GitHub Copilot Premium Requests: Your Dec 2 Playbook

On December 2, 2025, GitHub will start removing legacy $0 budgets for older enterprise and team accounts, changing how GitHub Copilot premium requests are governed. That sounds innocuous until you realize what counts as a “premium request”: upgraded models, coding agent sessions, Spark prompts, and some code review features—all of which can burn through allowances fast if you don’t set guardrails. The upside: you can keep productivity high and costs sane with a few policy flips, a model strategy, and one hour of admin work.

What exactly are GitHub Copilot premium requests?

Premium requests are the metered actions that use advanced AI capabilities beyond the included models. Every Copilot plan includes a monthly allowance, and overages bill at $0.04 per premium request when allowed by policy. Current per-seat allowances: Free 50, Pro 300, Pro+ 1,500, Business 300, Enterprise 1,000. The included models on paid plans (for example, GPT‑4.1 and GPT‑4o) don’t consume premium requests; premium models and certain features do.

Two details matter for planning:

• Model multipliers. Some models cost more than one request. For example, a top‑tier reasoning model may have a 10× multiplier (one interaction deducts ten premium requests), while lighter models can be 1× or even 0.33×. Choose carefully, or you’ll torch allowances without realizing it.
• Feature fixed rates. Copilot Spark (app builder) consumes a fixed 4 premium requests per prompt. The Copilot coding agent uses one premium request per session (a session = create or modify a PR). That’s good news for predictability.

The December 2 change: who’s impacted and how

If your organization had an account‑level $0 budget created before August 22, 2025, GitHub is removing it starting December 2, 2025. After removal, your Premium request paid usage policy decides what happens when users exceed allowances:

• Enabled – charges are allowed for overage premium requests.
• Disabled – premium features stop when the allowance is exhausted.

Individual Pro/Pro+ subscribers aren’t affected by the legacy budget removal. For Business and Enterprise, this is the moment to audit usage, set a policy, and—if you want to allow spend—add a realistic cap via budgets tied to cost centers.

Framework: the 90‑minute “DEC2” rollout for admins

Here’s the playbook we’re shipping with clients this week. Block 90 minutes, screenshare with your finance partner, and run it end‑to‑end.

D — Detect where you’ll blow through allowances

Download your Copilot usage report. Sort by users who exhausted allowances or hit model multipliers frequently. Tag heavy users by team and repository to see if spikes correlate with deadlines or particular codebases.

E — Establish the policy

In Copilot settings, set Premium request paid usage to one of:

• Disabled for orgs that can’t incur AI overage by policy (regulated environments, cost‑sensitive teams).
• Enabled for high‑leverage teams, but only after you add a budget cap.

Reminder: some users hold licenses from multiple orgs. Ensure they select the correct “Usage billed to” entity; otherwise premium requests may be rejected or misbilled.

C — Calibrate budgets with a simple formula

Start with this: Expected overage = (Premium demand − Allowance) × $0.04 × model multipliers. If your mobile team averages 1,800 premium requests/month per heavy user on a 300‑request plan, overage is 1,500. At 1×, that’s $60 per user/month; at 10×, it’s $600. Set a per‑team monthly cap that’s 1.2× the expected overage to absorb swings, then revisit monthly.

2 — Two safeguards

Enable email alerts to billing managers at 50% and 90% budget utilization. Also set a hard stop budget at the account level (e.g., $3,000/month) so a model reconfiguration or onboarding surge can’t run away with spend.

People also ask: will Pro and Pro+ be affected on December 2?

No—individual plans keep their $0 default overage budgets unless you manually change them. The December 2 removal targets legacy $0 budgets in Enterprise and Team accounts created before August 22, 2025.

Strategy: choose models like you choose instance sizes

Here’s the thing: most teams don’t need the most expensive reasoning model for daily chat. Reserve it for gnarly refactors and architecture decisions. For routine code review and Q&A, pick a 1× model. For scaffolding tests and simple transforms, consider lightweight models or the included ones (0× on paid plans).

Practical defaults that work:

• Chat default: 1× generalist model.
• Review default: 1× model; allow opt‑up for complex PRs.
• Agent sessions: enabled, one premium request per session—great value on big tasks.
• Spark: permitted for a subset of users; budget separately (4 requests per prompt adds up).

If you’re leaning into Google’s or Anthropic’s latest releases, pressure‑test multipliers with a fixed two‑week pilot. We covered the model landscape—and when to swap defaults—in our take on Google AI Mode after Gemini 3.

How many GitHub Copilot premium requests do we really need?

Baseline it by persona:

• Feature devs on active sprints: ~600–1,200 requests/month if using premium chat, agent sessions, and occasional model upgrades.
• Infra/DevOps: ~200–500; spikes when writing workflows or IaC from scratch.
• Staff/Principal: wide variance; fewer, deeper requests (higher multipliers) during design spikes.
• QA/SDET using the agent for test scaffolding: ~400–800.

Give Business seats to the broad base and Enterprise seats (1,000 allowance) to the top 10–15% who consistently hit >800/month. It’s cleaner than chasing overages on Business. Reassess quarterly.

Admin walkthrough: settings to flip before Dec 2

In one sitting:

1. Export usage for the last 60–90 days and identify heavy users and teams.
2. Set the org policy (Premium request paid usage): Disabled for default; Enabled only for teams with a defined budget.
3. Create budgets by team or business unit with email alerts at 50%/90% utilization.
4. Pick model defaults in tooling guidelines: 1× model for chat/review; restrict 10× models to a short‑lived “opt‑up” process.
5. Enable coding agent (public preview) for repos where agent PRs are safe; it’s one request per session and often replaces many ad‑hoc chats.
6. Gate Spark to builders and solutions engineers; brief them on 4‑request prompts and expected monthly spend.
7. Require “Usage billed to” selection for users with multiple org licenses; include it in onboarding.
8. Publish a one‑pager in your engineering handbook with all of the above and support routes.

Cost modeling: a quick sheet finance will accept

Build a two‑tab sheet. Tab 1: allowances and user counts by plan. Tab 2: a forecast with knobs for model mix and agent/Spark adoption. Use these defaults to start:

• Business seat: 300 allowance; 25% at 1× overage, 5% at 10×; 8 agent sessions/user/month.
• Enterprise seat: 1,000 allowance; 15% at 1× overage, 2% at 10×; 12 agent sessions/user/month.
• Spark users: 50 prompts/month × 4 requests.

Then sensitivity‑test model choices. Moving 20% of chats from a 10× model to a 1× model can drop overage by half without impacting outcomes if you teach developers when to opt up. That’s the lever executives actually feel.

People also ask: how do we block premium request charges?

Set Premium request paid usage to Disabled. Keep in mind: when users exhaust their allowance, premium features stop working until the next month. For teams working on deadlines, consider Enabled + a small budget cap with alerts.

Common pitfalls we’ve seen this quarter

These are avoidable:

• “Free lunch” model assumptions. Teams assume included models perform identically to premium ones. They usually do for routine tasks, but not for multi‑step reasoning. Push a clear policy for when to opt up (architecture decisions, non‑trivial refactors, security‑sensitive code review).
• Agent sessions without repo scoping. Enable the coding agent only on repos with linters, tests, and CI checks that will catch over‑broad edits. It’s one request per session, but the blast radius of a bad session can be large.
• Unbounded Spark experimentation. It’s 4 requests per prompt; a single enthusiastic builder can burn thousands per month without noticing. Set a separate budget or restricted group.
• Multi‑org users not picking a billing entity. Premium requests get rejected until they set “Usage billed to.” Add this to onboarding and remind in your IDE tips.

Governance that doesn’t slow teams down

Lean governance beats blanket bans. Publish a one‑page “AI Usage Guide” covering:

• Model ladder (included → 1× → 10×) with examples.
• Agent playbook—when to prefer an agent session over a long chat, how to scope PRs, and when to stop and ask for review.
• Data hygiene—sensitive repos and secrets policies still matter. If you need a refresher on taming your supply chain, study our npm supply chain attack playbook.
• Budget expectations—which teams are Enabled vs. Disabled for overage, caps, and alert recipients.

People also ask: where can I read the full Dec 2 details?

We broke down the change and timeline in a short explainer here: GitHub Copilot Premium Requests: The December 2 Switch. Share that link with stakeholders who want the background before you run this playbook.

Step‑by‑step: a 30‑minute developer tune‑up

Ask each developer to do this today:

1. Open Copilot in your IDE and check your monthly premium request counter. If you’re hitting the limit, note which tasks made the biggest dents.
2. Switch your default chat model to a 1× option. Add a shortcut or snippet to “opt up” when you genuinely need deeper reasoning.
3. Use the coding agent for chunky work (migrations, test scaffolding, config re‑writes). It consumes one request per session and often replaces a dozen ad‑hoc chats.
4. Cache prompts for repeated tasks—save them in your snippets or repo README. Re‑running long prompts repeatedly wastes requests and adds little value.
5. Avoid retry storms. If a 10× model returns a mediocre answer, don’t hit it again. Drop to a 1× model, refine your prompt, or try the agent with a better task description.

What to do next (leaders + owners)

• Run the DEC2 rollout: detect usage, set policy, calibrate budgets, add two safeguards.
• Publish your “AI Usage Guide” and set defaults for models, agent, and Spark.
• Upgrade the top 10–15% to Enterprise seats if they consistently exceed 800 requests/month—simpler than paying unpredictable overages on Business.
• Schedule a 30‑minute dev tune‑up. Ask for before/after examples of when opting up changed the outcome, then codify that into the guide.
• Bring finance to the first review. Show the sheet, the caps, and the alert triggers. Confidence wins you more room to experiment.

Where we can help

If you want someone to own the policy setup, modeling, and comms, our team does this routinely alongside CI/CD hardening and cost baselining. See our services and ping us if you need a same‑week rollout. Prefer to talk through your specific repos and workflows? Book a working session, and we’ll leave you with a tuned configuration and a one‑pager your org can adopt.

Zooming out

AI features are moving from “magic” to “metered.” That’s not a bad thing. The meter forces you to set intent: which work deserves a premium model, when an agent should take the wheel, and who gets to experiment with Spark. Done right, the December 2 change becomes a forcing function to professionalize your AI usage—clear defaults, reasonable caps, and a model ladder that’s easy to follow.

If you’re already tackling other platform changes this quarter—App Store policy updates, Lambda runtime upgrades, or CDN pricing shifts—thread this into the same operational cadence. Our pieces on Apple’s new AI consent rule and CloudFront flat‑rate pricing show how policy and pricing pivots reward teams that respond quickly with simple, durable playbooks.