“Enterprise on-premise option”
“VS Code, JetBrains and other IDE integrations.”
“Private Kubernetes deployment in your data centre or private cloud.”
“Supports fully air-gapped installations.”
“Commercial product with vendor support.”
“Tabnine also explicitly documents on-premise and completely air-gapped deployments.”
“Self-hosted AI coding assistant server.”
“Code completion and chat capabilities.”
“You operate the inference infrastructure.”
“Integrates with popular developer environments.”
“VS Code and JetBrains integrations.”
“Supports configurable model providers.”
“Can connect to internal model endpoints.”
“Works with locally hosted models through services such as Ollama.”
“Continue + vLLM | Maximum architectural flexibility”
“Self-hosted AI Gateway.”
“Supports models hosted on your own infrastructure.”
“Integrates AI into the GitLab development lifecycle.”
“Supports offline deployment under specific licensing arrangements.”
“GitLab Duo Self-Hosted | Strong integration if you already use GitLab”
“GitLab's documentation is particularly interesting because it explicitly describes a fully self-hosted configuration in which prompts, code inputs and model responses remain inside your infrastructure.”
“Important: Some licensing and billing configurations still require external connectivity.”
“Self-hosted Sourcegraph Enterprise.”
“Repository-wide code search and contextual assistance.”
“Supports enterprise IDE integrations.”
“Can use a customer-controlled model provider.”
“Important: Self-hosting Sourcegraph alone does not guarantee that Cody requests stay internal.”
“You don't need to use OpenAI, Anthropic or Google cloud APIs.”
“You don't need to use OpenAI, Anthropic or Google cloud APIs.”
“You don't need to use OpenAI, Anthropic or Google cloud APIs.”
“Devstral | Agentic software engineering”
“DeepSeek Coder | Code generation and reasoning”
“Codestral | Code completion and programming assistance”
“You don't necessarily have to give up the developer experience of tools like GitHub Copilot or Cursor.”
“StarCoder2 | Code completion and open-source experimentation”
“You don't necessarily have to give up the developer experience of tools like GitHub Copilot or Cursor.”
“For inference, I'd start with vLLM if you're building a shared GPU service, or Ollama for a smaller proof of concept.”
“For inference, I'd start with vLLM if you're building a shared GPU service, or Ollama for a smaller proof of concept.”
“Qwen Coder | Code completion, chat, code generation”
“For inference, I'd start with vLLM if you're building a shared GPU service, or Ollama for a smaller proof of concept.”
“For inference, I'd start with vLLM if you're building a shared GPU service, or Ollama for a smaller proof of concept.”