacy-preserving AI applications with no data leaving the device
Integrations
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Frequently Asked Questions
- Is Ertas free?
- Ertas is a paid tool ($25/mo). No permanent free tier is offered.
- Is Ertas open source?
- No — Ertas is a closed-source tool. Source code is not publicly available.
- When was Ertas released?
- Ertas was first released in 2026.
- What platforms does Ertas support?
- Ertas is available on: Web-based platform; exports to iOS, Android, desktop, and web apps.
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Curated lists that include this category
Fine-tuning a model on your product documentation should not require provisioning GPUs or writing training scripts. Ertas AI addresses that gap with a canvas-based workflow: you bring your domain content, configure a training run through a visual interface, and export the resulting model for deployment. The intended output is a model you can embed in a mobile app for offline inference or serve per-customer inside a SaaS product — without touching cloud inference APIs after the model is exported.
The core differentiator Ertas targets is cost reduction through local model execution. Once you export a fine-tuned model, inference runs on-device or on your own infrastructure, eliminating per-token API fees from providers like OpenAI. For solopreneurs or agencies shipping multiple client projects, that cost ceiling is the practical reason to go through fine-tuning at all rather than wrapping a hosted API.
Where Ertas fits: a single-tenant chatbot trained on a product knowledge base, an offline mobile feature that needs to work without a network connection, or an agency delivering a white-labeled AI feature to a client who wants something that answers their domain correctly out of the box. Where it breaks: the absence of an API means you cannot automate model delivery or trigger retraining from an external system — every update is a manual operation. No self-hosted option means training data leaves your environment, which disqualifies the platform for healthcare, legal, or financial data before the conversation starts. Multi-tenant SaaS use cases are listed as a target, but managing per-customer model versions without programmatic tooling creates operational overhead that grows with each new tenant.