AI News
09 Aug 2026
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Discover best VS Code local AI extensions that save credits
best VS Code local AI extensions help you run models locally to cut cloud AI credits and protect code
Looking for the best VS Code local AI extensions to cut Copilot credit use? These five Ollama-powered picks bring chat, code completion, and agent actions to your editor, all on your machine. They can protect your code and reduce cloud costs. Expect trade-offs in setup, speed, and upkeep depending on your hardware and model size.
GitHub’s shift to usage-based Copilot billing makes local AI more attractive. Running smaller coding tasks on-device can save credits for bigger jobs. This guide reviews the best VS Code local AI extensions surfaced by a Marketplace search for “local AI” and highlights what each does well, where it struggles, and who should try it. Note: install counts reflect listings on Aug. 4, 2026, and measure installs, not active users.
Why local AI in VS Code is catching on
– More teams plan to use open-source AI tools, according to Linux Foundation Research. – Cost pressure is rising; thousands of developers now cite AI spend as a pain point. – Ollama has grown fast as a local runtime, making setup simpler on macOS, Linux, and Windows. – Local models can keep code private and reduce cloud tokens, but require RAM, storage, and power.Meet the best VS Code local AI extensions
Local AI Pilot — Ollama (Offline AI)
- Installs: ~16.9K | Rating: 4/5 | Cost: Free
- Engine: Ollama (local by default), optional remote providers
What it does
- Chat, code completion, and contextual actions (explain, fix, review)
- Container mode adds RAG-style doc Q&A, chat history, and caching
- Lets you pick different models for chat and completion
Good to know
- Supports fill-in-the-middle models for better completion
- Container mode can route to OpenAI, Anthropic, Gemini, Cohere, or Mistral; verify settings if you want 100% local
- Larger models need more CPU/GPU and memory
Best for: Developers who want a flexible local-first setup with optional advanced features and the ability to mix models per task.
DeepSeek Offline (Vatsalya Gautam)
- Installs: ~6.2K | Rating: n/a | Cost: Free
- Engine: Ollama with DeepSeek-R1 (focus on deepseek-r1:1.5b)
What it does
- Sidebar chat for offline help, code explanations, and suggestions
- Targets a compact reasoning model for low-resource machines
Good to know
- Requires 8GB RAM (16GB recommended) and ~10GB storage for models
- Windows setup in docs uses WSL2 + Docker + Ollama-in-WSL, though Ollama now has a native Windows app
- Small GitHub repo with few commits; test before relying on it daily
Best for: Tinkerers who want a focused DeepSeek experience and are comfortable with setup steps, especially on Windows.
Ollama Agent — Local AI Coding Assistant (Nishant Unavane)
- Installs: ~5.4K | Rating: 3/5 | Cost: Free
- Engine: Ollama by default; optional OpenAI and Anthropic
What it does
- Chat, opt-in ghost-text completions, quick inline edits
- Code review, test and doc generation, reusable prompts/snippets
- Project indexing and semantic search
Context and control
- Can include terminal output, diagnostics, Git changes, and picked files
- Read mode suggests changes; agent mode can create/edit files and run tasks
- Agent runs capped at eight steps for safety
Best for: Power users who want agent features in a local-first flow. Double-check provider settings if you must keep everything on-device.
Ollama Codex (Parth Kansara)
- Installs: ~4K | Rating: n/a | Cost: Free
- Engine: Ollama (no external API noted)
What it does
- Integrates with VS Code Chat; use @ollama to start
- Suggests fixes, refactors, and config help
- Agent mode can create, edit, and delete files on request
Models and setup
- Recommends Qwen2.5-Coder 14B or Gemma
- Simple stack: VS Code extension → agent layer → Ollama → local model
Best for: Developers who want a purely local chat-and-agent workflow using the native Chat UI, with no cloud fallback.
Local AI Code Completion (chjweb)
- Installs: ~3.2K | Rating: 5/5 | Cost: Free
- Engine: Ollama with Code Llama (7B by default)
What it does
- Focuses on one job: local code completion
- Trigger: Ctrl+Alt+C, accept with Tab, cancel with Esc
- Tune model, temperature, and top-p
Good to know
- Supports many languages; startup and inference can be slow on modest PCs
- Docs are dated (once claimed no Windows support for Ollama); verify compatibility with current VS Code and Ollama
Best for: Minimalists who only want on-demand local completions without chat or agents.
Setup tips for speed and reliability
- Pick the right model size: start small (e.g., 7B–8B) and scale if your hardware allows.
- Use fill-in-the-middle models for stronger completions.
- Check memory: 16GB RAM is a safer floor for multi-file work; more is better.
- Confirm the provider endpoint: keep it pointing to localhost if you need strict local.
- Warm up models: first run loads weights; later prompts are faster.
- On Windows, prefer Ollama’s native app unless an extension requires WSL.
Before you install: what to watch
- Maintenance: Some projects ship updates slowly. Test on a sample repo first.
- Agent safety: Review diffs. Limit write access until you trust a tool.
- Privacy: “Local-first” may still allow cloud providers. Lock settings to local.
- Hardware costs: Local isn’t “free.” You pay in RAM, storage, and power.
- Scope: Marketplace results came from the “local AI” phrase; other good tools may not use that wording.
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FAQ
Q: Which VS Code extensions are highlighted as the top local AI tools?
A: The extensions are Local AI Pilot (Offline AI), DeepSeek Offline, Ollama Agent, Ollama Codex, and Local AI Code Completion. The article notes these five came from a Marketplace search for the exact phrase “local AI” and all rely on Ollama for local model inference.
Q: Do these extensions run models locally or use cloud providers?
A: All five extensions rely on Ollama for local model inference, but some—like Local AI Pilot and Ollama Agent—offer container or provider settings that can route requests to remote services such as OpenAI, Anthropic or Gemini. Developers who need strict local execution should verify provider, host and model settings to ensure data stays on their machine.
Q: How can using local models affect GitHub Copilot usage-based billing?
A: The article explains that GitHub’s shift to usage-based Copilot billing makes local AI appealing because running routine tasks locally can preserve GitHub AI Credits for more demanding work. Offloading chat, summaries and repetitive tasks to local models can therefore reduce cloud token consumption and related costs.
Q: What are the main trade-offs of running local AI in VS Code?
A: Running local models can protect code privacy and cut cloud charges, but it requires hardware resources, storage and power, and larger models demand more memory and processing power. Setup complexity and extension maintenance also vary, so performance and upkeep depend on your machine and chosen models.
Q: Which extension is best suited for minimal on-demand code completion?
A: If you’re comparing the best VS Code local AI extensions, the Local AI Code Completion extension is the simplest entry focused solely on local code completion and uses a 7B Code Llama model by default. Its documentation is dated and startup or inference can be slow on modest hardware, so verify compatibility with current VS Code and Ollama before relying on it.
Q: What hardware and model-size recommendations does the article give for local AI?
A: The article recommends starting with smaller models (for example, 7B–8B) and notes larger models need more CPU/GPU and memory, while DeepSeek Offline specifies at least 8GB RAM (16GB recommended) and about 10GB of storage for model files. It also suggests 16GB RAM as a safer floor for multi-file work and warming up models to improve later prompt speed.
Q: How do agent features differ among these extensions and what safety steps are suggested?
A: Some extensions, notably Ollama Agent and Ollama Codex, support agentic workflows that can read, create, edit or delete files and run multi-step tasks, while others focus on chat or completion only. The article advises reviewing diffs, limiting write access until you trust a tool, and notes that some agent runs are capped (Ollama Agent limits runs to eight steps) to help manage safety.
Q: How should I confirm an extension stays fully local before installing it?
A: Verify the extension’s provider, host and model settings to ensure the endpoint is localhost and that no optional container mode routes requests to remote services, since the article warns container modes can connect to external providers. Also test an extension on a sample repo and check project maintenance and compatibility, because Marketplace install counts reflect installs not necessarily active users and some listings have dated documentation.
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