Choose a local coding model if keeping inference on your own machine, working offline, or controlling the runtime matters most—and your hardware can handle the model. Choose a cloud coding assistant if you prefer hosted inference and a managed editor or agent workflow. Neither option is automatically more private, capable, or affordable; the right fit depends on the specific product, task, and setup.
What do “local” and “cloud” mean for coding assistants?
The terms describe where the model runs, not necessarily where every part of the coding workflow runs.
| Approach | Where inference runs | What that means for you |
|---|---|---|
| Local model | On your computer, when the model and the rest of the chosen workflow are configured to run locally. | You choose and maintain the model runtime, and your computer must have sufficient resources for the task. |
| Cloud assistant | On infrastructure managed by the service or model provider. | The provider handles hosting; you access the assistant through its supported editor, repository, or agent experience. |
| Hybrid workflow | It depends on the model connection you configure. | GitHub documents a bring-your-own-key option for Copilot that can connect to models running locally or hosted elsewhere. That does not by itself establish that every other part of the workflow stays local. |
What happens to your code and prompts?
Privacy depends on the specific product, plan, model provider, settings, and integration—not simply on whether the model is described as local or cloud-based.
Google’s documentation for Gemini Code Assist Standard and Enterprise says the service can process conversation history and IDE context, including snippets from open files, snippets from files adjacent to an open file, and cursor location. GitHub’s documentation describes different model-hosting arrangements; for individual subscribers, interaction data such as prompts, suggestions, and generated code snippets may be used to train and improve models subject to the applicable privacy statement and user settings. Other arrangements described by GitHub differ.
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- Confirm the exact plan and model provider you use.
- Find out which files and surrounding context the tool can send.
- Review retention, training, and privacy controls for that plan.
- For workplace use, check applicable enterprise and regional policies.
- Check whether your editor, extension, or agent makes external calls even when the model itself runs locally.
Can a local model keep up with your coding workload?
There is no general answer based on deployment location alone. Results depend on the chosen model, its configuration and context, the available hardware, the task, and the assistant’s tools. A cloud service may also offer multiple models, so comparing a single local model with a whole cloud service can be misleading.
A 2026 preprint, “Comparing AI Coding Agents: A Task-Stratified Analysis of Pull Request Acceptance,” analyzed 7,156 pull requests across five coding agents. Its reported leaders varied by task type. That is evidence that agent performance can depend on the work being attempted; it is not a controlled local-versus-cloud benchmark and does not establish an overall winner between those categories.
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For a useful comparison, try both options on representative work from your own day-to-day tasks. Include tasks that differ in scope, such as explaining unfamiliar code, making a focused change, and handling a change that touches several files. Judge the resulting code and the time needed to reach an acceptable result, rather than relying on the model label alone.
What hardware and ongoing costs should you consider?
A local model uses your computer’s resources. Ollama’s hardware documentation lists supported NVIDIA GPU families and Apple GPU acceleration through Metal, but that compatibility information does not set a universal minimum GPU or establish that every coding model requires an upgrade. Check the requirements for the particular model and context length, the runtime’s supported acceleration, and the hardware you already own before buying anything.
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Compare total cost over the period you expect to use the tool. For a local setup, account for any hardware purchase, power, setup time, and maintenance. For a hosted assistant, check the current subscription or usage terms for the particular service and plan. There is no universal cheaper choice: the answer depends on your workload, existing equipment, and how you value setup and maintenance time.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should setup and editor integration affect your choice?
With a local setup, you select and maintain the runtime, model, and connections to your editor or agent. Supported hardware acceleration and tool compatibility vary, so a model that runs successfully is not necessarily a drop-in replacement for a managed coding assistant.
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A hosted assistant generally provides a provider-managed model and an integrated workflow, but its available editors, agents, and configuration options are product-specific. Compare the complete workflow you would use—including how it reaches your code and which model it can call—not just the underlying model.
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How can you decide between them?
- Set the data boundary. Identify what code and context may leave your machine, then verify that the product’s plan and settings meet your privacy or workplace requirements.
- Pick representative tasks. Use coding work you actually do, rather than a generic demo, so the comparison reflects your needs.
- Check feasibility. For a local option, verify model, context, and runtime compatibility with hardware you already own. For a hosted option, confirm the current plan and workflow fit.
- Compare the whole experience. Evaluate output quality, time to a usable result, setup effort, and total cost for the same tasks.
- Recheck policies before adoption. Data handling and plan details are product- and settings-specific; confirm the current terms for the exact configuration you intend to use.
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