GitHub Copilot Alternatives With Local Model Support 2026

GitHub Copilot's local-model story changed in June 2026: VS Code can now run chat and agent workflows offline without a GitHub account or Copilot plan, but local models still cannot power its standard inline suggestions. That leaves only three reasons to switch: local tab completion, centralized self-hosting, or a stronger agent workflow, and the cheapest paid team alternative saves just $480 a year at 10 seats before hardware and administration.

The short answer: Zed is the best complete local replacement

Zed is the best GitHub Copilot alternative if both the coding agent and inline suggestions must run on infrastructure you control. Kilo Code is the better choice if changing editors is a nonstarter. Cline is the strongest focused local agent inside VS Code, Tabby is the deployment to choose for a centrally managed completion server, Aider is the cleanest Git-first terminal option, and OpenCode covers the most surfaces.

There is also a seventh answer: do not switch. If local chat and agent work are enough, current VS Code already connects to Ollama and other local providers. The alternative earns its place only when it closes a specific gap.

Prices, limits, trial terms, and product capabilities below were verified against each vendor's live pages on August 25, 2026. "Starting price" means the software or platform fee. A local model can still create hardware, electricity, setup, and maintenance costs.

Before switching, know what VS Code now does locally

VS Code itself is now a credible local-model host, but only for one side of the Copilot experience. Microsoft's June 2026 BYOK update lets VS Code connect to Ollama, Foundry Local, and compatible model providers for chat and supported agent workflows. It works without a GitHub sign-in or Copilot plan, including fully offline use.

That does not make GitHub Copilot a locally hosted product. It makes VS Code the application layer that gives a model tools, context, and an interaction loop. The model can run locally while the editor supplies the chat and agent interface.

Four terms keep this comparison honest:

• Local model means inference runs on your computer or equipment you control.
• Self-hosted model means the endpoint runs inside infrastructure you control. It may sit on another machine or in your private cloud rather than on the developer's laptop.
• Agent means the model can inspect files, edit multiple files, and use tools such as a terminal under the application's permission system.
• Inline completion means the ghost-text or multi-line suggestion that appears while you type and is usually accepted with Tab.

The gap is the last item. Microsoft's current language-model documentation says BYOK applies to chat and utility tasks, not standard inline suggestions. Semantic search and embedding-backed features also continue to depend on GitHub or Copilot support. VS Code exposes an extension API that other products can use for custom completion, but its built-in local-model path does not send your inline suggestions to Ollama.

This splits the old "local Copilot alternative" question into two smaller questions:

1. Do you want a local model to plan and edit code in a visible agent loop?
2. Do you also want every suggestion that appears as you type to be generated locally?

If the answer to the second question is no, adding another extension may increase complexity without improving privacy. Configure the Language Models editor, connect the local provider, choose the model for chat or supported agent work, and keep the existing editor. If the answer is yes, Zed, Kilo Code, or Tabby becomes relevant because each documents a local completion path.

The same distinction matters for offline claims. A local chat can work without the internet while a repository search, extension update, remote source-control action, or hosted embedding service still reaches outside the machine. "The model is local" and "the whole development environment is air-gapped" are not interchangeable promises.

The Copilot price being replaced

GitHub's current individual plans are Free, Student, Pro at $10 per month, Pro+ at $39 per month, and Max at $100 per month. Free includes up to 2,000 inline completions per month, while Student is free for verified students. The organization plans are Business at $19 per granted seat per month and Enterprise at $39 per granted seat per month.

GitHub's live plan page also says new self-serve Business sign-ups for organizations on GitHub Free and GitHub Team have been temporarily paused since April 22, 2026. That does not change the $19 comparison baseline, but it can change the buying path. A replacement decision should compare against the plan the organization can procure, not a remembered checkout flow.

How these alternatives were picked

A product qualified only if its current vendor documentation showed a supported local-model path for a coding workflow. A repository that can theoretically be patched to call a local endpoint did not count. Neither did a cloud assistant that offers strong privacy terms but still sends inference to its own service.

The six products were judged on five criteria:

1. Coverage: whether local models work for the agent, inline completion, or both.
2. Deployment clarity: whether the vendor documents Ollama, LM Studio, llama.cpp, an OpenAI-compatible endpoint, or a self-hosted server.
3. Workflow fit: whether the product lives in an editor, terminal, desktop app, or central service.
4. Operational wall: the first constraint a serious deployment hits, such as an editor migration, fixed completion model, hardware floor, one-GPU limit, or missing identity controls.
5. Total cost: every public software tier plus the compute or inference charge that sits outside it.

No hands-on performance test was claimed. Local latency and output quality depend too heavily on model size, quantization, context length, GPU memory, repository shape, and the task to pretend that one laptop result represents the category. The rankings instead use current supported capabilities, exact prices, documented limits, and normalized cost analysis.

This process also cut several familiar names. A cloud-only coding assistant is not a local-model alternative just because it has a generous free plan. Continue was removed from the ranked list because its live homepage now says it was acquired by Cursor and describes the remaining open-source code as a foundation, not as a supported independent product for a new deployment. That change alone makes several older local-Copilot lists stale.

For the broader cloud and local landscape, the best AI coding assistants in 2026 comparison covers a wider set. The list here stays deliberately narrow: every ranked product has a documented route to local inference.

1. Zed: best complete local Copilot replacement

Zed is the strongest complete replacement because it documents local models for both its Agent and its Edit Prediction system. Edit Prediction is Zed's inline completion layer, including single-line and multi-line suggestions. That two-part coverage is rare, and it removes the ambiguity behind many "supports Ollama" claims.

The product is a full code editor, not a VS Code extension. Local models can run through Ollama, LM Studio, llama.cpp, or a compatible local server for Agent, Inline Assistant, and related AI features. A separate local Edit Prediction configuration supports Ollama, vLLM, llama.cpp server, LocalAI, and other servers that implement the OpenAI completion API.

That architecture makes Zed the cleanest answer for a solo developer who wants both flows local and does not mind changing editors. It is also the largest workflow change in this list. Extensions, shortcuts, remote-development habits, debugging features, and team conventions all need to be checked before a migration.

Best for: Developers who want local agent work and local inline completion in one editor.
Standout: Documented local paths for both Agent and Edit Prediction.
Pricing: Personal $0 forever; Pro $10/month; Business $30/seat/month; hosted usage above Pro's included $5 is API list price plus 10%.
Free trial: Pro for two weeks or until $20 of trial token credits is consumed; Business has no free trial.

Zed pricing, with the hidden boundary

Zed Personal is $0 forever and includes 2,000 accepted hosted edit predictions plus unlimited use with your own API keys or external agents. Zed Pro is $10 per month, includes unlimited edit predictions and $5 of hosted tokens, then bills hosted use at API list price plus 10%. Zed Business is $30 per seat per month for organization-wide model policies, data controls, unified spend visibility, unlimited edit predictions, and role-based access controls.

The word "Business" can create the wrong expectation. The plan has no fixed LLM credit allowance, no free trial, and no current SSO, SAML, or SCIM. There is no seat minimum, although order forms start at 25 seats. For an identity-heavy enterprise deployment, the missing SSO family can outweigh the product's local-model completeness.

At 10 seats, Zed Business costs $3,600 per year before model usage or local compute. Copilot Business costs $2,280 at the same seat count. Zed is therefore $1,320 more expensive on platform fees alone. Choose it for editor design and complete local control, not as a license-saving exercise.

Mini-tutorial: run Zed Agent and Edit Prediction through Ollama

Zed documents the two local paths separately. Keep them separate during setup so a working agent does not create the false impression that inline predictions are local too.

1. 1

   Start the local runtime

   Install Ollama, run ollama pull mistral for Zed's documented Agent example, then run ollama serve where the service does not start automatically. Zed should discover pulled Ollama models and show them in its model picker.

2. 2

   Select the Agent model

   Open the Agent panel, choose the discovered Ollama model, and begin with a bounded read-only task such as explaining one module. Confirm from the selected provider and local process activity that the request is hitting Ollama.

3. 3

   Configure Edit Prediction separately

   Open Zed's settings for edit_predictions. The vendor's documented example sets the provider to ollama, the endpoint to http://localhost:11434, the model to qwen2.5-coder:7b-base, prompt format to infer, and maximum output tokens to 512.

4. 4

   Prove both paths

   Disconnect the network on a non-critical test machine. Send an Agent prompt, then type in a representative file and confirm an Edit Prediction appears. A passing Agent check plus a failing completion check means only half the deployment is local.

The precise model should match the machine and task. The value of the tutorial is the two-path verification, not the example model name. A local model that cannot call tools reliably is still a poor Agent choice even when its completion output is acceptable.

2. Kilo Code: best local option inside VS Code and JetBrains

Kilo Code is the best fit when the editor must stay and both agent work and inline completion need a local route. Its open-source extensions cover VS Code and JetBrains, while its CLI adds a terminal surface. That makes the migration smaller than Zed's, and the product documents local Ollama for the agent plus local Codestral through Ollama or LM Studio for autocomplete.

The strength comes with two restrictions. Kilo's autocomplete model is currently fixed to Codestral, so "local completion" does not mean an arbitrary model picker. Its agent guidance also sets a serious hardware expectation: 24 GB or more of GPU VRAM, or a Mac with 32 GB or more of unified memory, for the recommended local models to run at decent speed.

Kilo's own documentation is unusually candid about quality. It recommends qwen3-coder:30b for agent work, names devstral:24b as an alternative, and warns that the smaller Qwen model can fail tool calls, loop, or produce syntax errors more often than a frontier cloud model. It recommends at least a 32k context window and documents a 10-minute default API timeout. Those are operational requirements, not footnotes.

Best for: VS Code or JetBrains users who want a local agent and a supported local completion path.
Standout: The least disruptive route to both local workflows inside familiar editors.
Pricing: Individual $0; Teams $15/user/month; Enterprise custom; inference and cloud compute are separate.
Free trial: 14-day Enterprise trial.

Kilo pricing: three bills, not one

Kilo's platform plans are Individual at $0, Teams at $15 per user per month, and Enterprise at a custom price. Teams adds analytics, shared agent modes, centralized billing, shared BYOK, privacy controls, and priority support. Enterprise adds SSO, OIDC, SCIM, audit logs, provider limits, private-gateway BYOK, SLA commitments, and dedicated support. The 14-day trial exposes Enterprise features, after which a customer chooses Teams or Enterprise.

Inference is a separate decision. Auto Free, BYOK, or local costs $0 per month in Kilo platform inference fees. Kilo Gateway has no subscription and charges exact provider rates without markup. Kilo Pass has Starter at $19 per month with up to $26.60 in monthly credits, Pro at $49 with up to $68.60, and Expert at $199 with up to $278.60. Credit purchases carry a 5% processing fee.

Cloud features create a third bill. Code Review is $0.33 per hour. Cloud Agent Docker and Small are each $0.60 per hour. Gas Town and Cloud Agent Standard are each $1.20 per hour. They are billed per second, and model inference remains separate.

For 10 developers, Kilo Teams costs $1,800 per year versus $2,280 for Copilot Business. The $480 difference is the local ceiling: spend more than $40 per month across extra hardware and administration, and the apparent platform saving is gone. One engineer spending even a few hours maintaining the local service can cross it.

3. Cline: best transparent local agent in VS Code

Cline is the best focused choice for a local, approval-driven coding agent inside VS Code. It exposes the agent's file changes, terminal actions, and model-provider choice without charging an individual platform subscription. The local path is well documented through Ollama, LM Studio, and Atomic Chat.

Cline is not the answer to every Copilot job. Its core product is an agent, not a first-party Tab-completion replacement. If the goal is local ghost text while typing, pair it with a separate completion extension or choose Zed, Kilo Code, or Tabby. Adding a second extension may still be the right design, but it creates two policies, two update paths, and potentially two model servers to govern.

The best Cline use case is a developer who wants to hand a bounded issue to a local model, inspect each proposed action, and keep the existing VS Code environment. Think "trace this failing test and propose a patch," not "predict the next six tokens every time a key is pressed." That interaction is slower and more deliberate than Copilot completion, but it can own more of a multi-file task.

Best for: VS Code users who want a visible, controllable local agent loop.
Standout: Free individual agent interface with documented local runtimes and no required subscription.
Pricing: Open Source free; Enterprise custom; inference supplied separately.
Free trial: No Enterprise trial listed; the open-source tier is the evaluation path.

Cline pricing and the hardware ladder

Cline Open Source is free for individual developers. There is no subscription or seat fee, but hosted inference is usage-based unless the developer brings a key or runs a local model. The free tier lists the VS Code extension and CLI, secure client-side architecture, MCP marketplace, multi-root workspaces, and community support.

Cline Enterprise is custom priced. It adds the JetBrains extension, SSO, SLA, dedicated support, centralized billing, configuration management, role-based access control, inference-provider limits, a team dashboard, and authentication logs. No Enterprise free trial is listed on the live pricing page.

The hardware guide gives a more useful boundary than the $0 platform price. Cline maps 16 to 32 GB of RAM to small or quantized models, 32 to 64 GB to mid-size models, and 64 GB or more to larger models and context windows. Its default local endpoints are http://localhost:11434 for Ollama and http://localhost:1234 for LM Studio.

That ladder should shape the pilot. A 16 GB laptop may prove the connection and still fail the intended workflow through slow generation, shallow context, or weak tool use. A deployment is ready only when the target model can read enough of the repository and complete the representative task within the team's patience threshold.

Where Cline fits in a mixed local setup

A practical split is Cline for explicit agent sessions and a dedicated local completion provider for typing flow. That architecture can outperform a single all-purpose model because an agent needs tool use and long-context reasoning, while completion needs low latency and fill-in-the-middle training. The price is operational duplication.

For a solo developer, that duplication may be a few settings. For a company, it means two approved models, two endpoint policies, two telemetry reviews, and two rollback paths. The enterprise coding-agent comparison goes deeper on governance, but the immediate Cline decision is simple: use it when agent transparency matters more than one-product completeness.

4. Tabby: best centralized self-hosted completion server

Tabby is the best choice when the primary job is to replace Copilot completion with a centrally operated, self-hosted service. It is an open-source coding assistant built around a team running its own LLM-powered completion server. Developers connect through editor extensions while the organization controls the service behind them.

That server-centered design is a different product category from a laptop-local agent. It is useful when ten or fifty developers should share approved models, repository context, access controls, and observability rather than each running Ollama independently. It also transfers responsibility for uptime, upgrades, GPU capacity, authentication, and incident response to the organization.

Tabby's strongest fit is a regulated or privacy-sensitive engineering group that values centralized completion more than autonomous agent behavior. Its Answer Engine and code browsing broaden the surface, but completion remains the reason to choose it over a desktop-first agent. A solo developer can run Community, although Aider, Cline, Kilo Code, or Zed will usually create less infrastructure.

Best for: Teams that want one controlled, self-hosted completion server.
Standout: Centralized local-first deployment with editor extensions and team plans.
Pricing: Community $0/user/month; Team $19/user/month; Enterprise custom; Pochi cloud usage separate.
Free trial: No time-limited Team or Enterprise trial listed; Community supports up to 5 users.

Tabby pricing: privacy without a license discount

Tabby Community costs $0 per user per month for up to 5 users. Tabby Team costs $19 per user per month for up to 50 users. Tabby Enterprise is a custom, annually billed plan with unlimited users, SSO, bespoke deployment, a dedicated Slack support channel, and roadmap prioritization.

Tabby Cloud's Pochi uses a separate usage-based model bill. The live pricing page includes $20 in free monthly credits, bills automatically when usage exceeds $10 or at month end for smaller amounts, and says Tab Completion is always free without a usage limit. That cloud offer should not be confused with the self-hosted server's compute cost.

At 10 seats, Tabby Team and Copilot Business both cost $2,280 per year in platform fees. Tabby then needs infrastructure. Its documentation gives about 8 GB of VRAM for CodeLlama-7B in int8 mode and says one instance supports a single GPU. The exact server cost depends on hardware ownership, utilization, redundancy, and support expectations, but the direction is clear: Tabby is a control purchase, not a cheaper license.

The one-GPU wall

The one-GPU limit per Tabby instance is the operational wall to plan around. A single GPU can be entirely adequate for a small team and become a queue during a synchronized coding period. Scaling then means additional instances and routing decisions rather than attaching several GPUs to one instance.

Measure time to first completion and accepted completions during peak overlap, not on an idle server. A fast average can hide a poor 95th-percentile experience that trains developers to ignore suggestions. Also test model reloads, extension fallback behavior, and what happens when the service is unavailable. Completion is valuable partly because it disappears into typing; any recurring wait destroys that advantage.

5. Aider: best Git-native terminal workflow

Aider is the best local-model alternative for developers who want the repository and Git history, not the editor UI, to organize the agent session. It runs in the terminal, builds a map of the codebase, edits files, automatically commits changes, and can run linters and tests. The Apache-2.0 software has no platform subscription.

The workflow is concrete: enter a repository, add the relevant files or let the repository map guide context, ask for a change, inspect the diff, and use Git to keep or undo it. That makes Aider attractive to developers who already trust terminal and version-control primitives. It is less natural for someone who expects a sidebar agent, visual checkpoints, and background completion while typing.

Aider connects to almost any cloud or local LLM, including Ollama. Its Ollama documentation recommends the ollama_chat/ model prefix and calls out a subtle failure: Ollama defaults to a 2k context window and silently discards material beyond it. A session may appear functional while losing repository context, which is worse than a clean error.

Best for: Terminal-first developers who want a Git-centered local agent.
Standout: Repository map, automatic commits, diffs, linting, and tests in one open-source loop.
Pricing: $0 Apache-2.0 software; model usage or local compute separate; no paid platform tiers.
Free trial: Not applicable because the software is free and open source.

Aider pricing and the $120 solo ceiling

Aider has no Personal, Pro, Team, or Enterprise platform tier. The software is free under Apache 2.0. The bill is the model provider or the machine running the model.

Replacing one $10 per month Copilot Pro subscription removes at most $120 per year. That is the solo version of the local ceiling. If a new GPU, additional memory, setup time, or slower output is purchased only to save the subscription, the arithmetic rarely closes. If the hardware already exists and the reason is privacy, offline work, or model control, the $120 becomes a secondary benefit.

The first configuration check should be context, not prompt cleverness. Aider documents an 8k Ollama server example and adjusts context to reduce silent truncation. Larger repositories may need far more. When a local model starts forgetting requirements, repeatedly reopening files, or modifying the wrong symbol, inspect the context window and repository map before blaming the interaction format.

6. OpenCode: best multi-surface local agent

OpenCode is the best option for one open-source agent that can follow a developer across terminal, IDE extension, and desktop app. It supports more than 75 model providers, including local models, and its core software does not require a paid subscription. The surface flexibility is its advantage over Aider's terminal-centered design.

Local Ollama works through an OpenAI-compatible provider endpoint at http://localhost:11434/v1. The provider documentation exposes the configuration rather than hiding it, including a suggestion to start around a 16k to 32k context window when tool calls fail. That is good for teams that want explicit control and extra work for users expecting one-click discovery.

OpenCode's local story is about agent sessions, not a complete replacement for Copilot's inline completion. It can work in an IDE, but the defining capability is the same multi-step agent across several interfaces. Choose it for surface portability and provider breadth, not for Tab suggestions.

Best for: Developers who want the same local-capable agent in terminal, IDE, and desktop.
Standout: More than 75 providers plus three interaction surfaces.
Pricing: Core $0 open source; optional Go $10/month; Enterprise custom per seat.
Free trial: Open-source internal pilot before an Enterprise sales engagement.

OpenCode pricing and data handling

OpenCode core is free open-source software, with the user choosing a local, BYOK, free, or hosted model. OpenCode Zen is an optional pay-as-you-go gateway with per-token model pricing. OpenCode Go is an optional $10 per month subscription for hosted open coding models. Its current limits are expressed as value ceilings: $12 per 5 hours, $30 per week, and $60 per month.

OpenCode Enterprise uses custom per-seat pricing. When a customer supplies its own LLM gateway, OpenCode says it does not charge for the tokens used. The evaluation path is an internal trial of the open-source product followed by a sales conversation for centralized configuration, SSO, the internal gateway, and implementation support.

The default data statement is strong: OpenCode says it does not store code or context, and processing happens locally or through direct calls to the selected provider. The exception is optional conversation sharing, which sends session data out. Its Enterprise documentation recommends disabling sharing during the trial. Privacy review should confirm both the default path and every optional escape hatch.

Who should pick what

Pick the missing capability, then accept the smallest workflow change that supplies it. Product enthusiasm is a poor decision method here because every option moves cost and complexity to a different layer.

Stay with VS Code when local agent work is enough

The native VS Code path is the lowest-change answer for a developer or team that wants local chat, planning, and supported agent tasks but can keep hosted Copilot completion or live without it. It preserves extensions, debugging habits, remote environments, and team support knowledge. The flip point is inline completion: if that data must also stay local, the built-in BYOK path stops short.

This is especially sensible for a small pilot. Configure one local provider, hide models that are not approved, select a utility model where needed, and document which features still depend on GitHub. Do not add another agent extension until a representative task shows the native setup is missing something material.

Pick Zed when completeness matters more than editor continuity

Zed wins for a solo technical builder or a small product group willing to adopt a new editor in exchange for one coherent local Agent and Edit Prediction system. Its decision flip is editor compatibility. If one required extension, debugger, remote environment, or accessibility workflow fails, Kilo Code becomes the safer answer.

For a larger organization, identity can flip the choice before editor fit. Current Business controls include model and data policies, but SSO, SAML, and SCIM are not available. A company that requires those controls should not treat roadmap language as a security feature.

Pick Kilo Code when VS Code or JetBrains must remain

Kilo Code wins when local agent work and local autocomplete are both required inside an existing editor. It asks developers to change an extension and model setup rather than the entire editing environment. The decision flips away from Kilo when the fixed Codestral autocomplete path is unacceptable or the local agent hardware cannot meet the documented memory needs.

It is also the most tempting cost story and the easiest one to overstate. Ten Teams seats save $480 per year versus Copilot Business before compute and labor. Use the product because the extension surfaces and provider controls fit, then treat the price difference as a small offset.

Pick Cline when the agent should be explicit and supervised

Cline wins when developers want a deliberate agent loop in VS Code and do not need that same product to generate inline suggestions. The product makes sense for issue-sized work where file reads, edits, commands, and approvals should remain visible. The choice flips to Kilo or Zed when a second local completion system would create too much configuration.

For a team, the public pricing gap also matters. Individual use is free, but Enterprise is custom priced. Validate the open-source workflow first, then ask sales for a quote only after the required SSO, provider limits, dashboard, and JetBrains surface have been identified.

Pick Tabby when the organization owns the service

Tabby wins when a platform or security group wants a central completion service rather than a model runtime on every laptop. It is the clearest choice for a shared, approved model and a predictable editor-extension interface. The decision flips when the group lacks an owner for uptime, upgrades, capacity, and developer support.

Do not assign that ownership implicitly to "the infrastructure team." Name the person or group, define a service target, and price the GPU before rollout. A local service with no operator becomes a less reliable version of the cloud product it replaced.

Pick Aider or OpenCode when the terminal is the center

Aider wins when Git diffs and automatic commits are the natural control surface. OpenCode wins when the same agent needs to move among terminal, IDE, and desktop while retaining broad model-provider choice. Neither is a direct answer to local inline completion.

The tie-breaker is workflow structure. Choose Aider for one repository, one terminal thread, and a tight diff-review loop. Choose OpenCode for multiple interfaces, sessions, and provider configurations. The Claude Code alternatives guide is the adjacent comparison when the decision is mostly about terminal agents rather than Copilot-style completion.

The cost comparison: calculate your local ceiling

Local models make inference ownership possible, not cost disappearance. At 10 seats, the public annual platform fees are:

• Kilo Teams: $1,800
• GitHub Copilot Business: $2,280
• Tabby Team: $2,280
• Zed Business: $3,600

Kilo creates the only public paid-plan saving in that four-product team comparison. It is $480 per year, or $40 per month. That amount is the local ceiling: the maximum additional monthly cost the deployment can absorb before its platform saving disappears.

The ceiling should include more than the GPU purchase. Count the annualized hardware cost, electricity, hosting or rack allocation, monitoring, backups, model downloads, patching, endpoint security, incident response, and the time spent helping developers when the service is slow. Also count the quality penalty if a smaller local model needs more retries or produces more review work.

For Tabby Team, the local ceiling against Copilot Business is zero because the seat prices are identical. Any compute and administration makes Tabby more expensive in cash terms. It can still be the right decision when the return is data control, air-gapped availability, model independence, or shared completion behavior.

Zed Business starts $1,320 above Copilot Business for 10 seats. It must earn that premium through the editor and control layer. A solo Zed user faces different economics: Personal is free and can use local models, while Pro matches Copilot Pro's $10 monthly platform price and adds hosted Zed features.

Aider, Cline Open Source, OpenCode core, Kilo Individual, Zed Personal, and Tabby Community can all bring the software line to $0 in their eligible scope. For one Copilot Pro user, the maximum subscription saving is $120 per year. Existing capable hardware may make that useful. Buying a machine mainly to recover $120 per year does not.

The ones to avoid

Continue is the clearest product to avoid for a new supported deployment in this category. Its live homepage says Continue was acquired by Cursor and that the open-source code remains available as a foundation. That is enough for an existing community to fork or maintain code, but it is not the same as choosing an actively positioned independent product with a current pricing and support path.

Existing Continue users do not need to panic. Pin versions, audit the repository and license, document the model endpoints, and decide who owns maintenance. New buyers should not rank it alongside Zed, Kilo Code, Cline, Tabby, Aider, and OpenCode without acknowledging that support change.

Also avoid any cloud-first assistant whose "local" claim means only local file indexing, a desktop application, or a private cloud account. The decisive question is where model inference runs for the exact surface you care about. Ask separately about chat, agent calls, inline completion, embeddings, telemetry, crash reports, updates, and optional sharing.

Finally, avoid the biggest local model your machine can barely load. A model occupying almost all available memory leaves little room for context, the editor, build tools, containers, and the operating system. A smaller model with consistent tool calls and acceptable latency often produces more completed work than a larger model that swaps, times out, or loses context.

The Monday move: run a two-engineer pilot

Run one repository with two engineers for one week before changing a team plan. The point is not to crown a model from a synthetic prompt. It is to find the first operational wall in the workflow you intend to buy.

1. 1

   Name the missing Copilot job

   Choose one primary job: agentic issue work, inline completion, centralized self-hosting, or terminal-driven edits. A pilot that tries to replace every Copilot feature at once will produce an ambiguous verdict.

2. 2

   Choose one representative repository

   Use a repository with the normal language mix, tests, build time, dependency shape, and security constraints. Give the two engineers the same three bounded tasks: explain a module, make a small multi-file change, and repair a failing test.

3. 3

   Lock the deployment

   Record the tool version, local runtime, model and quantization, context window, endpoint, hardware, network state, sharing setting, and any hosted fallback. Without that record, a good result cannot be repeated and a bad result cannot be diagnosed.

4. 4

   Measure the workflow, not token output

   Track time to first useful response, time to a valid diff, accepted inline suggestions where relevant, failed tool calls, manual interventions, retries, and reviewer time. Note peak latency when both engineers use the service together.

5. 5

   Make the budget decision

   Compare annual platform cost with compute and the estimated monthly ownership time. Keep the local path only if it wins the named control requirement and stays inside the local ceiling, or if the control benefit clearly justifies exceeding that ceiling.

The Monday decision should be small. Keep the existing VS Code local path, extend the pilot with one ranked alternative, or stop. Do not buy seats, migrate editors, or provision shared GPUs until the pilot shows which exact Copilot job is being replaced.

Frequently asked questions

Can you use GitHub Copilot locally?

VS Code can use a local model for chat, supported agent workflows, and utility tasks without a GitHub account or Copilot plan. GitHub's hosted Copilot models are not running locally, and the built-in local path does not cover standard inline suggestions.

Is there a way to run Copilot locally?

The precise answer is that VS Code hosts the local model through BYOK. Connect a provider such as Ollama and select that model for chat or supported agent work. Inline completion, semantic search, and embedding-backed features have separate dependencies.

Can I use local models with VS Code Copilot?

Yes for chat and supported agent work through VS Code's model controls. No for the standard Copilot-style inline-suggestion model. Kilo Code and Tabby can add local completion inside VS Code, while Cline adds a local agent.

Is there a free GitHub Copilot alternative with local model support?

Yes. Zed Personal, Kilo Individual, Cline Open Source, Tabby Community, Aider, and OpenCode core all have a $0 software path. Hardware, hosted inference, team governance, and maintenance may still cost money.

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