What are the best alternatives to Tabby?

Based on documented capability coverage across the Agentic Index 14 point taxonomy, the closest alternatives to Tabby among coding agents are camelAI, Blackbox AI, iGent, 10Web. Each is graded on the same public evidence, and no vendor pays for placement.

Can Tabby be self hosted or run in a private VPC?

Full under the Agentic Index verification standard. Tabby publishes public evidence of self hosted or private VPC deployment, along with data residency options for controlling where agent data is processed and stored. Evidence basis: Written in Rust and distributed under Apache 2.0 as a self-contained server requiring no external database management system or cloud service, installed by Docker, standalone binary, Homebrew tap or Kubernetes, and running on consumer-grade. Graded from public evidence only against the Agentic Index 14 point capability taxonomy. No vendor pays for placement.

Can you bring your own model or route between providers with Tabby?

Full under the Agentic Index verification standard. Tabby publishes public evidence of bring your own model and bring your own key (BYOK), and of routing tasks between models or providers rather than being locked to one. Evidence basis: The model is selected by the customer at launch through a serve flag and a separate chat model can be paired with the completion model in the same command, with embeddings configurable independently; supported families named across first-pa. Graded from public evidence only against the Agentic Index 14 point capability taxonomy. No vendor pays for placement.

Does Tabby support MCP and tool calling?

Partial under the Agentic Index verification standard. Tabby publishes some public evidence of tool calling and API or SDK extensibility, but narrower than full Model Context Protocol (MCP) support as defined by the Agentic Index taxonomy. Evidence basis: The self-hosted server exposes an OpenAPI interface documented as easy to integrate with existing infrastructure including cloud IDEs, serving completion, chat and answer endpoints that the official plugins consume, and custom documentation. Graded from public evidence only against the Agentic Index 14 point capability taxonomy. No vendor pays for placement.

Back to vendors

Tabby

Also known as: Pochi

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Coding agentindependentVerified 2026-09-29

Entry priceCommunity is free and open source for up to 5 users with local deployment; Team is $19 a month per seat for up to 50 users with flexible deployment; Enterprise is custom with unlimited users, SSO and group managementFull pricing detail

Open-source, self-hosted coding assistant serving fill-in-the-middle completion, an Answer Engine for codebase questions, inline chat and a code browser entirely on your own hardware, with customer-chosen open models and no external database or cloud service.

Tabby is an open-source, self-hosted AI coding assistant built by TabbyML for teams that want modern AI help without sending code to a third party. Written in Rust and distributed under Apache 2.0, it runs as a single self-contained server via Docker, a standalone binary, Homebrew or Kubernetes, and needs no external database or cloud service. It loads an open coding model onto a GPU and serves suggestions over an OpenAPI-documented REST interface, running on consumer-grade NVIDIA, Apple and AMD hardware. The project is mature and widely used, with a long release history and more than a hundred contributors.

Its core is fast code completion tuned for the fill-in-the-middle pattern autocomplete relies on. Around that, Tabby adds an Answer Engine that lets a developer ask questions about the codebase and get grounded explanations in the editor, an inline chat for real-time back and forth, and a code browser. The server keeps a persistent index of the team's repositories, parsed with Tree-sitter and embedded, and Context Providers pull in documentation, configuration files, Git repositories and external interfaces, so the assistant understands a project rather than a single file. Answer Engine threads can be turned into persistent, shareable Pages for a team.

Tabby is deliberately model-agnostic, and because the server is self-hosted the customer supplies and runs the weights. The model is chosen at launch, a separate chat model can be paired with the completion model, and embeddings are configurable independently. Supported families include StarCoder and StarCoder2, CodeLlama, Qwen and Qwen2.5-Coder, Codestral through a Mistral integration, and Mistral embeddings, with Llamafile deployment supported and fine-tuning on a team's own code possible. It integrates as a plugin across VS Code, the JetBrains family, Vim, Neovim and Emacs, and works with cloud editors.

What Tabby is not is an agent. It serves completions, answers and chat rather than planning, executing or acting on external systems: there is no tool-calling loop, no code execution, no terminal or browser, and no test or review engine. The homepage promotes a new Agent, but no Tabby agent capability is documented; the planning and executing teammate TabbyML now markets is Pochi, a separate cloud product. Buyers should read Tabby as an open-source, privacy-first assistant rather than as a coding agent.

For administration, Tabby ships an admin dashboard for user accounts, per-developer API tokens, model management and usage analytics, with LDAP directory authentication and generic OAuth for team deployments; SSO and group management sit on the Enterprise plan. No security certification is documented.

Pricing is on tabbyml.com/pricing: the Community plan is free and open source for up to 5 users on local deployment, Team is $19 a month per seat for up to 50 users, and Enterprise is custom with unlimited users. On every plan the team also pays for the hardware or cloud compute that serves the models, and needs the capacity to run and maintain model serving.

Vendor details

Canonical URL

https://tabbyml.com

Subcategory

Open source self-hosted coding assistant (completion, answer engine, chat)

Funding status

TabbyML is the company behind the open-source Tabby project, distributed under Apache 2.0. Specific venture funding is not disclosed on first-party sources. The project reports roughly 33,800 GitHub stars and 1,780 forks with over a hundred contributors, and the latest stable release documented is v0.32.0 on 25 January 2026, adding Mistral Embedding API support, generic OAuth and multi-branch repository indexing. Note that the tabby repository's last recorded activity is 30 June 2026, while the company's newer TypeScript project Pochi was updated 24 August 2026, suggesting engineering attention has shifted.

Company status

independent

Use cases & customers

Primary use cases

private self hosted code completioncodebase question answering in the IDEAI assistance for regulated and air gapped teamsmodel agnostic coding assistant on owned hardware

Target customers

privacy first and regulated teams (finance, healthcare, defense)organizations that run their own infrastructureteams wanting an open source alternative to cloud coding assistants

Deployment options

Open-source self-hosted (Docker, standalone binary, Homebrew)On-premisesPrivate cloud / KubernetesConsumer-grade GPUs (NVIDIA CUDA, Apple Metal, AMD ROCm)Fully offline, no external DBMS or cloud

Integrations

Runs as a self-hosted server exposing a REST API for completion, chat, and answers, and connects to IDEs through official plugins for VS Code, the JetBrains family, Vim, Neovim, and Emacs, plus cloud editors. Context Providers ingest documentation, configuration files, Git repositories, and external interfaces to ground the assistant, and custom documentation can be added over the API. An admin dashboard manages users, models, and usage. Tabby focuses on completion, answers, and chat rather than acting on external tools, and no Tabby agent capability is documented.

In practice

You cannot send source code to a cloud service. Tabby runs entirely on your own hardware or private cloud, serving completion, chat, and answers with no external calls, so code never leaves your infrastructure.

You want to pick your own model. Tabby is model agnostic, running open coders like StarCoder, CodeLlama, or Qwen, and you can combine a completion model with a chat model or fine tune on your codebase.

Your team is large enough that per seat subscriptions add up. Tabby is free and open source with no seat or usage fees, so your only cost is the hardware or cloud compute running the server.

Sources & related URLs

Related / legacy domains

https://github.com/TabbyML/tabby https://tabby.tabbyml.com/docs/welcome/ https://github.com/tabbyml https://github.com/TabbyML/pochi https://www.tabbyml.com/pricing

Research sources

https://www.tabbyml.com/ https://github.com/TabbyML/tabby https://tabby.tabbyml.com/docs/welcome/ https://github.com/tabbyml

Agentic Index coverage score

6.0 / 14 capabilities · 43%

Integrations & Tool Calling	Partial
A self-hosted server exposes an OpenAPI-documented REST interface for completion, chat and answers, with official plugins for VS Code, the JetBrains family, Vim, Neovim and Emacs plus cloud IDE support; Context Providers ingest documentation, configuration files, Git repositories and external interfaces, and integrations cover GitLab SSO and self-hosted GitHub and GitLab for repository access, with custom documentation addable over REST APIs. The product ingests context and returns suggestions rather than calling tools to act: no MCP client, tool-calling loop, write path to source control or CI action is documented. Sourcetabbyml.com and github.com/TabbyML/tabbyread 2026-08-30
Workflow Orchestration	Not documented
The shipped product serves single-turn interactions: fill-in-the-middle code completion, an Answer Engine that responds to codebase questions, inline chat, and a code browser, with background jobs for repository indexing. An Agent feature is promoted on the marketing site, but the vendor's own release feed records it as a private preview waitlist announced 25 May 2025 requiring direct-message approval, and no shipped agent capability, multi-step execution loop, planning stage or task orchestration is documented in the repository release notes, the documentation or the site fifteen months later. Sourcegithub.com/TabbyML/tabby release feed and tabby.tabbyml.com/docsread 2026-08-30
Knowledge Grounding & RAG	Full
The server keeps a persistent index of the customer's repositories, parsed with Tree-sitter and embedded, re-indexed by background jobs and extended to multiple branches in v0.32.0; Context Providers (data connectors) add documentation, configuration files, Git repositories and external interfaces, custom documentation can be added over REST APIs, and completions, the Answer Engine and chat all retrieve from that index. A maintained retrieval structure over the customer's knowledge is Full; structural cross-file reasoning such as a call graph is not required. Sourcegithub.com/TabbyML/tabby release feed and tabbyml.comread 2026-09-29
Human Oversight & Guardrails	Partial
Every output is proposed for the developer to accept or reject: inline ghost-text completions with multiple choices available since VSCode plugin 1.6, chat responses, inline edits invoked deliberately through a right-click option, and auto-generated commit messages, with nothing written without an explicit accept; team deployments add administrator-managed accounts, per-developer API tokens and directory authentication bounding who can use the server. No per-action approval gate, permission scoping on agent actions or runtime guardrail is documented, and no autonomous execution exists to gate. Sourcetabbyml.com and github.com/TabbyML/tabbyread 2026-08-30
Security, Identity & Governance	Partial
Team deployments support LDAP directory authentication, shipped in v0.24.0, and generic OAuth added in v0.32.0 on 25 January 2026, with an admin dashboard managing user accounts, API token issuance per developer and model configuration; self-hosting means code and inference never leave the customer's infrastructure and no external service holds data. No security attestation or certification, SAML, SCIM, fine-grained role-based access control or audit logging surface is documented in the repository, the docs or on the site. Sourcegithub.com/TabbyML/tabby and tabby.tabbyml.com/docsread 2026-08-30
Observability & Auditability	Partial
An admin dashboard creates user accounts, issues per-developer API tokens, manages models and shows usage analytics, with a notification box reporting background job status added in v0.22.0 and improved job notifications in v0.24.0; Answer Engine threads are shared on the main page for discoverability since v0.19.0 and can be turned into persistent Pages since v0.28.0, giving a durable record of questions and answers. No execution trace, audit log or per-action record is documented, and no agent execution exists to trace. Sourcegithub.com/TabbyML/tabby release feedread 2026-08-30
Memory & State Persistence	Not documented
Answer Engine messages can be turned into persistent shareable Pages since v0.28.0 and recent shared threads surface on the main page since v0.19.0, so questions and answers persist across sessions and are discoverable by a team, and repository indexes persist server-side between sessions with completion caching for speed. No memory mechanism carrying learned context, preferences or corrections into later interactions is documented, and no per-user or per-repository memory layer appears in the release feed, the docs or on the site. Sourcegithub.com/TabbyML/tabby release feed and tabby.tabbyml.com/docsread 2026-08-30
Deployment & Data Residency	Full
Written in Rust and distributed under Apache 2.0 as a self-contained server requiring no external database management system or cloud service, installed by Docker, standalone binary, Homebrew tap or Kubernetes, and running on consumer-grade hardware across NVIDIA CUDA, Apple Metal and CPU, with documented deployment on GPU cloud hosts for teams without local hardware; the vendor states Tabby runs on the customer's own terms whether cloud or on-premises, so code and inference stay inside the customer boundary. Sourcegithub.com/TabbyML/tabby and tabbyml.comread 2026-08-30
Prebuilt Agents, Templates & Packs	Not documented
A single assistant ships covering completion, the Answer Engine, inline chat and a code browser, configured through Context Providers the customer points at their own documentation, configuration files and repositories, and through model selection at launch; a registry repository catalogs supported models rather than agents or templates and has not been updated since May 2025. No library of prebuilt agents, template gallery, prompt pack, skills catalog or marketplace is documented on the site, in the docs, in the release feed or across the GitHub organization. Sourcetabbyml.com, github.com/TabbyML/tabby and github.com/tabbymlread 2026-08-30
Triggers & Channel Coverage	Partial
Developers reach Tabby through official plugins for VS Code, the JetBrains family, Vim, Neovim and Emacs, plus cloud IDEs and a web interface hosting the Answer Engine and code browser, with invocation by typing for inline completion, by asking a question, or by opening inline chat and the chat side panel; background jobs handle repository indexing with a notification box reporting their status. No event-driven trigger, scheduled run or inbound chat, ticketing or source control channel is documented. Sourcegithub.com/TabbyML/tabby and tabbyml.comread 2026-08-30
Model Flexibility & Routing	Full
The model is selected by the customer at launch through a serve flag and a separate chat model can be paired with the completion model in the same command, with embeddings configurable independently; supported families named across first-party surfaces include StarCoder and StarCoder2, CodeLlama, Qwen and Qwen2.5-Coder variants, Codestral through a documented Mistral integration, and Mistral Embedding API support added in v0.32.0, alongside Llamafile deployment integration, and teams can fine-tune on their own code. Because the server is self-hosted the customer supplies and runs the weights directly. No per-task routing gateway across providers is documented. Sourcegithub.com/TabbyML/tabby and tabby.tabbyml.com/docsread 2026-08-30
APIs, SDKs & MCP Extensibility	Partial
The self-hosted server exposes an OpenAPI interface documented as easy to integrate with existing infrastructure including cloud IDEs, serving completion, chat and answer endpoints that the official plugins consume, and custom documentation and context sources can be added over REST APIs since v0.29; the entire server and plugin suite are Apache 2.0 open source and forkable, with a Homebrew tap and Docker images published. No SDK, MCP server, webhook surface or API for driving Tabby as an agent from another system is documented. Sourcegithub.com/TabbyML/tabby and tabbyml.comread 2026-08-30
Testing, Debugging & Optimization	Not documented
The product ships code completion, an Answer Engine, inline chat, a code browser and auto-generated commit messages, and documents no test generation, bug detection, static analysis, code review or quality engine; the vendor's own positioning is a coding assistant serving suggestions rather than a tool that validates code. No customer-facing testing, debugging, evaluation or optimization capability appears in the release feed, the documentation or on the site. Sourcetabby.tabbyml.com/docs, github.com/TabbyML/tabby and tabbyml.comread 2026-08-30
Browser & Computer Use	Not documented
Tabby serves completions, answers and chat over a REST interface and documents no code execution, terminal, sandbox or browser automation as part of the product; the REST interface, plugins and repository indexing are all programmatic and are not computer use. The vendor's GitHub organization hosts a fork of vercel-labs/agent-browser, a browser automation CLI for AI agents, but it is unconnected to any documented Tabby capability and belongs to the separate Pochi line. Sourcetabbyml.com, tabby.tabbyml.com/docs and github.com/tabbymlread 2026-08-30

The Agentic Index coverage score grades every vendor Full, Partial or Not documented against the same 14 buyer facing capabilities, from public evidence only. Each capability links to how all vendors in the index score on it. How this evidence is graded

Pricing

Community is free and open source for up to 5 users with local deployment; Team is $19 a month per seat for up to 50 users with flexible deployment; Enterprise is custom with unlimited users, SSO and group management

Per seat for Team, custom for Enterprise; the Community plan is free for up to 5 users, and the team still pays for the hardware that serves the models

Free tier

Included quota

The full open source product with no usage limits: code completion, the Answer Engine, inline chat, a code browser, Context Providers, an admin dashboard, and directory based authentication, all self hosted. Teams supply and run their own models and hardware. There are no per seat or per call charges.

What is public

tabbyml.com/pricing lists Community free and open source for up to 5 users with local deployment, Team at $19 a month per seat for up to 50 users with flexible deployment, and Enterprise custom with unlimited users, customized deployment, enhanced security and group management and SSO. The same page carries Tabby Cloud usage pricing for Pochi, which bills the token cost of the LLMs it runs with $20 in free monthly credits.

Billing mechanics

Free for up to 5 users; beyond that, Team is billed per seat monthly or yearly, and Enterprise is contracted. Model serving hardware is the customer's cost on every plan.

Cost watchouts

The real cost is infrastructure and operations: a graphics processor to serve models, and the engineering time to deploy, secure, and maintain the server. Larger models need more capable hardware. There is no vendor bill, but self hosting shifts cost and responsibility to the team.

Variable cost rationale

Tabby serves self hosted open models, so there is no per token vendor billing. Cost is the infrastructure that runs the server, which is largely fixed for a given hardware footprint and scales in steps as a team adds capacity, not continuously with each request. Exposure is therefore low and predictable, dominated by hardware rather than usage metered fees.

Additional watchouts

Self-hosting means the team pays to operate it on every plan: budget for graphics hardware or cloud compute and for the engineering time to deploy, secure, and maintain the server and models.

Overage / add-ons

Not applicable to the seat plans; Pochi, a separate product, bills automatically once usage passes its free credit.

Sales call required

Mixed (some tiers require a call)

Free / trial

Community plan: free and open source for up to 5 users with local deployment, covering completion, the Answer Engine, inline chat and Context Providers

Lowest paid plan

Team, $19 a month per seat, up to 50 users

Commercial notes

The self-hosted server is still Apache 2.0 open source, but team use above five users is now a paid seat plan.

Key ambiguities

Beyond the seat price, cost planning is mostly about infrastructure: which model, how much graphics memory, and how many concurrent developers a node can serve. Enterprise pricing is custom.

Cancellation / refund

No subscription or contract exists. Tabby is free and open source; teams control their own infrastructure and can stop at any time.

Missing data

Enterprise pricing is custom. The page also shows three annual plans ($180, $280 and $480 a year) with generic feature lists that do not match the rest of the page.

Agentic Index verified 2026-09-29

Official pricing page

Related vendors

Alternatives to Tabby

The closest documented capability profiles to Tabby among coding agents tracked by Agentic Index, ordered by similarity on the same 14 point evidence the rankings use. No vendor pays for placement.

camelAI6.0 / 14Adds documented Workflow Orchestration and Memory & State Persistence
Blackbox AI7.5 / 14Adds documented Workflow Orchestration and Testing, Debugging & Optimization
iGent5.0 / 14Adds documented Workflow Orchestration and Memory & State Persistence, among others
10Web8.5 / 14Adds documented Workflow Orchestration and Prebuilt Agents, Templates & Packs, among others
Aider9.5 / 14Adds documented Workflow Orchestration and Memory & State Persistence, among others
Blitzy8.5 / 14Adds documented Workflow Orchestration and Memory & State Persistence, among others

Similarity is computed from each vendor's Agentic Index coverage score evidence, axis by axis, not from the totals. How this evidence is graded

Compare Tabby with other vendors →Compare all coding agents →See where Tabby ranks →

Data confidence: medium

Head to head

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