Continue vs Cline vs Aider vs Tabby: the Local Coding Tool Matrix

All four are open source, all four run against local models, and none of them overlap as much as their marketing suggests. This page is the feature matrix and the honest trade-offs; if you want installation walkthroughs, the end-to-end setup guide covers Continue and Cline click by click.

One framing note: your tool choice is the cheap decision — switching extensions takes minutes. Your model choice is what actually determines output quality, and that's a hardware question: see the best local coding models by VRAM tier.

The last row is the one people skip and regret: agentic tools multiply model weaknesses. A model that chats pleasantly at 8B will wander off-task the moment Cline hands it a five-step plan. Budget your hardware for the tool you actually intend to use.

Continue's job is to make you forget you cancelled Copilot: ghost text while you type, chat on selection, inline edit commands. Its killer feature for local use is per-role model assignment — a 3B FIM model answers autocomplete in ~150 ms while a 27B handles chat, each defined in one YAML block. Weaknesses: its edit flows are single-file-minded, and its agent ambitions trail Cline's. It's the right first install for 90% of readers, and the one this hub's setup guide standardizes on.

Cline turns a task description into a plan, edits across files, runs commands, and waits for your approval at each checkpoint — with the best MCP story of the four, so it can reach databases, browsers, and custom tools. The cost: agent loops are token furnaces (a single task can burn 50–100K tokens of context), which on local hardware means you want a 24 GB card running Qwen 3.6 27B or Devstral Small 2 24B before autonomy feels dependable — see Cline with local models for what actually works per tier. Below that, keep tasks surgical or run it hybrid against an API.

Aider's design decision that ages best: every AI change is a git commit. Bad edit? git revert. It builds a repo map so the model sees structure without stuffing whole files into context — the most token-efficient of the three assistants, which matters doubly on local hardware. No GUI, no autocomplete; it pairs naturally with Continue rather than against it. Setup and model picks: Aider with local models.

Tabby answers a different question: "how do ten developers get completions without ten bespoke setups or any code leaving the network?" One Docker container on a shared GPU box, lightweight IDE plugins, central repo indexing and usage stats. Its bundled completion models trail the open-weights frontier and it doesn't try to be an agent — the trade for being deployable by an ops team in an afternoon. Solo developers should skip it; team leads evaluating Copilot Business should start here (the economics are in the Copilot alternatives roundup).

• Solo, 8–16 GB GPU: Continue alone (small FIM model + mid chat model). Add Aider when tasks outgrow single files.
• Solo, 24 GB GPU: Continue for the inner loop + Cline or Aider for delegated tasks, all on one card — the setup the pillar guide calls the sweet spot.
• Team with compliance requirements: Tabby for completions + Aider per-developer for agent work against the same GPU box.
• Terminal purist: Aider alone. It's a complete workflow by itself.