On December 2, 2025, GitHub removed default $0 budgets for many Enterprise and Team accounts, changing how Copilot premium requests are authorized and billed. If your organization was created before August 22, 2025 and you relied on that $0 budget as a hard block, you now need explicit policy controls and budgets in place—or you’ll either get surprise charges or, just as bad, unexpected denials when engineers hit the allowance wall. Here’s the practical guide I’ve been using with clients this week.
What changed on December 2—and who’s affected
Three milestones matter for planning:
First, monthly allowances for premium requests became the norm for paid Copilot plans in mid-2025. Second, in November 2025, GitHub began introducing dedicated SKUs per AI tool (for example, the coding agent and Spark) so companies could meter usage with more granularity. Third, on December 2, 2025, GitHub started removing legacy, account-level $0 premium request budgets for Enterprise and Team orgs created before August 22, 2025. The removal nudges you to use the policy switch—“Premium request paid usage: Enabled/Disabled”—and explicit budgets per org, enterprise, or cost center instead of leaning on a blanket $0 cap.
Who is not affected? Individual Pro/Pro+ users keep their default $0 budget behavior for now. Also note: unlimited code completions remain, and paid plans still include access to core chat/agent models with separate rate limits. What drives incremental cost are premium requests: certain advanced models and features that meter separately from your included monthly allowance.
Copilot premium requests explained (without the guessing)
Think of Copilot usage in two buckets. Bucket A: included usage—unlimited code completions and core chat/agent interactions on included models, subject to rate limits. Bucket B: premium requests—calls to higher-tier models or features that draw from a monthly per-user allowance. When the allowance is exhausted, what happens depends entirely on your settings. If you have “Premium request paid usage” set to Enabled and a positive budget, Copilot will continue and overages will be billed. If it’s Disabled, Copilot will block premium requests while leaving non-premium capabilities intact.
Why the dedicated SKUs matter: they let you report and cap spend by capability (for example, your coding agent versus Spark) instead of seeing one giant Copilot line item. Finance finally gets a clean mapping from tool to cost center.
The December Billing Switch Playbook (do this now)
Here’s the fast, safe path I’m running with engineering leaders and FinOps teams. It takes one afternoon if you have admin access; two days if you need stakeholder sign-offs.
1) Establish your baseline (90 minutes)
Pull last two months of Copilot usage reports and identify three numbers: a) premium requests per active seat, b) percentage of users who hit allowance in the last billing cycle, and c) top SKUs by consumption. Don’t overfit—directional is enough to start. You’re looking for power users, spikes around release weeks, and any bot or service account consuming requests.
2) Decide your policy posture per environment
Set “Premium request paid usage” to Disabled by default for sandbox and training orgs. For production engineering, choose one: Enabled with a tight budget cap, or Disabled with a manual escalation path for critical needs. Teams running 24/7 incident response typically set Enabled with caps; teams focused on routine backlog often stay Disabled to avoid drift.
3) Create budgets where they belong
Budgets can live at enterprise, org, or cost center. The common mistake is to put a single enterprise cap and call it done. Better: set enterprise guardrail budgets plus org-level budgets for your major cost centers (for example, Consumer App, Platform, Data). If you’re already tagging cost centers in your HRIS or identity provider groups, mirror those names in your Copilot budget labels for quick reconciliation.
4) Use SKU-level caps for noisy neighbors
If Spark runs hot in one group while the coding agent is steady elsewhere, cap Spark for that group only. This avoids penalizing teams that use the coding agent responsibly. The new per-SKU metering is built for this.
5) Wire alerts that drive action
Configure email and webhook alerts at 75%, 90%, and 100% of budget. Send 75% to engineering managers, 90% to the relevant director and FinOps, and 100% to your platform duty channel with instructions for what to turn off or who can approve temporary increases. Alerts without a runbook are just noise.
6) Publish a one-page escalation policy
Engineers need clarity: when Copilot blocks a premium request, do they file a ticket, ping a chat channel, or switch models? Write a short doc that lists who can approve budget increases, the max temporary bump (for example, 20% above baseline), and the review window (for example, 48 hours).
7) Clean up access and exceptions
Remove premium access from service accounts and interns. Where legal or security requires, keep the default at Disabled and grant time-bound exceptions via a request form. If you have multiple orgs, align their policy posture to avoid shadow overages in the “forgotten” org.
The 7–7–30 budgeting rule of thumb
While exact allowances vary by plan and model, you can prevent 90% of headaches with a simple heuristic: set monthly budgets to baseline usage plus a 30% buffer; trigger a human-in-the-loop review if any team burns more than 7% of the budget in a single day or more than 7 consecutive days above baseline. It’s conservative enough to catch runaways and generous enough not to trip on normal release cycles.
People also ask
What actually counts as a premium request in Copilot?
Premium requests typically involve higher-capability models or features beyond the included set for your plan. They’re the calls that draw down the monthly allowance and, once exhausted, either stop or become billable based on your settings. Your usage report lists them by SKU, which is why turning on SKU-based metering is so useful.
Will Pro users get surprise charges?
No. Individual Pro/Pro+ users still default to a $0 budget for premium requests. They’ll see blocks rather than charges unless they explicitly change their budget and payment settings. Enterprise and Team orgs should review their policy switch and budgets post–December 2.
What happens when a budget is reached?
If your policy is Disabled, premium requests stop and users fall back to included capabilities. If your policy is Enabled and you’ve set a dollar budget, Copilot continues until the budget hits 100%, then blocks unless you increase it. Alerts at 75% and 90% give you time to adjust.
Can we cap per user?
Budgets are configured at enterprise, org, or cost center level. The monthly premium allowance is tracked per user, and reports surface high-usage users. Many teams approximate per-user control by scoping access via groups and setting granular cost center budgets mapped to those groups.
Turning SKUs into clean chargeback
Here’s the thing: finance leaders don’t want another opaque AI bill. The dedicated SKUs introduced in November let you align consumption to value stories. For example, tag your coding agent consumption to “Developer Productivity” and Spark to “Architecture + R&D.” In your monthly review, stack-rank SKUs by cost and by outcome: merged PRs, cycle time, or incidents resolved faster. If a SKU costs 4% of your engineering budget but saves two on-call rotations a month, that’s a win you can defend.
To make this stick, integrate the Copilot usage export into your data warehouse and join it with team and repo metadata. A simple dashboard with “premium requests per active contributor” and “cost per merged PR” turns a contentious budget line into a performance lever.
Developer experience: avoid accidental slowdowns
Sudden blocks feel like a broken tool. Set expectations: communicate that premium requests now respect policy and budgets, and show engineers how to see their allowance status in the IDE. Include a quick tip sheet: switch models when blocked, prefer included capabilities for routine tasks, and escalate only when the business impact justifies it. A 30-second Loom beats a 30-minute standup.
Risks and edge cases I’m seeing
Cross-org drift: you tightened policy in one org but not the shadow org used by a legacy team. Fix: audit all orgs under your enterprise and standardize settings.
Service accounts: a CI bot can spike premium usage if it calls a premium model in a loop. Fix: disable premium for service accounts and rely on included capabilities or offline batch jobs where possible.
Release crunch: engineers burn through allowances during pre-release weeks. Fix: temporarily raise cost center budgets with a clear sunset date and require a postmortem to tune prompts and workflows.
Confusion about what’s “unlimited”: unlimited code completions still have rate limits, and premium requests are the billable category when you go beyond allowances or enable paid usage. Educate managers so they don’t mix these up in retros.
A 72-hour implementation checklist
Day 1 morning: export last two months of usage, identify top consumers, and list all orgs. Afternoon: choose your default policy posture per org and flip the switch accordingly.
Day 2 morning: create enterprise and org-level budgets with 75/90/100% alerts; set SKU caps for any noisy neighbor. Afternoon: publish your one-page escalation policy and update onboarding docs.
Day 3 morning: record and send a 3-minute video to engineers explaining the change; add an IDE status tip. Afternoon: integrate the export into your warehouse or BI tool and draft a simple dashboard. Book a 30-minute weekly review for the next month to tune budgets.
What this means for AI roadmaps
Zooming out, the shift to SKUs and policy-first billing is the grown-up phase of AI inside developer tooling. You get clearer levers to pay for capability when it’s worth it and to block it when it’s not. It encourages teams to define when a premium model is warranted—security reviews, complex refactors, migration scaffolding—and when included models are fine. Expect other vendors to follow with more explicit tiers and metering. Treat that as a feature, not a tax.
Where we can help (and what to read next)
If you need a neutral partner to stand up budgets, policies, and dashboards, our team has done this dance across startups and global enterprises. See how we work in our services overview, browse relevant work in the portfolio, and reach out via contact to get a two-week cost governance sprint on the calendar.
For a deeper dive on the policy change itself, we’ve covered specifics in our recent pieces: the billing shift on December 2 and what it means for enterprise guardrails, plus a reality check for how premium allowances interact with day-to-day developer workflows. Start with our practical breakdown in What Dec 2 Changed, then read the broader context in Copilot Premium Requests: The New Reality. If you’re also tracking AI platform strategy post–re:Invent, our take on Nova Forge’s build-your-own-models is here: Nova Forge Is Live.
Metrics that matter
As you move from triage to steady-state, report these monthly:
• Premium requests per active user (target a stable range by team).
• Percentage of users hitting allowance before mid-month (aim under 10%).
• Overage spend rate versus budget (keep within your 30% buffer).
• Cost per merged PR for teams using premium SKUs in critical paths.
• Time-to-approve budget escalations (should be under 24 hours for production teams).
Pair those with qualitative signals from engineering managers: did policy changes slow work, or did they simply move premium tasks to the right moments?
What to do next
• Check your org’s policy switch today; don’t assume the old $0 budget still blocks anything.
• Create at least two budgets: enterprise guardrail and per–cost center caps. Turn on 75/90/100% alerts.
• Cap the noisiest SKU first and review weekly while usage settles.
• Publish a one-page escalation policy and add an IDE tip to your onboarding docs.
• Build a lightweight dashboard that ties Copilot consumption to outcomes. Make the monthly review boring and predictable.
The December switch isn’t a crisis. It’s an invitation to align AI capability with business value—and to prove, with clean data, that your engineering investments are working as hard as your teams are.
