Cost baseline and goals

Establish a two-week baseline using your current tools. Record cost per day and total tokens consumed by coding sessions. Note any throttling or privacy flags from your provider.

Define success as 8× lower monthly cost and equal or faster delivery. Tie that to specific repositories and sprint goals. Without this bar, small wins can mask larger waste.

What to measure each day

Measure prompt-to-first-token time, end-to-end completion time, and edit acceptance. Use repeatable prompts across identical code states. Capture results inside issues for later review.

Log where context windows help or hurt. Large local context often removes retry loops. When retries drop, your token bill and time spent both fall.

7-day roadmap for decisive results

Day one validates hardware and installs local tooling. Day two brings the assistant into your editor and warms caches. Day three compares against real features from your backlog.

Day four adds hybrid routing. Day five tracks full-day performance with production code. By day seven, cancel excess subscriptions and commit to the chosen stack.

Hardware and model choices

Use a Windows machine with a recent NVIDIA GPU for best results. 24 GB VRAM accelerates larger coder models, but smaller cards still help. Start with a strong local coder model, then tune for speed or accuracy.

Balance throughput and memory with quantized weights. Mixed-precision models often match cloud quality for routine code. Try two models for a day each to calibrate responsiveness.

Installation steps on Windows

Install Ollama and the Continue extension, then pull a coder model. Test quick edits, multi-file refactors, and docstring generation. Keep prompts inside the editor to reduce context waste.

Run these commands to bootstrap quickly on Windows and VS Code. The steps avoid global system changes and keep rollback simple.

```powershell
# Install Ollama on Windows
winget install Ollama.Ollama
# Start service and pull a coder model
ollama serve
ollama pull qwen2.5-coder:7b

# Install Continue in VS Code
code --install-extension Continue.continue
```

</section>
<section class="content-section" id="pros-cons-and-maintenance">
<h3>Pros, cons, and maintenance</h3>
<p>Pros: unlimited usage, on-disk privacy, and predictable day-to-day speed. Cons: larger models want more VRAM and time for first download. Keep a small fallback model for tight memory days.</p>
<p>Maintenance is light: occasional model updates and driver refreshes. Pin versions during sprints to avoid midweek surprises. Document your working config in the repo README.</p>
</section>
</section>
<section class="content-section" id="fully-local-tabby-self-hosted-copilot">
<h2>Fully local: Tabby self-hosted copilot</h2>
<p>Tabby provides a self-hosted alternative with enterprise guardrails. You run the server and decide how code flows, which keeps audits simple. It integrates with VS Code and supports team policies.</p>
<p>Choose Tabby when teams need centralized management and on-prem privacy. It pairs well with Docker and Windows containers. Your developers get familiar completions without cloud exposure.</p>
<section class="content-section" id="why-teams-pick-tabby">
<h3>Why teams pick Tabby</h3>
<p>Centralized model hosting reduces sprawl and shadow tooling. Admins enforce quotas and model versions across repositories. Compliance teams like local logging and simple retention policies.</p>
<p>Performance remains strong with a capable GPU. You can share a beefy workstation for small teams. It becomes your dependable coding hub each workday.</p>
</section>
<section class="content-section" id="docker-deployment-on-windows">
<h3>Docker deployment on Windows</h3>
<p>Deploy Tabby with Docker Desktop to keep updates atomic. Map a persistent volume for models and logs. Expose only the needed port to your LAN or VPN.</p>
<p>Use this minimal compose to get moving, then refine. Test latency against real code prompts before scaling users.
<pre><code>```yaml
version: &#039;3.8&#039;
services:
tabby:
image: tabbyml/tabby:latest
ports:
- &#039;8080:8080&#039;
volumes:
- tabby-data:/data
deploy:
resources:
reservations:
devices:
- capabilities: [gpu]
volumes:
tabby-data:
```

Admin, updates, and policies

Schedule rolling updates outside sprint peaks. Pin a release for two weeks, then evaluate changes on a staging node. Document policies inside your internal wiki.

Set access tokens per repository. Limit external calls by default, then allow exceptions with approvals. Small guardrails prevent surprise bills later.

When hybrid wins the day

Use hybrid routing for long reasoning tasks, migrations, and complex tests. Local models handle ordinary edits with zero token cost. You get the best of both worlds at modest spend.

There are moments when cloud beats local decisively. Tight deadlines, gnarly diffs, and multi-language refactors favor burst inference. Keep that path ready, not default.

Routing and cost controls

Create rules by file path, prompt size, or latency thresholds. Cap monthly cloud spend and alert at 60% and 90%. Prefer local for short prompts and push heavy prompts to cloud.

Log routed events per day to spot drift. If cloud usage climbs, review prompts for waste. Simple hygiene keeps budgets stable.

Reference docs and setup pointers

Start with Continue settings, then add Groq or DeepInfra API keys. Validate token accounting against provider dashboards. Verify retries, timeouts, and fallbacks behave as expected.

Use provider docs as your living playbook. Keep links close inside your engineering handbook. Update routing as models and prices evolve.

Cursor Pro for agentic work

Agentic editing helps with multi-file changes and scaffolding. Cursor executes plans and proposes diffs with fewer prompts. It can reduce context juggling on busy days.

Trial Cursor on a feature branch for one sprint. Compare plan accuracy and rework time against local stacks. Choose it when orchestration outperforms manual prompting.

Copilot Pro for flat-fee stability

Copilot Pro wins on simplicity and known cost. Install, authenticate, and start coding inside VS Code. Little setup, little maintenance, strong everyday coverage.

Teams use it as a baseline while piloting other stacks. Keep it if output quality meets your bar and privacy needs. Replace it when local control matters more.

Decision guide for your week

If privacy is critical, start local and add hybrid burst later. If predictability is king, choose Copilot Pro. If orchestration speed matters, consider Cursor.

Make the choice by day seven with data, not impressions. Lock tooling for one quarter. Reopen evaluations when model pricing or quality shifts.