The Day the Quota Exploded
A week ago, I briefly reacted to a LinkedIn post about GitHub Copilot's new token calculation system. A simple comment, a few lines. I naively thought the system would work as before.
I thought it was a bug.
Then I waited. I told myself that the credits would adjust, that it was a transient anomaly. Things always sort themselves out, right?
Nope.
In 3-4 days, my quota was completely drained. And not just exhausted—I even went into overage. The tool silently entered "overage mode," continuing to consume without warning.
I also observed some pretty violent fluctuations in token rates:
- Haiku dropped from 10.8 to 1.08 in just a few days
- Other models followed a similar trajectory
Result: I had to stop using GitHub Copilot entirely. No more autocomplete. No more pair programming. Back to basics with a standard editor.
The Problem: A Monthly Quota vs. A Real Constraint
Here's the real problem that hit me: the quota is monthly.
Not weekly like some competitors. Not adjustable. Not flexible. One 30-day block, and if you burn through it in 3 days, tough luck.
Before the pricing changes, I could easily make it through the month. Even with regular usage, the credits were enough. It was an acceptable model.
Now? Definitely not.
And that's where it really stings.
The Impact: Who Can Afford It, Who Gets Locked Out
With this strict quota model, GitHub Copilot remains comfortable for one specific category of users: those who can absorb the cost.
- Enterprises with dedicated IT budgets
- Senior developers on permanent contracts at well-funded companies
- Those who can drop several hundred euros a month on optimization, testing, and iteration without guilt
But founders building on a shoestring budget? Small teams running on tight cash reserves? Freelancers who need to justify every euro spent?
They hit a wall. Fast.
It's not a gradual curve. You go from "this is useful" to "this is inaccessible" in days, not months.
This isn't just a technical problem. It's a problem of access and equity.
The Alternative: Local LLMs Are Back in Play
Facing this dead end, I reopened a door I'd been putting off for months: local LLMs.
Ollama, LM Studio, vLLM... tools I'd tested briefly but never seriously explored. They interested me but lacked urgency. Why run a model locally when Copilot was there—performant and well-integrated?
The constraint forced me to revisit what had been sleeping for months.
Local LLMs aren't a silver bullet. Performance varies by model and use case. But they offer something Copilot can no longer guarantee: cost predictability.
Comparison: Copilot Cloud vs. Local LLMs
Here's a brutally honest comparison:
| Criterion | GitHub Copilot | Local LLM |
|---|---|---|
| Available models | Claude, GPT-4, Gemini (plan-dependent) | Mistral, Llama, Qwen, Phi... |
| Performance (large projects) | Excellent | Good (depends on model and CPU) |
| Latency | Instant (cloud) | 1-5s (depends on hardware) |
| Monthly cost | €20/mo → quota blown | €0 (after initial investment) |
| Cloud dependency | Yes (connectivity issues?) | No (works offline) |
| Privacy | Microsoft sees your code | Zero telemetry if configured right |
| Control | Microsoft sets the rules | You decide everything |
The question isn't "Is local LLM better?" but rather "Is local LLM sufficient for my needs?"
And here, a hypothesis emerges: yes, probably for 80% of tasks.
The Real Use Case: Covering 80% of Work
When you look at actual AI tool usage in development:
- Simple autocomplete: small local models crush the cloud
- Code refactoring: Mistral 7B handles it very well
- Documentation and comments: Phi or Llama 2 are more than enough
- Basic debugging: local is fast and free
- Boilerplate generation: perfect for local
Cases where Copilot Cloud still wins:
- Complex architecture across multiple files
- Advanced debugging with large context windows
- Deep algorithmic optimization
But honestly? That's 20% of my workday.
The other 80%? A well-configured Llama 13B or Mistral gets the job done.
Constraint = Opportunity
I could have waited for pricing to stabilize. Waited for GitHub to offer a fairer model. Waited months before exploring alternatives.
Necessity just accelerated the inevitable.
It's a pattern we forget too often as founders and makers: real innovation rarely comes from comfort. It comes from constraints. From scarcity. From "this has to work or we're blocked."
Copilot blocked me. It's frustrating. But it also forced me to rediscover an ecosystem that has evolved massively in 18 months. Ollama didn't exist 2 years ago. Mistral was an experiment. Quantized models barely fit on a MacBook.
Now?
You can run an excellent 7B model on almost any modern hardware. For a handful of euros in monthly electricity.
That's a real choice. Not a compromise.
The Real Question: For Whom?
Copilot Cloud is still better for certain use cases. Enterprises with budget? Stay on Copilot. The integration is too good, the performance too predictable.
But founders on tight budgets? Small teams? Freelancers optimizing every euro?
You now have a real alternative. And it no longer asks you to sacrifice on performance.
That changes the game.