GitHub Copilot premium requests have moved from a nice-to-understand concept to a must-manage line item. Beginning December 2, 2025, GitHub is removing default $0 account-level budgets for many enterprise and team accounts created before August 22, 2025, shifting control to a “premium request paid usage” policy and granular SKUs. That means your guardrail changes—and so does your risk. Here’s how to turn this into a cost advantage rather than a surprise invoice.
GitHub Copilot premium requests: what’s new on Dec 2?
Let’s get the facts straight. On December 2, GitHub will begin removing the historical $0 budget backstop for eligible enterprise and team accounts. Practically, your ability to block or allow paid usage will be governed by an org setting called premium request paid usage. If it’s disabled, paid usage is blocked regardless of budgets. If it’s enabled, paid usage can proceed and budgets become your spend cap mechanism.
Two more details matter for planning: counters reset at 00:00:00 UTC on the 1st of each month, and extra premium requests beyond a plan’s allowance are billed per request. Model multipliers apply, so a single interaction can count as multiple premium requests depending on the model and feature used. If you’ve been relying on the $0 budget default as your guardrail, that guardrail is moving.
What counts as a premium request?
Premium requests are metered interactions with certain models or features beyond your included allowance. Examples that commonly trigger premium requests include advanced chat models, reasoning models, or specific agent capabilities. Your plan’s included models (for many paid plans, GPT‑4.1 and GPT‑4o) don’t draw down premium requests, but other models do—sometimes with multipliers.
Here’s the thing: multipliers are the hidden swing factor. A reasoning or large frontier model can count as multiple premium requests per interaction. If your team uses an agent that escalates to a higher‑tier model for tricky tasks, the math can change quickly.
Are GPT‑4.1 and GPT‑4o still “free” on paid plans?
For most paid plans, usage of GPT‑4.1 and GPT‑4o in chat or agent flows doesn’t consume premium requests. Rate limits still apply, but this is your baseline for predictable productivity without metered overages. It’s the higher‑end or specialized models—think more expensive reasoning tiers—that tap into the premium request allowance and then into paid usage.
Model multipliers, in plain English
Model multipliers determine how many premium requests an interaction costs. Some quick, illustrative examples used by many teams today: a powerful reasoning model might have a 10× multiplier (one chat equals 10 premium requests), a thinking variant could sit around 1.25×, and speed‑optimized models can be 0.25×–0.33×. The mix you allow per workflow directly shapes your bill. Treat multipliers like CPU quotas in the old world: set them deliberately, review monthly.
The two-hour cutover plan (do this before December 2)
Block two hours on your calendar. Get your platform lead, a finance partner, and a team manager in the room. Then run this playbook:
1) Decide your default stance: enable or disable paid usage
Ask one question: if the allowance runs out, should premium requests stop or continue? If you pick “stop,” set premium request paid usage to disabled. If you need continuity for certain teams, enable it but protect with budgets and alerts (next steps). Write the decision down and circulate it.
2) Set budgets and alerts like you mean it
Budgets aren’t paperwork—they’re rate limiters for money. Create monthly budgets at the enterprise or org level. Turn on alerts at 75%, 90%, and 100% so someone actually responds. If you must enable paid usage, set a modest cap for December while you watch the numbers (for example, 25–35% above the included allowance multiplied by your expected mix).
3) Map models to work types
Make a quick worksheet with three rows: speed, general accuracy, deep reasoning. Assign models accordingly. Encourage the default use of included models for day‑to‑day coding and chat. Reserve high‑multiplier models for code migrations, complex refactors, or design‑by‑example sessions. If you have an agent that auto‑escalates to a reasoning model, document when escalation is allowed.
4) Lock policies per team, not just at the org level
High‑leverage teams (platform, security, data) may deserve different caps or model access than large feature squads. Create policy groups that mirror cost centers. This is where the newer dedicated SKUs help—agent vs. chat vs. other AI tools can be tracked per SKU and cost center. Finance will thank you.
5) Turn on reporting and appoint a human
Enable usage reports and share them weekly in your #eng‑leadership channel. Nominate one owner to review spikes and open a ticket when usage crosses 90%. No owner, no control.
A simple spend model your CFO will accept
You don’t need a data lake. Use three inputs: seats using premium models (S), average interactions per seat per month that hit premium models (I), and weighted multiplier (M). Estimated paid usage equals S × I × M minus the included allowance. If that result is positive, multiply by the per‑request price to estimate cost. Want a quick December cap? Set your budget at 1.2–1.4× the estimated paid usage to leave breathing room without opening the floodgates.
Example: 120 engineers, 30 premium‑model interactions per month, and a weighted multiplier near 0.5 because most flows use lightweight models. That’s 120 × 30 × 0.5 = 1,800 premium requests. Subtract your plan’s allowance; any remainder times the per‑request price is your expected December overage. If you’re piloting a 10× reasoning model with a handful of staff engineers, run that math separately so it doesn’t skew the whole org.
Policy guardrails that actually work
If you’ve read this far, you want practical safety rails. Use this framework:
- Default to included models for day‑to‑day chat and coding. Make it easy; pin the right model as default.
- Gate reasoning models behind a short template: problem complexity, expected time saved, model to use, and a link to the resulting PR or document.
- Cap monthly paid usage per cost center with a budget and “stop on hit” for noncritical groups. For critical groups, switch to “alert on hit, continue” and review in your weekly ops sync.
- Audit model usage weekly. Pull three examples of good use and three of overkill. Share in engineering chat so the norms evolve.
- Train teams on when to escalate. “When you’re stuck 30 minutes or more, ask the reasoning model” is better than “use it whenever.”
Need a reference for structured, pragmatic risk control? Our security note on dependency hygiene takes a similarly actionable angle—different problem, same discipline. See the npm supply chain attack playbook for how we operationalize guardrails with teams.
People also ask: quick answers you can paste internally
What happens on December 2 if we do nothing?
If your org relied on an account‑level $0 budget as a hard stop and you do nothing, that backstop can be removed. Your premium request paid usage policy becomes the control. If it’s enabled without a budget, paid usage can proceed until you manually intervene. If it’s disabled, paid usage remains blocked.
How much is a premium request?
Beyond your plan’s included allowance, premium requests are billed per request. The meter doesn’t care who used them—only that they were used. That’s why budgets and SKUs per team are worth the five minutes to set up.
Which models should we allow?
Default to included models for most scenarios. Allow a cheaper fast model for bulk Q&A or summarization. Permit a single reasoning model for complex refactors, migrations, or design exploration, but require a justification template. Your usage reports will tell you if the mix needs tuning.
Are counters truly monthly?
Yes. They reset at 00:00 UTC on the first of each month. That reset time can land mid‑evening in the United States; plan your cutover and reporting cadence around UTC, not local time.
Does data residency change billing?
If you use GitHub Enterprise Cloud with data residency, premium request billing may not be active yet in your environment; check your account’s docs and settings. Don’t assume; verify before you plan your budget and policy stance.
Let’s get practical: a 30‑minute checklist you can run today
Set a timer for 30 minutes. Ship this:
- Open org settings and decide: enable or disable premium request paid usage.
- Create a December budget with alerts at 75/90/100% for each major cost center.
- Pin included models as default in your team’s IDE guidance. Document one approved reasoning model and when to use it.
- Download usage reports and share them weekly in leadership channels.
- Nominate a single owner for December monitoring and escalation.
Why SKUs actually help finance and engineering
GitHub’s shift to dedicated SKUs for different AI tools (for example, coding agents versus other experiences) is a net positive for accountability. You can align SKUs with cost centers, making it easier to answer “what did we get for this spend?” in January. Treat December as a learning month: keep budgets tight, watch which SKU drives the most value, and expand deliberately in Q1.
A note on developer experience
No policy survives first contact with a sprint if it’s annoying. The best guardrail is the default model selection in the IDE. If you want people to avoid costly models by default, make the right model one click away and the expensive one two clicks away with a short justification. Celebrate examples where a reasoning model saved a day or avoided a risky refactor. Culture beats policy every time.
Pitfalls we’ve seen in real teams
Three patterns crop up:
- “Free until it isn’t.” Teams assume included models cover everything, then a specialized agent quietly escalates to a high‑multiplier model under load. Fix: require escalation rules.
- “One mega budget.” A single giant org budget hides waste. Fix: allocate per cost center and set different stop/continue behaviors.
- “No owner.” Spikes go unnoticed. Fix: one person owns the December watch. Back them up with alerts and a weekly slot in the ops review.
When paying extra is absolutely worth it
There are great reasons to spend: porting a service to a new framework, rewriting flaky tests at scale, or generating design docs that unblock a cross‑team project. Treat premium requests like burst capacity: concentrate them on work that compounds. If the output won’t be reviewed or merged, it’s probably not worth the multiplier.
Zooming out: AI procurement meets developer autonomy
This moment is more than a billing tweak. It’s the start of real AI procurement inside engineering: budgets, SKUs, policies, and a shared language between builders and finance. If you put the right defaults in place, GitHub Copilot premium requests become a lever—one you can push for speed when it matters and lock when it doesn’t.
If you want the short, date‑driven explainer on the policy change itself, read our guide to the December 2 Copilot switch. For model strategy and how to pick the right AI for the job, see our take on Google’s AI mode shift after Gemini 3. And if you’re mapping AI work into your broader platform roadmap, our what we do page outlines how we help teams define guardrails without slowing delivery.
What to do next (this week)
- Make the org‑level call on paid usage (enable/disable) and document it.
- Create December budgets with alerts; split by cost center.
- Set default models in IDE guidance; allow one reasoning model with escalation rules.
- Turn on weekly reporting; assign an owner; review in ops.
- Pilot a limited high‑multiplier workflow where it creates outsized value.
Need a second set of eyes on your setup? Drop us a note via contact. If you’re planning broader runtime or platform upgrades in parallel, our Lambda Node.js upgrade playbook shows how we sequence fast wins while keeping risk low.
December doesn’t have to be stressful. Set a sensible default, cap your exposure, and let engineers ship. That’s the whole point of these tools—and the only metric that matters when the quarter closes.
