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Ertas vs Tabby

Bottom line

Ertas has a free plan plus paid upgrades, while Tabby is free. Tabby is open source and Ertas is not. You can run Tabby on your own machine or server; Ertas is hosted by the vendor.

Which one should you try?

Ertas

Freemium

Best for Indie developers and solopreneurs building AI-powered apps

Visit Ertas See full review

Tabby

Free

Best for Teams requiring on-premises deployment and data sovereignty

Visit Tabby See full review

Pricing, capabilities and platforms side by side. Prices are as of the "Pricing checked" date in the table.

Ertas

Ertas

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.

Tabby

Tabby

Open-source, self-hosted AI coding assistant with code completion, chat, and agentic automation.

AttributeErtasTabby
PricingFreemiumFree
Price$10/moCommunity free (up to 5 users); Team $19/user/mo (up to 50 users); Enterprise custom
Pricing checkedNov 5, 2026Oct 19, 2026
Free trialNot statedNot stated
Open sourceNoYes
Has APINoYes
Self-hosted optionNoYes
PlatformsWeb-based platform; exports to iOS, Android, desktop, and web appsLinux, macOS, Windows (via Docker); Cloud IDEs; AWS, GCP, Azure
Languages—All (language-agnostic; supports any language supported by underlying LLM)
Released2026-022023
Pros
  • 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.
  • Fully open-source and self-hosted with no vendor lock-in
  • No external databases or cloud services required
  • Agentic multi-step task automation with Pochi agent
  • Support for multiple popular IDEs and code editors
  • End-to-end stack optimization for fast completions under 1 second
Cons
  • 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.
  • Requires infrastructure management and GPU resources for optimal performance
  • Agent (Pochi) is in private preview, not fully released to general availability
  • Steeper setup complexity compared to cloud-based alternatives

Made up your mind?

Ertas

Freemium

Best for Indie developers and solopreneurs building AI-powered apps

Visit Ertas See full review

Tabby

Free

Best for Teams requiring on-premises deployment and data sovereignty

Visit Tabby See full review

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Specs come from each tool's own site and docs (how we test). AIDiveForge is editorially independent.

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