Choose a local coding model if keeping inference on your own machine, working offline, or controlling the runtime matters—and your hardware can handle your tasks. Choose a cloud coding assistant if you prefer hosted inference and a managed editor or agent workflow. Neither is automatically more private, faster, better, or cheaper: compare the exact data flow, results on your work, total cost, setup, and integrations.
What “local” and “cloud” mean in a coding workflow
A local model runs inference on your computer. That can keep inference on the machine, but it does not guarantee that every part of the workflow is local: an editor extension, agent, or other connected service may still make external calls. Check the runtime and each integration you use.
A cloud assistant sends requests to hosted infrastructure. Depending on the product, those requests may include code and other IDE context, not just the question you typed. The provider manages the inference hardware and usually more of the service workflow.
There can also be a hybrid arrangement. GitHub documents a bring-your-own-key option for Copilot that can connect to models running locally or hosted elsewhere. That changes the model connection, but it does not by itself establish that the whole workflow stays on-device; verify what the editor and integration send.
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How the options compare
| Decision factor | Local inference | Cloud inference |
|---|---|---|
| Where inference runs | On the user’s machine, if the chosen model and workflow are configured locally. | On provider-managed infrastructure. |
| Privacy and governance | Inference can remain on-device, but connected tools may still transmit data. | Prompts or code context may be processed by a service or model provider; handling varies by product, plan, provider, and settings. |
| Quality | Depends on model choice, quantization, context, hardware, and task. | Depends on the assistant and model selected; some services offer multiple models. |
| Hardware and connection | Requires enough local resources for the chosen workload. A network connection may still be needed for other parts of the workflow. | Inference hardware is managed by the provider; the user still needs a client device and network connection. |
| Cost | May include hardware, power, setup, and maintenance; usage cost depends on the setup. | May involve a subscription or usage charges. Current prices are not stated in the cited documentation; check the actual plan. |
| Setup and maintenance | You choose and maintain the runtime, model, and integrations. | The provider manages hosting and much of the service workflow. |
What happens to your code and prompts?
Do not assume that every cloud assistant trains on submitted code, or that every local setup keeps all data private. Policies differ. GitHub’s documentation on Copilot model hosting describes distinct provider arrangements and says interaction data from individual subscribers—including 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 in that documentation differ.
Google’s Gemini Code Assist Standard and Enterprise security and privacy documentation identifies context the service may process. Examples include conversation history, snippets from open files, snippets from files adjacent to an open file, and cursor location.
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Before using either kind of assistant with sensitive work, check:
- The exact product plan and model provider.
- Which files, snippets, history, and other context the tool sends.
- Retention, training, and opt-out settings in the applicable terms.
- Any enterprise or regional rules that apply to your account.
- Whether local editor extensions, agents, or other integrations make external calls.
For a team, make these checks against the actual account configuration and governing terms—not just the assistant’s general product description.
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Will a local model keep up with your coding work?
There is no single answer based on deployment location. A local model’s results depend on the model, its quantization, the available context, and the task; a cloud assistant’s results depend on its service and selected model. Try both on representative work—such as a change in your codebase, a debugging task, or a review—and judge correctness and usefulness rather than treating “local” or “cloud” as a quality rating.
A 2026 preprint, “Comparing AI Coding Agents: A Task-Stratified Analysis of Pull Request Acceptance,” analyzed 7,156 pull requests across five agents and reported different performance leaders for different task types. That is evidence that results can vary by task, not a controlled comparison of local coding models against cloud assistants. It does not establish a winner for this choice.
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What hardware does local inference require?
Requirements depend on the model and workload; there is no universal minimum or ideal GPU established for all coding models. Ollama’s hardware support documentation lists supported NVIDIA GPU families and Apple GPU acceleration through Metal. That shows supported acceleration can matter, not that everyone needs to buy a GPU.
Before choosing hardware—or upgrading—check the requirements of the specific model and runtime you plan to use, including memory, context length, and acceleration compatibility. Compare those requirements with the computer you already own. A GPU for running local coding models is a conditional part of the cost, not a default recommendation.
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How to choose for your work
Choose local inference when
- Your policy or workflow calls for inference on your own machine, and you have verified that connected tools do not undermine that requirement.
- Offline availability or control over the model and runtime is important to you.
- Your existing hardware can run the chosen model at a useful level, and you are comfortable managing setup and maintenance.
Choose a cloud assistant when
- You prefer hosted inference and a managed editor, repository, or agent experience.
- You do not want to manage a local runtime or provide inference hardware.
- The service’s data handling and terms meet your requirements after checking the exact plan, provider, and settings.
Compare the full cost and workflow
For either option, estimate cost over the period you expect to use it. Include local hardware, power, setup, and maintenance where relevant, and the actual subscription or usage charges for a hosted service. Then compare integration with your editor and agent workflow: a model that works in isolation may not fit the tools you rely on. There is no universal cheaper choice without a specific workload, setup, and current plan pricing.
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