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A free hosted coding assistant is usually the better fit when you need immediate editor integration and your monthly workload fits its allowance. A self-hosted server is the stronger fit when local processing and administrative control outweigh installation, model-management and hardware work.
There is no universal winner. Compare usage limits, privacy, setup effort, hardware and context, and day-to-day workflow against the codebase and editor you actually use. “Free” describes a hosted plan, not guaranteed unlimited work; “self-hosted” describes where inference runs, not a promise of zero cost or automatic privacy for every component.
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GitHub’s current Copilot Free plan is listed at $0 and includes 2,000 code completions per month, selected model access and a limited allowance of GitHub AI Credits for chat and agent features. A local stack built around Ollama can keep inference on a computer or server you control, but you must install the runtime, obtain models, configure an editor integration and provide enough memory for the model and context you choose.
The practical choice depends on five dimensions:
| Dimension | Free hosted assistant | Self-hosted server | Question to answer |
|---|---|---|---|
| Usage and limits | Plan quotas and eligible models determine how much work you can do. | Your practical ceiling is set by hardware, model speed and any software limits. | Will the allowance or local capacity cover your real workload? |
| Privacy and data handling | Prompts and code are processed by the hosted provider under its plan and privacy terms. | Local inference can keep data on your hardware, subject to the editor, extension, plugins and server you add. | Which components can see source code, prompts and responses? |
| Setup and maintenance | Install an extension, sign in and use the supported workflow. | Install a runtime, download models, configure integration, update components and troubleshoot compute. | Is saving setup time more important than controlling the stack? |
| Hardware and context | Provider infrastructure supplies inference; your device mainly runs the editor. | Memory, GPU or CPU capacity and context length constrain usable models. | Can your machine run the model at the context your tasks require? |
| Workflow and capability | Deep integration across supported editors, chat and agent features, with model availability set by the plan. | Model and agent choices are flexible, but integration quality depends on configuration and the local model. | Which option fits your editor, languages and task types? |
The sources do not establish an apples-to-apples benchmark for latency, code quality, agent success or total cost. Treat those as workload-specific measurements you must make rather than assuming one deployment style is inherently more productive.
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Dimension 1: usage limits and model access
What the free hosted tier provides
GitHub’s plans page currently lists Copilot Free at $0, with 2,000 monthly code completions. It also lists selected model access and a limited allowance of GitHub AI Credits for chat and agent features. Those are plan entitlements, not performance guarantees: a completion quota does not tell you how often suggestions will be accepted, and an AI Credit allowance does not guarantee that a long agent task will finish within one interaction.
Read the live plan details immediately before committing a team or a production workflow. Verify whether the models you need are eligible on the free tier, which chat or agent actions consume credits, and whether limits reset monthly. A plan can be an excellent starting point for intermittent coding while becoming restrictive for continuous pair programming, large refactors or frequent agent runs.
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What “unlimited” local use actually means
A self-hosted runtime normally has no hosted-plan completion counter. Your effective limit is the time and capacity of the machine running inference, the model’s licensing and availability, storage for model files, and any limits imposed by the editor integration or agent framework. A model that answers slowly or exhausts memory can be less usable than a quota-limited hosted assistant even when the local software itself is free.
Estimate demand before installing. Record how many completions, chat turns and agent tasks you perform in a normal week, the largest files you ask the assistant to inspect, and whether several developers will share one server. Compare that demand with the hosted allowance and with the local model’s observed throughput on your hardware. No published figure in the available material converts a plan quota into an equivalent local workload.
Usage fit test
- Hosted is a fit when your monthly completions and chat or agent allowance cover normal work with headroom for busy periods.
- Local is a fit when your workload exceeds the hosted allowance and you can accept the machine time, queueing and administration required to serve it.
- Neither is a fit without testing when your workload is dominated by large agent jobs, multiple simultaneous users or unusually long context windows.
Dimension 2: privacy and data handling
Hosted processing requires a policy decision
GitHub’s current plan page says some interactions from Free and Pro users may be used to train and improve GitHub models unless users opt out. The exact setting, scope and applicable terms can change, so review the account controls and privacy documentation for the region and plan you will use. An organization handling proprietary code should make that review part of its approval process rather than treating a free tier as automatically unsuitable or automatically safe.
Also identify what leaves the editor. A completion request may include surrounding code, file names, diagnostics or chat history. Agent features can process more context than a single inline suggestion. Map those data flows, then apply repository rules, secret scanning and access controls before enabling an assistant on sensitive projects.
What local processing does and does not establish
Ollama states in its privacy-policy summary: “We don’t see your prompts or data when you run locally.” Its policy describes locally processed data as not collected, stored, transmitted or accessible by Ollama. It separately says cloud prompts and responses are transiently processed to fulfill a request and are not used for training. Those statements are Ollama’s own policy claims, not an independent audit.
Local inference protects only the path that actually stays local. An editor extension can send telemetry; a coding agent can call a remote service; a model-management tool can download metadata; and a shared server can expose logs or files to administrators. Review every extension, plugin, proxy and server in the chain. Disable cloud fallback if the editor offers it, restrict network access where practical, and set retention and authentication policies for shared infrastructure.
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Privacy fit test
- List the repositories, secrets and regulated data that the assistant may encounter.
- For each workflow, identify whether prompts, context, telemetry and generated code leave your device.
- Check the provider’s current terms and opt-out controls for a hosted plan.
- For a local stack, inspect the runtime, editor integration, model source and any agent service separately.
- Run a redacted test repository first and verify logs, network connections and cache locations.
Dimension 3: setup and maintenance
Hosted setup favors speed
A hosted assistant generally requires installing the supported extension for your editor, authenticating an account and confirming that the plan and organization policy permit the features you intend to use. GitHub says Copilot integrates with Visual Studio Code, Visual Studio, JetBrains IDEs and Neovim. That breadth can reduce friction when a team already standardizes on one of those environments.
The maintenance burden shifts to the provider: model availability, service updates and capacity are managed remotely. You still need to track extension releases, account changes, quota resets and policy updates, but you do not maintain an inference runtime or model files on every developer machine.
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Continue’s Ollama integration guide describes an IDE assistant for Visual Studio Code and JetBrains connected to local Ollama models, with the runtime on a laptop or a server. The guide is dated May 2024; use current installation and configuration documentation for exact buttons, package versions and commands because labels and defaults can change.
A dependable local rollout has these stages:
- Install the runtime. Install Ollama on the chosen laptop or server and confirm that the service starts under the account and operating system you will support.
- Acquire a model. Download a coding model that fits your memory budget and license requirements. Keep model files on an encrypted volume if the machine stores sensitive projects or prompts.
- Validate local inference. Run a short coding request outside the editor and confirm that the response is generated locally, completes within an acceptable time and does not trigger an unintended cloud route.
- Install the editor integration. Add the current Continue or another supported integration for Visual Studio Code or JetBrains, then select the local Ollama endpoint and model in its configuration.
- Set context and behavior. Configure repository indexing, file inclusion, context length, tool permissions and logging according to your project’s policy. Start with read-only tools before allowing file edits or shell execution.
- Operate and update. Track runtime, extension and model versions; monitor memory and disk use; back up configuration; and test updates on a noncritical repository before rolling them out.
For a shared server, add authentication, per-user isolation, capacity planning and a process for revoking access. A local laptop is simpler but ties availability to that machine and its sleep, power and network settings.
Setup fit test
- Choose hosted when a developer should be productive in minutes and the organization does not want to operate inference infrastructure.
- Choose local when the team can own installation, upgrades, troubleshooting and support, and those costs buy meaningful control.
- Run a pilot with one editor and one repository before standardizing a local configuration across a team.
Dimension 4: hardware, model size and context
Context length is a first-class requirement
Ollama’s January 2026 coding guidance recommends a 64,000-token context for coding tools and says to update the context to at least that length. Context includes the instructions, conversation, retrieved files and tool output that the model must hold at once. Increasing it can improve repository-level tasks, but it also increases memory demand and may reduce speed.
Do not treat 64,000 tokens as a universal minimum. It is a vendor recommendation for coding tools; a small bug fix may work with less, while a multi-file refactor may need more. Measure the context your integration actually sends, including hidden system prompts and tool traces.
Model-specific memory examples
For one example, Ollama’s guide cites about 23 GB of VRAM for glm-4.7-flash at a 64,000-token context. The same guide lists glm-4.7-flash, qwen3-coder and gpt-oss:20b as local options. The 23 GB figure applies to that model and context combination; it is not a general GPU threshold.
An older Continue guide gives dated Code Llama examples of 16 GB or more of memory for a 13B model and 32 GB or more for a 34B model. Those figures illustrate how model size changes requirements, but they should not size a current deployment without checking the documentation for the exact model, quantization, context and runtime.
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| Planning variable | Why it matters | How to verify |
|---|---|---|
| Model family and parameter size | Larger models generally require more memory and may run more slowly. | Use the model’s current runtime documentation and download metadata. |
| Quantization | Different weight formats change memory use and output characteristics. | Record the exact quantized variant, not just the model family. |
| Context length | Longer prompts and tool traces increase memory demand. | Measure the configured context and the largest real request. |
| Concurrent users or models | Parallel requests can exhaust memory even when one request works. | Load-test with the expected number of sessions. |
| GPU versus CPU execution | Hardware affects latency, capacity and whether a model fits at all. | Test the intended model on the actual machine, not a specification sheet alone. |
Hardware fit test
- Choose the model and context first; do not buy hardware based on a generic “AI-ready” label.
- Check memory requirements for that exact model and quantization.
- Leave headroom for the operating system, editor, repository index and concurrent requests.
- Test a representative task: a completion, a multi-file explanation and an edit with tool output.
- Record response time, memory pressure, failures and whether the runtime falls back to CPU or refuses the request.
Dimension 5: workflow and capability
Hosted workflow strengths
GitHub positions Copilot across Visual Studio Code, Visual Studio, JetBrains IDEs and Neovim, with completion, chat and agent experiences governed by the plan and model availability. A supported integration can provide predictable commands, account management and updates across a team.
Suggestion quality is not uniform across languages. GitHub notes that quality can vary with the volume and diversity of training data for a language. Evaluate the languages, frameworks and conventions in your repositories rather than extrapolating from a demonstration in a different stack.
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Ollama’s local model options can be connected to coding agents and editor integrations. You can select a model suited to your privacy requirements, run without a hosted inference endpoint and tune context or tool permissions. The trade-off is responsibility: you must choose a model, keep it available, and verify that the integration sends the right files and instructions.
Capability is a property of the complete workflow, not the deployment label. Compare the same model class, context, repository slice and tool permissions when evaluating hosted and local options. A local assistant may excel at private code explanation while a hosted agent may offer smoother multi-file operations; the reverse can also occur for a particular project. The available evidence does not establish a universal ranking.
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Use a 1-to-5 score for each option, but define the score from your own constraints rather than from marketing claims.
| Score | Usage | Privacy | Setup | Hardware | Workflow |
|---|---|---|---|---|---|
| 1 | Allowance or capacity frequently blocks work. | Data path conflicts with policy. | Operational burden is unacceptable. | Model cannot run at required context. | Editor or language support is inadequate. |
| 3 | Works for normal weeks but has little surge capacity. | Acceptable with controls and monitoring. | Team can support it with planned effort. | Runs with compromises in speed or concurrency. | Core tasks work; edge cases need manual handling. |
| 5 | Comfortable headroom for expected demand. | Matches data-handling requirements. | Fast to deploy and easy to maintain for your team. | Runs the chosen model and context with headroom. | Fits the editor, languages and agent tasks you perform. |
Weight the dimensions instead of averaging them when one is non-negotiable. For example, a regulated project may make privacy a pass/fail gate; a small laptop may make hardware a gate for local inference. Keep the test repository and task list identical when comparing options.
Practical decision paths
Start with the free hosted tier
Choose this path when you value immediate productivity, use a supported editor and can stay within the current allowance. Confirm model eligibility and data-use settings, then monitor completions and AI Credits for a full month. If the allowance is consistently sufficient and the workflow meets policy, there may be no reason to operate a server.
Move to a local server or workstation
Choose this path when local processing is a priority and your team can support the stack. Select a model and context before sizing hardware; Ollama’s cited example of approximately 23 GB of VRAM applies specifically to glm-4.7-flash at 64,000 tokens. Pilot the runtime, editor integration and repository controls, then document updates, backups and access management.
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Use a staged evaluation
For uncertain cases, run the same three tasks in both environments: an inline completion, a repository-level explanation and a constrained multi-file change. Record quota consumption, response time, accepted edits, context failures, privacy observations and operator effort. This produces evidence for your codebase without pretending that a plan limit or parameter count predicts productivity.
Costs: why neither option is automatically cheaper
The hosted free tier has a listed price of $0, but its allowance and model access are limited and may change. A self-hosted setup may avoid per-request fees while requiring a capable computer or GPU, electricity, storage, upgrades, monitoring and staff time. Shared servers add administration and availability costs; individual laptops add duplication and inconsistent configurations.
The available information does not provide comparable lifetime costs for equipment, power, maintenance and hosted usage under a defined workload. Make a spreadsheet for your own scenario: one-time hardware, monthly power, replacement cycle, administrator hours, model storage, hosted overage and the value of developer time. Do not infer savings from the word “free.”
Common failure modes and fixes
The hosted allowance runs out
Check which action consumed the quota, whether the reset date has arrived and whether a less intensive workflow fits the remaining allowance. Do not assume switching editors changes the plan’s accounting.
The local model does not fit
Reduce the model or context only after confirming that the smaller configuration still solves your task. Otherwise move to hardware with adequate memory. The model-specific requirement, not a generic GPU tier, should drive the decision.
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Responses are too slow
Measure whether the bottleneck is CPU execution, memory pressure, context length, concurrent requests or the editor’s indexing step. Test a smaller context or model and compare the same task; do not claim a speed improvement without measuring it.
Private code still leaves the machine
Inspect extension settings, cloud fallback, telemetry, agent tools, proxy logs and shared-server access. Local inference alone does not certify the entire workflow.
Quality varies by language or repository
Build a small evaluation set from your own code: representative functions, tests, naming conventions and documentation tasks. GitHub’s note that quality varies with training-data volume and diversity is a reason to test each important language rather than rely on a single demo.
Quick Recap
Final checklist before standardizing
- Record the exact hosted plan, model eligibility, monthly completion allowance and AI Credit rules on the date you decide.
- Document whether hosted interactions may be used for model improvement and how users can opt out.
- For local use, name the runtime, model, quantization, context length, hardware and editor integration.
- Verify the complete data path, including extensions, agents, telemetry, logs and cloud fallback.
- Test the same representative tasks in both environments and keep the results with the configuration.
- Plan updates, backups, access control and rollback for every self-hosted component.
- Recheck provider plans, model catalogs and privacy terms before publication or procurement because they change.
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