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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchLocal coding AI runs the model on hardware you control; a cloud coding assistant sends prompts and relevant code context to a provider’s hosted service. Neither approach is automatically cheaper, safer, faster, or better. The right choice depends on your existing computer, model and context size, data policies for the exact plan, agent execution location, and how much usage you expect.
What “local” and “cloud” mean for coding AI
“Local” and “cloud” describe where inference—the generation of the answer or code—takes place. A local assistant loads a model into your computer’s memory and performs inference there. A cloud assistant transmits a prompt, instructions, and some coding context to a provider’s servers, which return the result.
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That label does not tell you where every part of an AI development workflow runs. An editor extension may use a local model while checking for updates online. A local model may call a hosted service for embeddings, authentication, telemetry, or a fallback model. Conversely, a cloud assistant may offer a local command sandbox for agent actions. Treat inference, supporting services, and command execution as three separate questions.
- Inference location: where the language model processes your request.
- Context path: which files, snippets, cursor position, prompts, and conversation history leave your machine.
- Execution location: where an agent runs shell commands, tests, package installs, or other tools.
Ask vendors about all three before calling a system “local” or “private.”
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How agent command execution changes the comparison
An assistant that only suggests text has a different risk profile from an agent that can edit files and run commands. GitHub documents both local and cloud sandboxing for Copilot. Its local sandbox runs commands on your machine with restricted access, is included with a standard Copilot seat, and is off by default. Its cloud sandbox 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 the permissions of your own account.
This means a cloud-inferred assistant can still execute work remotely, while a locally inferred assistant can still execute commands with broad local permissions. Before enabling an agent, identify the working directory, network access, operating-system account, secrets exposure, persistence of the environment, and who pays for compute.
A practical permission checklist
- Confirm whether the agent can read files outside the repository.
- Check whether shell commands run as your normal user, a restricted user, or an isolated container.
- Review network access and whether package managers can download code.
- Remove API keys, cloud credentials, and production secrets from the workspace.
- Require approval for destructive commands, database changes, and pushes to shared repositories.
- Inspect the session after completion so generated files and logs do not retain sensitive data.
Cost: compare total ownership, not a subscription headline
A local model has no mandatory per-request provider bill, but it is not free. Count the computer you already own, any memory or GPU upgrade, electricity, storage, cooling, setup time, model downloads, and maintenance. If the machine is used for other work, allocate only the incremental cost of running the model rather than assigning the entire purchase price to coding.
A cloud assistant usually replaces hardware costs with a subscription, usage allowance, token charges, overages, or separate agent-sandbox billing. The cheapest option depends on your request volume and whether your existing computer can run the target model at an acceptable speed.
A simple total-cost model
For a chosen time period, estimate:
Local total = allocated hardware cost + upgrades + electricity + setup/maintenance time
Cloud total = subscription fees + metered usage + overages + agent/sandbox chargesUse the same period and workload for both sides: number of coding sessions, approximate context length, agent minutes, and whether you need a second machine. Do not claim a payback period unless those assumptions are stated.
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On August 31, 2026, Ollama announced plans that illustrate how hosted access can combine a subscription with usage credits:
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| Plan | Published price | Included usage credits | Credit scope |
|---|---|---|---|
| Pro | $20 per month | $60 per month | Individual account |
| Max | $100 per month | $300 per month | Individual account |
| Team | $500 per month | $1,000 shared | Shared team allowance |
Those figures are the provider’s announcement for that date, not a market-wide benchmark. Check current per-token rates, expiration rules, overage pricing, and whether credits cover the particular model or agent feature you intend to use.
When local can cost more
- You need a new GPU, more system memory, or faster storage to reach usable latency.
- You run the machine continuously and electricity or heat is significant.
- You spend engineering time downloading, quantizing, updating, and troubleshooting models.
- Your local model is too small for the task, so you pay for a second cloud model anyway.
When cloud can cost more
- You send long repository context or high-volume requests every workday.
- An autonomous agent consumes billable sandbox minutes or token overages.
- Your team requires several seats, dedicated retention controls, or enterprise administration.
- A plan’s included credits are lower than your actual usage.
Privacy: “cloud” is not one policy
Data handling depends on the provider, product, account type, configuration, and date. Read the terms for the exact plan rather than applying a consumer policy to a business account or vice versa. Important questions are what is collected, how long it is retained, whether administrators can log it, whether it trains models, and which subprocessors receive it.
Google Gemini Code Assist
Google’s documentation for Gemini Code Assist Standard and Enterprise treats prompts, responses, conversation history, snippets from open and adjacent IDE files, and cursor location as Customer Data. Google describes the service as stateless: prompts and responses are not stored in Google Cloud unless an administrator configures logging. It also says customer data is not used to train models without permission.
Google’s broader Gemini for Google Cloud documentation says prompts and generated responses are processed to serve requests and are not used to train or fine-tune underlying models. It says data is not stored unless instructed otherwise. Because Code Assist gathers relevant open-file and project-file context, inspect IDE and project settings to understand exactly what is selected for a request.
GitHub Copilot
GitHub says Copilot processes prompts and context, generated suggestions, and engagement or usage data. It states that data from Copilot Business and Copilot Enterprise is not used to train its models. That statement is specific to those named plans; do not extend it automatically to every Copilot plan, preview, or access method. Verify the current terms for your account and administrator configuration.
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In an update dated September 5, 2026, Anthropic said its consumer plans—Free, Pro, and Max, including Claude Code—retain inputs and outputs. The same update describes separate organizational configurations with zero-data-retention terms 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.
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What local inference does and does not guarantee
A model that performs inference on your computer can keep the prompt and generated text on that machine, but that alone does not prove that no data leaves it. Extensions may send telemetry, update checks may contact a server, a tool may upload files, and a failed request may fall back to a hosted model. Review network permissions, configuration files, logs, model-download endpoints, and fallback settings. If your policy requires offline operation, test with network access disabled and document which features stop working.
Hardware: there is no universal minimum
Running a coding model locally makes your available system RAM, GPU memory, compute throughput, storage, and power budget directly relevant. The evidence does not establish one defensible minimum GPU or RAM figure for all coding models. A recommendation is meaningful only when it names the exact model, quantization, context length, runtime, and acceptable latency.
Variables that determine the requirement
- Model size: larger parameter counts generally require more memory.
- Quantization: reduced-precision formats can lower memory use, with possible quality or speed trade-offs.
- Context length: a long repository prompt consumes additional memory beyond the model weights.
- Runtime: CPU-only, GPU, and mixed CPU/GPU runtimes have different throughput and memory behavior.
- Concurrency: multiple users or editor sessions need more memory and compute than one interactive session.
- Latency target: occasional completions tolerate slower generation than an interactive agent loop.
Before buying a graphics card for running local language models, select the model and quantization first, then consult that model’s official documentation for memory requirements. If those details are unknown, buying hardware on the basis of a generic “AI GPU” recommendation is guesswork.
How to evaluate a computer you already own
- Choose the coding tasks: autocomplete, chat, repository questions, test generation, or autonomous edits.
- Choose a specific model and quantization rather than comparing vague model families.
- Record available system RAM, GPU memory, storage space, and operating-system support.
- Run a representative repository with the context length you expect to use.
- Measure time to first token, sustained generation speed, editor responsiveness, and thermal or power behavior.
- Decide whether the resulting latency is acceptable before purchasing an upgrade.
A model that technically loads but pauses for every agent step may be less productive than a metered cloud model. Conversely, a modest local model can be ideal for private autocomplete or short explanations where latency and data control matter more than maximum capability.
Capability and workflow trade-offs
Cloud services typically offer managed model updates, account-based synchronization, hosted repositories or IDE integrations, and scalable compute. They can be convenient when a team needs a common configuration without maintaining model files. Their trade-offs are network dependence, provider retention terms, possible rate limits, and recurring or metered costs.
Local systems offer control over model version, network isolation, and when updates occur. They can continue working during an outage and avoid sending source code to a provider when the entire stack is genuinely offline. Their trade-offs are installation effort, hardware limits, model maintenance, and the possibility that a smaller model struggles with broad repository reasoning.
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Do not infer quality from location. The available sources do not provide a controlled benchmark comparing all local and cloud products. Evaluate the model, context window, tools, and workflow you actually use.
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Editor and repository integration questions
- Does the assistant index the whole repository or only open and adjacent files?
- Can you exclude directories such as build outputs, dependency caches, and secrets?
- Does it support your editor, operating system, language servers, and version-control workflow?
- Can you pin a model version locally or select a provider model in the cloud?
- Are generated patches reviewable before they are applied?
- Can administrators enforce retention, logging, and permission settings?
Which approach fits common situations?
| Situation | Usually favors | Reason to verify |
|---|---|---|
| Strict offline or source-code residency requirement | Local inference and local tools | Check telemetry, updates, extensions, and fallback paths. |
| Existing computer with adequate memory and modest coding tasks | Local | Benchmark the exact model and context size; setup time still has a cost. |
| No suitable GPU or need for a large model | Cloud | Calculate subscription, token, and sandbox charges for real usage. |
| Team-wide administration and standardized access | Managed cloud plan | Confirm the plan’s training, retention, logging, and seat terms. |
| Unreliable internet connection | Local or hybrid | Ensure every extension and agent feature works without network access. |
| Autonomous agents that run tests and install packages | Either, with explicit sandboxing | Execution location and permissions matter as much as inference location. |
A decision process that avoids expensive surprises
- Classify your code. Separate public, internal, regulated, customer, and secret material. Set a rule for which classes may leave the machine.
- Define the workload. List languages, repository size, expected context length, daily sessions, autocomplete frequency, and agent actions.
- Select candidate models. For local candidates, record model size, quantization, runtime, and context limit. For cloud candidates, record plan, included usage, overages, and retention.
- Map the data path. Identify files sent, conversation storage, administrator logging, training use, telemetry, and fallback providers.
- Map execution separately. Document whether commands run locally, in a remote ephemeral environment, or with your normal account permissions.
- Run a representative trial. Use the same repository tasks and measure latency, correctness, edit quality, setup friction, and failure recovery.
- Calculate the time-horizon cost. Include hardware allocation, electricity, subscriptions, credits, overages, and sandbox billing.
- Choose a policy, not just a product. Define approval gates, excluded files, secret handling, logging, and a rollback path for generated changes.
Common mistakes and how to correct them
Assuming local means completely private
Why it fails: extensions, telemetry, update checks, or cloud fallback can still transmit data.
Correction: inspect network behavior and disable or block nonessential services; test the workflow offline.
Assuming cloud means unsafe
Why it fails: retention and training rules differ by vendor and plan. Some documented business configurations prohibit training use or support zero-data-retention arrangements.
Correction: read the exact plan terms, administrator settings, and logging configuration before sending proprietary code.
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Buying hardware before choosing a model
Why it fails: memory needs depend on model size, quantization, context, runtime, and concurrency.
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Correction: select the workload and model first, then verify official requirements and benchmark your repository.
Comparing only monthly prices
Why it fails: local electricity and setup effort disappear from the calculation, while cloud overages and agent charges may be hidden behind a seat price.
Correction: estimate a complete cost for the same number of sessions and agent minutes.
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Ignoring command permissions
Why it fails: an assistant can make a harmless suggestion but an agent can delete files, expose secrets, or alter a database.
Correction: use restricted sandboxes, require approvals, and review diffs and command logs.
Generalizing one product’s policy to another tier
Why it fails: consumer, business, enterprise, preview, and API access can have different retention and training terms.
Correction: record the product name, plan, region if relevant, and policy date when approving a tool.
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Choose local coding AI when offline operation, direct control of source code, or predictable use of existing hardware outweighs setup and maintenance. Choose a cloud assistant when you need managed infrastructure, a larger model than your computer can run, or team administration—and when the exact plan’s data terms and costs meet your requirements. A hybrid arrangement is often practical: keep sensitive or routine tasks local, and send approved workloads to a cloud model under a documented retention and execution policy.
The defensible comparison is not “local versus cloud” in the abstract. It is a specific model and runtime, on specific hardware or a specific plan, handling a defined repository, with a documented data path, permission boundary, and cost over a stated time horizon.
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