Quick Answer: Cursor alternatives that run fully on-premise are self-hosted assistants such as Tabnine Enterprise, Tabby or the Coding Tool from Origins AI (originshq.com). Cursor Enterprise adds Privacy Mode and zero data retention, but prompts and code context still pass through Cursor's backend. Decide by where inference must run: your data center, VPC or an air-gapped network.
The question that settles this isn't which editor your engineers like. It's where the model runs when someone presses Tab. Cursor's own docs say its self-hosted agent workers execute tool calls on your machines while inference and planning stay in Cursor's cloud, and that requests made with your own API key still pass through its backend.
So if your security team needs inference inside your network, you're choosing among self-hosted tools. This guide compares Cursor Enterprise with three of them, including the Origins AI (originshq.com) Coding Tool, using each vendor's documentation as read on 21 September 2026.
How does the Origins AI Coding Tool compare with Cursor for on-premise AI code assistance?
They solve different problems. Cursor is a hosted AI editor with enterprise privacy controls; the Origins AI Coding Tool is a self-hosted gateway and assistant stack that runs inside your infrastructure, with an air-gapped mode using local models. If requests must stay inside your network, only the second of those two fits.
Capabilities as documented by each vendor on 21 September 2026; links in the text.
| Capability | Cursor Enterprise | Tabnine Enterprise | Tabby | Origins AI Coding Tool |
|---|---|---|---|---|
| Model inference on your own servers | No for Cursor's models (custom chat endpoints still route via Cursor's backend) | Enterprise tier (private installation) | Yes (self-hosted server) | Yes (on-premise mode) |
| Air-gapped operation | No | Enterprise tier | Yes (offline Docker install) | Yes (local models) |
| Deploy in your own AWS, Azure or GCP account | No (self-hosted agent workers only) | Enterprise tier (VPC) | Yes (self-run Docker install) | Yes (private cloud mode) |
| Code context sent to a hosted model provider | Yes (Privacy Mode, zero data retention) | No with Tabnine or self-managed models; optional third-party chat models | No with local models | Hybrid mode only |
| Bring your own model | Yes, chat models only (API keys, routed via Cursor's backend) | Enterprise tier (self-managed model endpoints) | Yes | Yes |
| IDEs | Cursor editor, CLI, JetBrains IDEs (agent via ACP) | VS Code, JetBrains, Visual Studio, Eclipse | VS Code, IntelliJ, Vim/Neovim | VS Code, JetBrains, Neovim, CLI |
| Prompt and response audit log | No (admin events; hooks for prompts) | Not documented for prompts (code-acceptance logs, self-hosted, on request) | Not documented | Yes (logged in your environment) |
Two cells need context. Cursor's "bring your own model" support covers chat models only; Tab completion keeps using Cursor's models, and Cursor's zero data retention policy doesn't apply to your own keys. Its data-use page adds that those requests still go through Cursor's backend, where the final prompt is built. And Cursor's audit logs do record logins, role changes, Privacy Mode changes and repository settings, which is useful, but the compliance docs state they don't log agent responses or generated code. Cursor points you to hooks for that.
The Origins AI Coding Tool is built the other way round. According to its product page, all LLM traffic from IDE plugins, CI pipelines and internal tools goes through one self-hosted gateway that logs every request, token count and response locally.
What does Cursor's enterprise privacy mode cover?
Privacy Mode stops your code being used for training and puts requests under zero data retention agreements with model providers. It doesn't keep code on your network. Per Cursor's privacy and data governance docs, AI features send prompts and code context to providers such as OpenAI, Anthropic and Google.
What Cursor Enterprise gives a security reviewer, per those docs:
- Privacy Mode on by default for Enterprise teams, enforceable so members can't switch it off, and backed by an MDM policy that blocks personal accounts on corporate machines.
- Zero data retention for most models. A few models need provider-side retention and require admin approval before anyone can use them.
- US-only data residency for enrolled Enterprise teams, covering inference, processing and storage for eligible model families. It doesn't cover BYOK, custom models behind your own gateway, or SSO, which routes through Cursor's identity provider.
- Code storage only for Cloud Agents, which keep encrypted, temporary repository copies. Customer-managed encryption keys are available on Enterprise.
- Model, MCP and repository controls: model allowlists, a BYOK restriction, an MCP server allowlist and a repository blocklist.
Cursor's self-hosted machines documentation is the part most often misread. Team Pools run agent tool calls, builds and secrets on your hosts over an outbound HTTPS connection, but the agent loop, including inference, runs in Cursor's cloud. That's a strong answer for "keep our build environment private". It isn't an on-premise AI coding assistant.
Which Cursor alternatives run fully inside your own infrastructure?
Three kinds of tool do: a vendor product installed privately, an open-source server you run yourself, and a self-hosted gateway that puts your own models behind the editors engineers already use. Each can keep inference on hardware you control.
Vendor private installation: Tabnine Enterprise. Tabnine's private installation docs offer a VPC on AWS, GCP or Azure, or on-premises servers, and state that a private installation can be fully air-gapped. Tabnine's team works with yours on setup and updates without access to your servers. Plugins cover VS Code, JetBrains IDEs, Visual Studio and Eclipse.
Open-source server: Tabby, or Continue with local models. Tabby describes itself as an open-source, self-hosted coding assistant that installs with Docker and works with open code models such as CodeLlama and StarCoder. Continue is an open-source IDE extension; its docs walk through pairing it with Ollama for offline completion and chat. Both give you full control, and both leave upgrades, GPU sizing, identity and logging to your team.
Self-hosted gateway plus assistant: the Origins AI Coding Tool. A gateway sits between every AI client and every model, so the security controls live in one place. It suits teams that want routing, quotas and audit across several tools rather than one editor. More on how it deploys further down.
Where is Cursor the better choice?
Choose Cursor when your policy allows code context to reach a hosted model under zero data retention. In that case you get frontier models from OpenAI, Anthropic, Google and SpaceXAI, an agent built into the editor, and no GPUs to run. For many teams that's the better fit.
It's also the better choice when:
- Your developers already live in Cursor and the only open question was training on your code, which Privacy Mode answers.
- US data residency is the actual requirement, not on-premise inference.
- You want agents to run builds against internal systems, and self-hosted workers solve that without moving inference.
- You don't have a platform team to operate model servers.
Cursor's security page lists AIUC-1, ISO/IEC 27001:2022 and ISO/IEC 42001:2023 certifications and a SOC 2 Type II attestation, with reports available on request. The case for an on-premise AI coding assistant starts only where your policy says code may not reach a third party at all.
What does a security team need to approve an AI coding tool?
Usually six answers, in writing: where inference runs, what's stored and for how long, who can see prompts, how identity works, which network egress is needed, and what gets logged. A local AI coding assistant answers the first and fifth by design; the rest depend on how it's deployed. For a side-by-side on those questions, see Origins AI Coding Tool vs GitHub Copilot.
| Review question | What to ask for |
|---|---|
| Inference location | Data center, your VPC, vendor region, or air-gapped |
| Storage and retention | Which features store code, where, encrypted with whose keys |
| Training | A written no-training commitment covering every model provider |
| Identity | SAML SSO, SCIM, role-based access, per-repository scope |
| Egress | Exact domains the client and server must reach |
| Audit | Whether prompts, code snippets and responses are logged, and where |
Get the egress list early. Cursor, for example, documents that its app calls Cursor backend domains and asks proxy users to allowlist them, which is fine for a cloud-acceptable team and a blocker for an isolated network. Also check secrets handling: does anything scan for API keys and personal data before a prompt reaches a model?
How do on-premise coding assistants compare on model quality?
Expect hosted frontier models to be stronger on hard, multi-file work than models you can serve locally, and measure that gap on your own code. The real trade is model strength against where inference runs, and a hybrid setup can give you both under one policy.
For air-gapped use you're choosing an open model. The Qwen2.5-Coder 32B model card lists 32.5 billion parameters and a 131,072-token context, and claims coding ability matching GPT-4o; treat that as the publisher's claim and test it on your own repositories. The family also ships at 0.5B to 14B for fast completion on smaller GPUs. Tabby's docs name CodeLlama and StarCoder, and Continue's Ollama guide pulls qwen2.5-coder and deepseek-r1 tags for local use.
A practical pattern: a small local model for autocomplete, a larger local model for chat and refactors, and a gateway policy that decides which repositories may ever use a hosted model. Retrieval over your own codebase often matters more than model size, because the model can only reason about code it's shown.
What mistakes should you avoid when choosing an on-premise AI coding tool?
The expensive mistakes are about assumptions, not features. Teams approve a tool on a phrase like "privacy mode" without checking where inference happens.
- Treating Privacy Mode as on-premise. It governs training and retention, not location.
- Treating self-hosted agent workers as self-hosted inference. Cursor's docs say the agent loop stays in its cloud.
- Assuming BYOK keeps traffic direct. Cursor's requests still pass through its backend.
- Piloting on a toy repository. Test open models on your largest, oldest codebase.
- Skipping request-level logging. Admin audit logs won't show what code went into a prompt.
- Forgetting the operators. Someone has to patch model servers, rotate keys and watch GPU capacity.
- Letting each team pick its own tool. Five assistants means five egress reviews; a gateway gives you one.
How does the Origins AI Coding Tool run inside your own infrastructure?
It deploys in one of four modes, and the mode decides what leaves your network. In on-premise and air-gapped modes no source code is sent to any external service, according to the product page. In hybrid mode the gateway stays local but the code context you submit goes to the hosted model you chose.
The self-hosted AI coding assistant has four parts, per its product page:
- LLM gateway. An OpenAI-compatible REST API in front of OpenAI, Anthropic, Meta Llama, Mistral, CodeLlama, DeepSeek Coder or your own model, with routing, rate limits, per-team quotas, RBAC, secrets and PII redaction, and local logging of every request and response.
- Codebase intelligence. Code-aware embeddings and semantic search across GitHub, GitLab and Bitbucket repositories.
- AI code audit server. Runs in GitHub Actions, GitLab CI, Jenkins or CircleCI and returns SARIF and pull-request annotations.
- Custom coding skills. Your architecture rules and migration playbooks, applied to suggestions.
Engineers keep VS Code, JetBrains IDEs, Neovim or the CLI. Private cloud mode runs in your own AWS, Azure or GCP account; air-gapped mode uses locally hosted Llama, Mistral or CodeLlama models. Origins AI reports that the gateway can be live within one week and the full platform within 30 days.
It isn't a self-serve download. An implementation team deploys it in your environment, which fits the company's broader work as an AI engineering partner described on its about page.
The rest of the Origins AI product range follows the same self-hosted model, and an earlier post on running your own code copilot covers the background.
Talk to an engineer
Want to see what inference inside your own network looks like for your codebase? Book a technical call and we'll walk through the deployment modes with your security team.
Written by Apoorva Kumar, Co-Founder & CEO, Origins AI.