The Tool Desk
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Local coding AI runs the model on hardware you control; a cloud coding assistant sends your prompt and relevant code context to a hosted service. Neither option is automatically cheaper, faster, or more private. The right choice depends on the computer you already own, your workload, each plan’s data terms, and where an agent is allowed to execute commands.
What “local” and “cloud” mean for coding AI
Inference location is the first distinction. With a local assistant, model weights and inference runtime operate on your workstation or another computer you administer. With a cloud assistant, the provider’s infrastructure performs inference after receiving your prompt and whatever repository, editor, or file context the integration sends.
Agent execution is a separate decision. An assistant can infer in the cloud while running shell commands in a local sandbox, or infer in the cloud and execute in a remote sandbox. Treat “where the model runs” and “where commands run” as two independent questions.
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Inference location
- Local inference: code and prompts can remain on your machine during inference, subject to the runtime, extensions, telemetry, update checks, and any configured cloud fallback.
- Cloud inference: the provider receives prompts and selected context. Retention, training use, logging, and administrative controls depend on the product and plan.
Command location
GitHub’s Copilot documentation describes both local and cloud sandboxing. Local sandboxing runs commands on your machine with restricted access, is included with a standard Copilot seat, and is off by default. Cloud sandboxing runs a session remotely in an isolated, ephemeral Linux environment and is billed according to usage. If local sandboxing is not enabled, commands run with your normal user-account permissions. A local model does not, by itself, make an agent safe: shell permissions still need to be constrained.
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Cost: compare total ownership, not the subscription sticker
Cloud pricing is easy to see but can hide usage charges. Local inference has no required per-request provider fee, yet the computer, electricity, setup time, maintenance, and possible upgrade are real costs. Compare both over the period you expect to use the assistant.
Cloud cost components
- Recurring seat or plan fee.
- Included tokens, requests, or usage credits.
- Overage pricing when included usage is exhausted.
- Separate charges for remote agent sessions or sandboxes.
- Any additional team, administration, or enterprise controls required by your organization.
Local cost components
- Hardware you already own, valued against other uses rather than treated as free.
- Optional GPU, memory, storage, or cooling upgrades.
- Electricity while generating tokens and while the machine is idle for a long session.
- Installation, model downloads, driver work, updates, and troubleshooting.
- The productivity cost of slower responses if the chosen model exceeds your computer’s practical capacity.
A dated cloud pricing example
In an August 31, 2026 announcement, Ollama listed these plans:
| Plan | Published price | Included monthly usage | Billing scope |
|---|---|---|---|
| Pro | $20 per month | $60 in usage credits | Individual plan |
| Max | $100 per month | $300 in usage credits | Individual plan |
| Team | $500 per month | $1,000 in shared usage credits | Team plan |
These are published figures for that date, not a market-wide price comparison. Check the provider’s current terms for token rates, credit expiration, overages, and model availability before calculating a budget. The announcement states zero data retention for those hosted requests, but that policy applies to that service arrangement and should not be generalized to every cloud assistant.
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How to estimate a local break-even point
- Record the one-time hardware cost you would actually incur, including upgrades.
- Estimate monthly electricity from the computer’s additional power draw and your expected hours of inference.
- Add a value for setup and maintenance time.
- Estimate the equivalent cloud plan, including overages and agent-sandbox charges.
- Divide the local one-time cost by the monthly cloud cost avoided, then test the result against different usage levels.
Do not claim a payback period without stating the model, response volume, hardware price, electricity rate, and cloud plan used. A lightly used workstation may never recover an upgrade cost; a heavily used existing machine can make local inference attractive even without a formal subscription comparison.
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Privacy: inspect the exact plan and data path
“Cloud” is not one privacy posture, and “local” is not a guarantee that no bytes leave the computer. Read the terms for the exact product, tier, account type, retention setting, and integrations you will use.
What a cloud assistant may receive
Typical coding integrations can transmit the prompt, generated response, open-file snippets, adjacent-file or project context, conversation history, cursor location, tool results, and usage or engagement data. The amount depends on the editor extension and its context-selection rules.
Google Gemini Code Assist
Google’s documentation for Gemini Code Assist Standard and Enterprise classifies prompts, responses, conversation history, IDE file snippets, and cursor location as Customer Data. Google says the service is stateless and does not store prompts and responses in Google Cloud unless an administrator configures logging. It also says customer data is not used to train models without permission. Separate Google Cloud documentation says prompts and generated responses are processed to serve requests, are not used to train or fine-tune underlying models, and are not stored unless instructed otherwise. Verify which statement applies to your edition and logging configuration.
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GitHub Copilot
GitHub says Copilot processes prompts and context, generated suggestions, and engagement data. Its documentation states that Business and Enterprise customer data is not used to train GitHub’s models. That statement is specific to those plans; do not extend it to every Copilot plan, account type, or access method without checking current terms.
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Claude and Claude Code consumer plans
Anthropic’s September 5, 2026 update says consumer Free, Pro, and Max plans, including Claude Code, retain inputs and outputs. The same update describes separate organizational zero-data-retention configurations and exceptions for covered models. Some retained data may be deleted after 30 days, subject to safety flags or legal-retention requirements. Consumer, commercial, and zero-retention arrangements must therefore be evaluated separately.
What local inference does—and does not—protect
A model running entirely on your computer can keep inference traffic off the provider’s servers. That protection can be defeated by an extension that uploads telemetry, a runtime that checks a hosted API, automatic model updates, a cloud fallback, or an agent tool that sends command output elsewhere. Before promising that data never leaves the machine, map every component:
- Model files and inference runtime.
- Editor extension and its telemetry settings.
- Repository indexing, crash reporting, and update checks.
- Agent tools, MCP-style connectors, and external APIs.
- Operating-system accounts, network permissions, and backup software.
Privacy questions to answer before adoption
- Which exact files and cursor context are transmitted for a request?
- Are prompts, responses, or tool traces retained, and for how long?
- Are they used for model training, abuse monitoring, or human review?
- Can an administrator disable logging or enforce a zero-retention mode?
- Does the provider’s policy cover your plan, region, and API or IDE access path?
- Can the assistant invoke a cloud model when the local model fails?
Hardware: there is no universal RAM or GPU minimum
Local coding performance depends on the exact model, parameter size, quantization, context length, runtime, batch settings, and desired response latency. The reviewed documentation does not establish one defensible minimum GPU, VRAM, or RAM figure for all coding models. Buying a graphics card on an arbitrary threshold is therefore poor advice.
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- System memory: holds the operating system, editor, repository tools, runtime, and parts of the model that do not fit in accelerator memory.
- GPU memory: can hold model weights and working context for GPU-accelerated runtimes; insufficient capacity may force CPU or partial offload.
- Compute throughput: affects token generation speed and interactive latency.
- Storage: model files, caches, indexes, and multiple quantized variants consume disk space.
- Power and cooling: sustained generation can raise energy use, fan noise, and heat.
- Context capacity: large repositories and long conversations require more memory even when the model’s parameter count is unchanged.
How to select hardware responsibly
- Choose the coding model and quantization first.
- Confirm its official runtime, supported operating systems, and memory guidance.
- Set a context-length target based on your repository and preferred prompt size.
- Decide whether CPU-only, GPU-only, or mixed offload is acceptable.
- Check expected tokens-per-second or latency in tests for your exact hardware rather than relying on a different model’s benchmark.
- Leave headroom for the editor, language servers, tests, containers, and browser tabs running alongside the model.
If the model, quantization, context length, or latency target is not yet known, consult the model’s official documentation before purchasing a GPU or memory upgrade. Hardware recommendations that omit those variables cannot be reliable.
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Workflow and agent safety
A coding assistant can suggest text without executing anything, or it can act as an agent that edits files, runs tests, installs packages, and executes shell commands. The second workflow has a larger security boundary regardless of inference location.
Local execution
When commands run locally, the agent may inherit your user permissions, environment variables, filesystem access, credentials, and network access. Use a dedicated account, least-privilege permissions, a disposable checkout, and an explicit approval step for destructive commands. A local sandbox can restrict access, but it must be enabled and correctly configured.
Cloud execution
A remote sandbox can isolate commands from your workstation, but repository contents and command results still cross the provider boundary. Review what is uploaded, how long the session persists, which network endpoints are reachable, and whether usage-based billing applies. An ephemeral Linux environment reduces local blast radius; it does not eliminate data-governance questions.
A safer agent checklist
- Start with read-only repository access.
- Require confirmation before file deletion, dependency installation, database changes, or network calls.
- Keep secrets out of prompts and environment variables exposed to the agent.
- Run generated commands in a disposable branch, container, or virtual machine when practical.
- Inspect diffs and test output before merging.
- Record which tools can execute locally and which send data to hosted services.
Quality, speed, and integration trade-offs
There is no controlled benchmark in the available evidence that ranks all local and cloud assistants by code quality or latency. In practice, your result is shaped by the model selected, context retrieval, editor integration, and hardware or network conditions.
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Local strengths
- Predictable operation when internet access is unavailable.
- Control over model files, runtime versions, and update timing.
- Potentially lower marginal cost when you already own suitable hardware and use the model heavily.
- A path for repositories that cannot be sent to a hosted service, provided every companion component is also local.
Local limitations
- Limited model choice or slower generation on modest hardware.
- Responsibility for drivers, quantization, upgrades, monitoring, and troubleshooting.
- Large contexts can exhaust memory and reduce responsiveness for the rest of the development environment.
- Extensions or tools may still contact cloud services unless explicitly disabled.
Cloud strengths
- Access to provider-managed models without buying or maintaining accelerator hardware.
- Fast startup and broad editor, repository, and team integration.
- Optional remote sandboxes that separate command execution from your workstation.
- Usage-based plans that can be economical for intermittent work.
Cloud limitations
- Ongoing subscription, token, overage, or sandbox charges.
- Dependence on network availability and provider service limits.
- Provider-specific retention, training, review, and regional-processing terms.
- Changes to model availability, pricing, or policy outside your direct control.
Which approach fits your situation?
Choose local-first when
- Your organization prohibits sending source code to external services.
- You already have hardware that meets the chosen model’s documented requirements.
- You need offline operation or control over updates and runtime versions.
- Your usage is high enough that recurring hosted fees and overages dominate the calculation.
Choose cloud-first when
- You need a capable assistant immediately without buying or tuning hardware.
- Your work is intermittent, so a subscription or metered plan is cheaper than an upgrade.
- Team administration, shared policies, and integrated remote environments matter more than offline access.
- Your organization has reviewed and accepted the selected plan’s data terms.
Use a hybrid workflow when
- Routine, sensitive, or offline tasks stay on a local model while difficult tasks use an approved cloud plan.
- Inference is cloud-hosted but command execution is restricted to a local or remote sandbox chosen for the repository’s risk profile.
- You maintain separate editor profiles so cloud fallback cannot occur accidentally on confidential projects.
A practical evaluation procedure
- Classify the repository. Mark source, credentials, personal data, regulated data, and third-party code that cannot be uploaded.
- Define the workload. Separate autocomplete, code explanation, refactoring, test generation, and autonomous agent tasks; they need different context sizes and latency.
- Pick candidate models. For local use, record parameter size, quantization, runtime, and context limit. For cloud use, record plan, included usage, retention, and training terms.
- Map the data path. Identify files, prompts, conversation history, telemetry, tool output, backups, and fallback APIs.
- Measure on representative tasks. Use the same repository, prompts, tests, and acceptance criteria. Do not compare a local small model with a cloud large model and call the result a location benchmark.
- Price the complete workflow. Include hardware, power, setup time, subscriptions, credits, overages, and sandbox sessions.
- Set permissions. Start read-only, add approvals, and isolate execution before allowing autonomous edits or commands.
- Review after a trial period. Check actual token use, latency, rejected requests, data-policy changes, and developer time saved.
Common mistakes and fixes
“Local means nothing leaves my machine.”
Fix: audit extensions, telemetry, update checks, agent connectors, backups, and fallback APIs. Verify network behavior rather than relying on the model’s location.
“Cloud is automatically unsafe.”
Fix: read the plan-specific retention, training, logging, and administrator controls. Google, GitHub, and Anthropic publish materially different terms across products and account types.
“A GPU with a certain amount of VRAM will run any coding model.”
Fix: select the model and quantization first, then follow its official memory and runtime guidance. Context length and offload strategy can change the result.
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Fix: include usage credits, overages, remote-agent billing, and team charges. The same applies locally to hardware, power, setup, and maintenance.
“A sandbox makes every agent action harmless.”
Fix: check filesystem, network, credentials, and persistence boundaries. Review diffs and command output even when execution is remote or isolated.
Bottom line for a buying decision
Choose local coding AI when control, offline operation, or data locality outweighs hardware and maintenance costs—and only after confirming that the entire stack, not just the model, stays local. Choose a cloud assistant when you value immediate access, managed infrastructure, and team integration, after verifying the exact plan’s retention and training terms and budgeting for usage and sandbox charges. For many teams, a deliberate hybrid setup is the most practical answer: keep sensitive or offline work local, use approved cloud capacity for demanding tasks, and treat agent execution permissions as a separate security decision.
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