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Local AI is the better fit when code must stay on a device or work without internet and your hardware can handle the chosen model. Cloud AI is usually easier to start, can provide service-backed editor features, and avoids buying a capable GPU, but it sends prompts and code context to a provider whose retention, training, and pricing rules depend on the exact plan and model. A hybrid workflow often gives the best control: keep sensitive or offline work local and use hosted models for tasks that benefit from larger infrastructure. The right choice depends on your data route, editor features, request volume, existing hardware, and offline requirements—not on a blanket claim that one side is always cheaper, safer, faster, or more accurate.
What is the difference between local, cloud, and hybrid AI coding?
A local coding assistant runs inference on your computer, while a cloud assistant sends prompts and relevant context to hosted infrastructure. A hybrid setup lets an editor or user choose different routes for different tasks; one application can therefore keep one request local and send another to a service.
| Aspect | Local execution | Cloud-hosted execution | What to verify |
|---|---|---|---|
| Data route | Inputs and outputs can remain on the computer when the selected model is genuinely local. | Prompts and relevant code context travel to a service or model provider for inference. | Which model route is selected for each feature, including chat, completion, indexing, tool calls, and telemetry. |
| Privacy | Less third-party exposure for inference content, subject to software telemetry and integrations. | Rules differ by provider, model, plan, retention period, and administrator settings. | The exact policy and account type, not just the product name. |
| Cost | No per-token inference charge for a model running on owned hardware in the cited Ollama description, but hardware and electricity still cost money. | Subscriptions, included credits, metered tokens, and possible overages create recurring costs. | Your request volume, selected models, existing hardware, power, and replacement assumptions. |
| Offline use | Can continue without an internet connection after the model and integration are installed. | Hosted inference requires network access. | Whether your editor’s surrounding features also require online services. |
| Capabilities | Depend on the local model, quantization, memory, and integration. | Can include provider-backed context, indexing, and other service features. | Feature-by-feature support rather than a general “local” or “cloud” label. |
| Performance | Changes with model size, context, quantization, acceleration, and concurrent requests. | Changes with model, service capacity, network latency, plan, and task. | There is no neutral, apples-to-apples benchmark in the cited material. |
Which option protects source code better?
Privacy depends on the actual data path and terms. A local route can keep model content on the device, but a cloud route is not automatically unsafe or automatically used for training; the provider, plan, model, and settings determine what happens.
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Ollama’s privacy policy, updated in March 2026, states that for local runs it does not collect, store, transmit, or have access to prompts, responses, model interactions, or other content processed locally. The same policy says cloud-hosted requests are processed transiently and are not retained beyond the time needed to fulfill a request, and that inputs and outputs are not used to train its models. That statement is limited to model content: the policy also describes basic account information and limited device or usage metadata. Read the current wording at Ollama’s privacy policy.
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Why a cloud service’s individual and business rules can differ
GitHub’s current Copilot material separates individual plans from business and enterprise access. GitHub says interaction data from Copilot Free, Pro, and Pro+ users may be used for model training unless the user opts out. Its plan documentation describes different retention defaults for business and enterprise customers, depending on how access is provided. The March 25, 2026 announcement says: “From April 24 onward, interaction data—specifically inputs, outputs, code snippets, and associated context—from Copilot Free, Pro, and Pro+ users will be used to train and improve our AI models unless they opt out.” See the GitHub policy announcement and the current plan page for the applicable account terms.
Model-provider terms can add another layer. GitHub’s hosting documentation identifies cases in which particular Anthropic preview models retain prompts and outputs by default unless eligible enterprise zero-retention access is enabled. A cloud subscription therefore does not describe one universal privacy rule.
How to audit a coding assistant before using a sensitive repository
- Identify every route. Check whether chat, inline completion, codebase indexing, embeddings, terminal tools, and error reports use the local model or a hosted service.
- Match the policy to your account. Read the terms for your individual, business, or enterprise plan and for the selected model provider.
- Check training and retention controls. Confirm opt-out or administrator settings and whether they apply to all features, not just chat.
- Inspect context scope. Determine which files, chat history, tool output, and repository metadata can be attached to a request.
- Test with non-sensitive code. Record the network behavior and feature route before allowing proprietary or regulated material.
These checks matter even in a mostly local setup: downloaded software, sign-in metadata, telemetry, extensions, and repository integrations can have their own data flows.
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There is no universal break-even point. Local inference avoids a per-token charge for the model running on hardware you already own, but buying or replacing that hardware and paying for electricity changes the calculation. Cloud pricing is easier to start but depends on request volume, model choice, included credits, and overage rules.
Published Ollama hosted-plan figures
Ollama’s August 31, 2026 pricing announcement lists the following hosted plans. The amounts are published examples, not permanent price guarantees; usage is metered at the stated per-token rates.
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| Plan | Published price | Included monthly usage credits | Qualification |
|---|---|---|---|
| Pro | $20 per month | $60 | Ollama’s August 31, 2026 announcement. |
| Max | $100 per month | $300 | Ollama’s August 31, 2026 announcement. |
| Team | $500 per month | $1,000 shared usage for unlimited users | Shared allowance in the same announcement. |
The announcement also describes a free tier with a starter amount of hosted usage and says local models run on the user’s hardware. See Ollama’s transparent-pricing announcement for the dated figures and metering details.
Published GitHub Copilot plan prices
On the GitHub plan page accessed October 25, 2026, listed prices are Free at $0 per month, Pro at $10 per month, Pro+ at $39 per month, and Max at $100 per month. AI-credit allowances and additional usage are separate parts of that page, and model choice and interaction complexity affect credit consumption. Prices and allowances can change, so check the live GitHub plans page before committing.
How to calculate your own total cost
- Start with existing hardware. If a suitable computer is already available, its purchase cost may be sunk; if not, include the machine, GPU, memory upgrades, and storage.
- Estimate workload. Count daily requests, average context size, long-running agents, and concurrent users. A few short questions have a different profile from repeated repository-wide analysis.
- Include operating costs. Account for electricity, cooling, maintenance, and the value of tying up a workstation while inference runs.
- Price the cloud route correctly. Include the subscription, included credits, model-specific rates, and any additional-usage charges.
- Compare like with like. A local small model and a hosted large model may deliver different capabilities, so a lower invoice is not necessarily a lower cost for the work.
The available figures do not support a general break-even number or an independent total-cost-of-ownership study.
How much hardware does a local coding model need?
Memory is the first practical constraint, but model size, quantization, context length, acceleration support, and concurrency all matter. A vendor’s recommendation for one model should not be treated as a universal minimum for every coding assistant.
A bounded example: Gemma 4 E2B
Ollama’s quickstart lists a Gemma 4 E2B download of about 7.2 GB and recommends 8 GB of available VRAM or Mac unified memory. With less VRAM, the system can use regular RAM, but responses may be slower. This is guidance for that specific example, not a floor for larger coding models. Consult the Ollama quickstart for the model-specific details.
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Why context length changes the requirement
Long prompts, repository files, retrieved snippets, and conversation history occupy memory in addition to the model weights. Ollama’s context-length documentation says memory needs rise with context length and recommends at least 64,000 tokens for coding tools. Its defaults vary with available VRAM, so a model that starts successfully may still become impractical when given a large codebase or several simultaneous requests. See Ollama’s context-length documentation.
What graphics hardware is supported?
Ollama’s current GPU documentation lists multiple supported NVIDIA families and other hardware through supported backends. Driver requirements and the supported list change; use the current GPU support page rather than relying on an old buying list.
A graphics card with 24 GB of VRAM can be an optional path for larger models, longer contexts, or more concurrency, but it is not a universal requirement. The documented 8 GB example demonstrates why model compatibility must be checked first.
Local-hardware checklist
- Confirm free VRAM or unified memory after the operating system and editor are running.
- Check the model’s download size and whether your chosen quantization fits.
- Set the context length for the repository and tool workflow you actually use.
- Verify a supported GPU backend and current drivers.
- Decide whether slower RAM fallback is acceptable for occasional work.
- Test one request, a long file, and concurrent requests before standardizing the setup.
Can a local coding assistant work offline?
Yes, a local model can run without an internet connection once the model and integration are present. Offline operation does not guarantee that every editor feature works: some functions depend on a hosted service even when chat itself is local.
What VS Code documents for local BYOK models
Visual Studio Code documents local “bring your own key” model use without a GitHub account, Copilot plan, or internet connection. In that path, however, local models cannot currently provide inline suggestions, and features relying on Copilot services—such as semantic search and embeddings—are unavailable offline. The exact limitations are editor-specific; read VS Code’s AI language-model documentation for the current behavior.
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Planning an offline workflow
- Prepare while connected. Install the editor integration, download the model, and verify that the required runtime and drivers work.
- Choose local for each supported feature. A local chat model does not automatically make indexing, completion, or tool services local.
- Define a fallback. Decide whether work pauses, uses a smaller local model, or waits for a connection when an unsupported feature is needed.
- Test with the network disabled. Confirm that prompts, file retrieval, and the editor functions you rely on still operate.
- Protect the model files. Local storage, backups, and logs can still expose code or prompts if the computer is shared.
Which route is faster or more capable?
“Local” and “cloud” do not predict quality or speed by themselves. Local latency depends on the model, quantization, memory bandwidth, context, and concurrent load; cloud latency depends on the hosted model, service capacity, network, plan, and task. The cited material contains no neutral benchmark on the same codebase and hardware, so a universal winner would be misleading.
When local can feel better
- Network round trips are expensive or unavailable.
- The selected model fits comfortably in memory and the workload uses short contexts.
- Keeping prompts and code on the device is a priority.
- You need predictable access during travel, field work, or an outage.
When cloud can feel better
- You need a capable model without purchasing or configuring local acceleration.
- Your editor relies on hosted indexing, embeddings, or other service-backed features.
- The workload is occasional and does not justify dedicated hardware.
- You can accept network dependence and the provider’s terms for the selected plan and model.
How should you choose between local, cloud, and hybrid?
Choose by threat model and workload rather than by marketing category. The following decision sequence keeps the important constraints visible.
Choose local first when
- Offline operation is mandatory.
- Repository content cannot leave a controlled device.
- You already have enough memory and supported acceleration for the target model and context.
- You accept that some editor services, such as the documented VS Code inline suggestions, may be unavailable.
Choose cloud first when
- You want to avoid buying a GPU or tuning a local runtime.
- Hosted editor capabilities matter more than offline operation.
- Your request volume fits the subscription and credit rules.
- You have reviewed retention, training, provider, and administrator settings for the exact account and model.
Choose hybrid when
- Some repositories are sensitive while others benefit from hosted capabilities.
- You want local operation during travel but cloud capacity at a desk.
- You can configure explicit routes per feature and communicate those rules to the team.
- You need a fallback when local memory, context, or speed is insufficient.
A practical pilot
- Select one representative repository and document its sensitivity, average file size, and largest expected context.
- Run the same small set of tasks locally and through the intended cloud plan, recording model, context, latency, failures, and output review time.
- Measure actual token or credit use for a normal week rather than extrapolating from a single prompt.
- Disable networking and repeat the local tasks to verify the offline path and identify missing editor features.
- Review logs, telemetry, and provider settings before expanding to production code.
This pilot produces a workload-specific decision without claiming that one architecture wins for every developer.
What are the main failure modes?
The “local” label hides a cloud feature
An editor may run chat locally while sending embeddings, semantic indexes, completion requests, or tool output to a service. Check each feature’s route instead of trusting the application name.
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The model fits, but the context does not
A successful launch with a short prompt does not prove that a repository-wide task will fit. Reduce context, use retrieval selectively, lower concurrency, or move that task to a hosted model with suitable capacity.
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Cloud usage exceeds the allowance
Credit consumption changes with model and interaction complexity. Set usage alerts where available, review the plan’s additional-usage terms, and reserve expensive models for tasks that need them.
Offline chat works but completion disappears
That behavior is documented for local BYOK in VS Code: inline suggestions and service-dependent semantic features are not supplied offline. Keep a separate completion route or adjust expectations for disconnected work.
Privacy assumptions outlive the policy
Plans and provider terms change. Recheck the current policy whenever an account, model, editor integration, or organization setting changes, especially before placing a sensitive repository on the service.
Frequently Asked Questions
Does local AI guarantee that no code ever leaves my computer?
No. Inference content can remain local when the selected model truly runs on the device, but extensions, sign-in systems, telemetry, backups, and editor services may have separate network paths. Audit those integrations as well as the model runtime.
Do I need a 24 GB graphics card for local coding models?
No universal requirement exists. Ollama’s documented Gemma 4 E2B example recommends 8 GB of available VRAM or Mac unified memory; 24 GB can be useful for larger models or contexts, but compatibility and workload should determine the purchase.
What happens to local AI when the internet is disconnected?
A downloaded local model can continue generating responses, but editor features that depend on hosted services may stop working. VS Code specifically documents unavailable offline semantic search, embeddings, and inline suggestions through its local BYOK path.
Are cloud coding-assistant subscriptions unlimited?
Not necessarily. Published plans can combine a subscription with model-specific credits or metered usage, and interaction complexity affects consumption. Check the current allowance and additional-use terms for the exact plan before estimating monthly cost.
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