Tabnine
Tabnine is the coding assistant for environments where code must not leave: fully air-gapped and on-premises deployment, zero code retention, a compliance posture built for audits, and custom models trained on your private codebase. It exists for the organisations the cloud assistants cannot serve.
That deployment flexibility, rather than peak suggestion quality, is the product: regulated industries, defence-adjacent teams and strict-sovereignty environments get modern AI coding inside their own walls.
It is enterprise-only, with no individual tier, and current suggestion quality deserves a direct test as part of any evaluation.
- Cost
- Paid only
- Ease
- Model
- Hosted service
- Checked
- August 2026
Prices, plans and model versions change fast: this is a mid-2026 snapshot; check the tool's official site for the latest.
Best for
- Fully air-gapped and on-premises AI coding
- Zero code retention with an audit-ready posture
- Custom models trained on your private codebase
- Regulated teams the cloud assistants cannot serve
- Sovereignty requirements with modern assistance inside
Less suited to
Individuals are outside the product entirely: there is no personal tier, and the buying motion is enterprise procurement.
Teams free to use cloud assistants trade little for its guarantees: suggestion quality and ecosystem breadth favour the mainstream tools.
Costs & data, in short
Free basic tier; paid plans per user, with enterprise self-hosted deployment priced by agreement.
The privacy architecture is the product: self-hosted and air-gapped options keep code entirely inside your perimeter, with licensed-data provenance documented.
Plans
| Tabnine Code Assistant | $39per user, per monthbilled annually; usage billed at API rates |
|---|---|
| Tabnine Agentic Platform | $59per user, per monthbilled annually; usage billed at API rates |
Prices as of August 2026. Prices and plans change regularly. Check with the provider before you buy.
In practice
How Tabnine is used, area by area.
Jobs
Software development
Tabnine serves developers in environments where the popular assistants cannot go
Tabnine serves developers in environments where the popular assistants cannot go. Banks, defence-adjacent and health teams get completions and chat from a deployment their security model allows, fully on-premises or air-gapped, with zero code retention, licensing provenance documented and custom models trained on the private codebase. It is the answer to wanting modern AI help inside hard constraints, and constraint is the operative word: developers with a free choice of tools will find better suggestions and broader ecosystems elsewhere. The buying motion is enterprise procurement with no individual tier, and evaluations should test current suggestion quality directly rather than assume it.
Example tasks
- Code with AI assistance where cloud tools are prohibited
- Get completions grounded in your organisation's own patterns
- Keep every keystroke inside the security boundary
- Roll out assistance without new data-processing agreements
- Evaluate quality on your own code before committing
Limits
Developers with a free choice of tools will find better suggestions and ecosystems elsewhere; Tabnine is chosen by constraints, and evaluations should test current suggestion quality directly rather than assume it.
Compares
| vs | Pick Tabnine when | Pick the other when |
|---|---|---|
| GitHub CopilotFull comparison → | Tabnine works where the popular assistants cannot go, serving completions and chat from deployments a locked-down security model allows, including on-premises, with licensing provenance documented | tool choice is free and suggestion quality and ecosystem lead the decision |
| GLM (Z.ai)Full comparison → | Tabnine is for the environment that is locked down enough that mainstream assistants are off the table, and rolls out without new data-processing agreements | a coding client is already in daily use and the flat plan should carry its routine volume |
| Sourcegraph CodyFull comparison → | Tabnine keeps every keystroke inside the security boundary, so the assistance never becomes a new place code can leave from | understanding the existing system is harder than writing the new code |
| QwenFull comparison → | Tabnine arrives through enterprise procurement with no individual tier, so it is adopted by an organisation rather than tried by a developer | your team wants a capable agentic coding model it controls end to end, starting locally with Ollama and moving to vLLM as usage grows |
| DeepSeekFull comparison → | Tabnine writes zero code retention into the deployment, which is the guarantee a security model asks about before it asks anything about quality | you want strong models cheap at volume, or fully under your control |
| Devin CloudFull comparison → | Tabnine puts AI assistance where cloud tools are prohibited outright, which settles the question before any comparison of capability starts | delegation with review fits your workflow better than another pair of hands |
| Devin DesktopFull comparison → | Tabnine is built for the banks, defence-adjacent and health teams whose security model decides the toolchain | multi-file AI edits appeal and prompt engineering as a hobby does not |
| Claude CodeFull comparison → | Tabnine grounds completions in your organisation's own patterns, with custom models trained on the private codebase rather than a general one | the backlog holds well-defined tasks and your judgement is worth more than your typing |
| OpenAI CodexFull comparison → | Tabnine expects its quality to be evaluated directly on your own code before a team commits to it rather than assumed | the backlog holds more well-scoped tasks than hands and delegation would genuinely clear it |
| Kimi CodeFull comparison → | constraint rather than preference is what picks the tool, since developers with a free choice will find better suggestions and broader ecosystems elsewhere | the client itself should be readable and changeable rather than only licensed, published as open source in the MoonshotAI/kimi-code repository |
Tasks
Coding & software development
Tabnine is the privacy-first entry in this category
Tabnine is the privacy-first entry in this category. It runs fully self-hosted or air-gapped, trains only on permissively licensed code, and documents the provenance and indemnification that compliance teams ask about, which puts AI coding assistance inside environments that ban every cloud rival. Its entire audience is teams whose policy or regulation rules out the mainstream assistants and who still want one. The category logic is a constraint filter: in unconstrained environments the mainstream tools win on quality and breadth, so Tabnine reaches a shortlist when deployment is the requirement, full stop. Current suggestion quality deserves a direct test on your own code.
Example tasks
- Provide completions and chat inside air-gapped environments
- Standardise assisted coding across a regulated organisation
- Ground suggestions in private repositories
- Satisfy security review with retention guarantees
- Pilot on a sensitive codebase the cloud tools cannot touch
Limits
In unconstrained environments the mainstream assistants win on quality and breadth; this category's reason to shortlist Tabnine is the deployment constraint, full stop.
Compares
| vs | Pick Tabnine when | Pick the other when |
|---|---|---|
| GitHub CopilotFull comparison → | Tabnine is the privacy-first choice, running fully self-hosted or air-gapped and training only on permissively licensed code with provenance and indemnification documented | the environment is unconstrained and quality and breadth decide it |
| GLM (Z.ai)Full comparison → | Tabnine is for the case where policy or regulation rules out cloud coding assistants and you still want one, providing completions and chat inside air-gapped environments | MIT-licensed weights and a 1M-token context are the reach a real codebase asks for |
| Sourcegraph CodyFull comparison → | Tabnine reaches a shortlist when deployment is the requirement rather than when the estate is large, which is a constraint filter rather than a ranking on capability | the codebase is vast, spread across repos, and context is what assistants lack |
| QwenFull comparison → | Tabnine satisfies the security review itself, arriving with the retention guarantees that decide whether a coding assistant is permitted at all | you want the model layer itself rather than a packaged assistant: open weights you serve and wire into your own agentic workflows |
| DeepSeekFull comparison → | Tabnine grounds its suggestions in private repositories, so the assistance is shaped by the code the organisation actually owns | coding AI spend matters, or you want strong open weights you control |
| Devin CloudFull comparison → | Tabnine can be piloted on a sensitive codebase the cloud tools cannot touch, which is where a regulated shop's evaluation has to happen at all | you have a backlog of well-defined tasks and would rather review PRs than write them |
| Devin DesktopFull comparison → | Tabnine standardises assisted coding across a regulated organisation, which is a rollout decision rather than an editor preference | you want an agentic editor that anticipates rather than waits for instructions |
| Claude CodeFull comparison → | Tabnine puts AI coding assistance inside environments that ban every cloud rival, which is the whole of its case in this category | you want to hand over tasks in a real codebase, not autocomplete inside one |
| Kimi CodeFull comparison → | Tabnine asks to be judged by a direct test of current suggestion quality on your own code rather than on reputation | you want an agent you can adopt without leaving your current tool, as a cost lever inside the stack rather than a migration |
Private, local & self-hosted
Tabnine exists for the environments the cloud assistants cannot serve
Tabnine exists for the environments the cloud assistants cannot serve. On-premises and air-gapped deployment with zero data retention, an audit-ready compliance posture and custom models trained on your private codebase are the offer, and in this category that is the whole case: organisations that cannot let source code leave the network get modern completions and chat inside their own walls, with the compliance paperwork procurement asks about first. Deployment flexibility rather than peak suggestion quality is the product. It runs proprietary models only, so bringing your own frontier model is not an option, the mainstream assistants lead on quality and ecosystem wherever the constraint does not bind, and a current-quality check on your own code is part of any serious evaluation.
Example tasks
- Deploy AI coding fully air-gapped inside your own walls
- Train custom models on the private codebase
- Meet audit requirements with zero-retention guarantees
- Serve regulated teams the cloud assistants exclude
- Keep sovereignty and assistance in the same environment
Limits
If you want the strongest suggestion quality or the broadest ecosystem, the mainstream assistants lead, and Tabnine runs proprietary models only, so bringing your own frontier model is not an option. A current-quality check on your own code is part of any serious evaluation.
Compares
| vs | Pick Tabnine when | Pick the other when |
|---|---|---|
| GitHub CopilotFull comparison → | Tabnine exists for the environments the cloud assistants cannot serve: on-premises and air-gapped deployment, zero data retention, an audit-ready compliance posture and custom models trained on your private codebase | the constraint does not bind, for inline completions and codebase-aware chat across every major editor |
| Ollama | Tabnine is the managed private assistant rather than the assembled one, arriving with the deployment options, retention guarantees and compliance paperwork procurement asks about first | one command pulling a model and a standard API on your own machine is the shape you want, with the coding layer assembled yourself |
| GLM (Z.ai)Full comparison → | Tabnine sells deployment flexibility rather than peak suggestion quality, and will train custom models on the private codebase itself | the MIT licence should turn the jurisdiction question into an infrastructure decision instead |
| QwenFull comparison → | Tabnine serves the regulated teams that cloud assistants exclude, which is a procurement problem before it is a modelling one | self-hosting is a policy decision to own the model layer and you would rather prove the idea on a laptop before buying GPUs |
| DeepSeekFull comparison → | Tabnine deploys AI coding fully air-gapped inside your own walls, with the deployment itself as the product | you want top-tier open weights on your own hardware, not the hosted app |
Where to start
Not sure what to adopt first?
Five quick questions about your job, task and constraints. We'll suggest your top three tools, plus the one to try first.
Alternatives
Same category, different strengths.
Common questions
Is Tabnine free?
No: Tabnine is a paid product, with plans for individuals and teams.
Where does Tabnine fit best?
Tabnine fits best in Software development and Private, local & self-hosted; see its practice notes for how.
Before sharing confidential or personal data, check this tool's data-governance and training policies. They differ between providers and can change.
Last checked: August 2026