GitHub Copilot Premium Requests: Last-Minute Settings

Starting December 2, GitHub will begin removing the $0 account-level budgets that previously blocked paid usage for GitHub Copilot premium requests on many enterprise and team accounts created before August 22, 2025. That means your “we can’t accidentally spend” safety net may vanish overnight. If your policy toggle allows paid usage or if a budget exists above zero, premium requests beyond each user’s monthly allowance can be billed at a per-request rate. Here’s how to lock down spend without turning off useful AI features your engineers rely on.

What’s changing on December 2?

Historically, many organizations had an account-level $0 Copilot premium request budget by default. When a user exhausted their monthly allowance, extra premium requests were rejected. Beginning December 2, GitHub is removing those legacy $0 budgets for affected enterprise and team accounts. After that removal, paid usage is controlled by your Premium request paid usage policy and whatever budgets you set going forward. Pro and Pro+ individual accounts keep their default $0 behavior and aren’t part of this switch.

If you’re not sure whether you’re affected, check the account creation date and whether a $0 budget is present today. If your org predates August 22, 2025 and you’ve never touched budgets, assume you’re in scope and act now.

What are GitHub Copilot premium requests?

Premium requests are metered uses of certain models or features that sit beyond your plan’s monthly included allowance. Allowances reset on the 1st of each month. If you exceed the allowance and your policy allows paid usage, additional requests are billed per request (for example, at $0.04 USD each). Model choice matters: some models have multipliers that count more than one premium request per interaction.

Paid Copilot plans still include unlimited GPT‑4.1 and GPT‑4o for chat/agent mode plus unlimited code completions, subject to rate limits. Premium requests typically kick in when you enable extra-capability models or certain advanced features—not for the core completion experience your team uses daily. That distinction is key to designing sane guardrails: you can keep everyday productivity while gating the expensive edge cases.

Why this matters: real dollar impact in minutes

Here’s the thing: a single user experimenting with a high-multiplier model can burn through an allowance and push your org into paid usage before lunch. Ten power users doing that near a deadline, and you’ve got a real invoice. At a $0.04 per-request price, just 10,000 overage requests equal $400. Make it 50,000 and it’s $2,000. If a model counts as 10×, each interaction consumes ten premium requests, and the math accelerates. You don’t need a catastrophe to see spend; you just need momentum and the defaults working against you.

The 20‑minute checklist to prevent surprise bills

Block out twenty minutes. Share screen with your finance partner if possible. Then:

1. Locate the policy toggle. In your organization or enterprise settings, find the Copilot policy named Premium request paid usage and set it to Disabled if you want a hard block on all paid usage after allowances run out. If you need controlled overages, set it to Enabled but continue through this list.
2. Create an intentional budget. If you enable paid usage, set a modest monthly budget to cap overages. For most orgs, start with an amount that equates to about $1–$5 per active Copilot seat, then iterate. Enable the option to stop usage when budget is reached.
3. Turn on alerts at 75%/90%/100%. Add multiple recipients: engineering leadership, an ops lead, and finance. If alerts only hit a generic inbox, they won’t be acted on quickly.
4. Lock model access before budgets. Budgets are your seatbelt; model access is your brakes. Restrict high-multiplier models to a small, named group (e.g., “AI research”) and default everyone else to the included models.
5. Enable per-SKU budgets when available. GitHub now supports dedicated SKUs for various AI products (for example, Copilot coding agent or Spark). Set individual caps so one tool can’t burn the entire pool.
6. Download the usage report. Pull the last full month plus the partial current month. Identify the top 10 users by premium requests and meet them where they are. Often they’re doing real work and need a sanctioned path.
7. Document the policy in your internal handbook. Write two sentences: which models are allowed by default, and the escalation path to request higher-cost models. If it’s not written, it’s not real.
8. Schedule a 30‑day review. Put a recurring calendar hold for the first business day of each month to review spend and adjust budgets before allowances reset.

Model multipliers you should know (and how to use them)

Not all models are priced the same from a premium requests perspective. Examples you’re likely to see:

• Claude Opus 4: 10× multiplier. One chat counts as ten premium requests. Great for deep reasoning, but gate it tightly.
• Claude Sonnet 3.7 Thinking: 1.25× multiplier. Useful for complex refactors and planning; restrict to senior devs or AI champions.
• Claude Sonnet 3.5/3.7: 1× multiplier. Safer baseline when you need non-included models.
• o4‑mini: ~0.33× multiplier. Efficient for quick structured tasks; good for high-volume teams if enabled.
• Gemini 2.0 Flash: ~0.25× multiplier for paid plans, 1× for free. Ideal when latency and cost efficiency matter more than deep reasoning.

Strategy: pick one low-multiplier model as your org’s default premium option and allow a request path to a higher-multiplier model for approved scenarios. Document examples (e.g., “use Opus for multi-file architecture work, not for routine code comments”).

People also ask

Do I need to pay for Copilot premium requests at all?

Plenty of teams won’t. If you stick to the included models and your developers primarily rely on completions and chat with GPT‑4.1/4o, you may never touch premium requests. Paid usage only appears when you exhaust the allowance and your policy permits overages. Many orgs simply disable paid usage or keep a small cap to cover occasional spikes.

What happens on December 2, 2025?

For older enterprise and team accounts, the legacy $0 account-level budgets that previously blocked paid usage begin to be removed. After removal, the Premium request paid usage policy and any new budgets you set determine whether overages are charged or blocked. If you do nothing and your policy allows paid usage, overages can start billing immediately once allowances are gone.

How many premium requests come with my plan?

Allowances vary by plan and are enforced monthly. Because GitHub continues to refine entitlements and model availability, treat the plan docs as your source of truth and review them quarterly. Regardless of plan, rate limits can still apply.

How much does a premium request cost?

Beyond the monthly allowance, each premium request is billed at a per-request price (for example, $0.04 USD). Remember multipliers: with a 10× model, one interaction can count as ten premium requests, effectively behaving like $0.40 worth of metered usage at that setting.

An opinionated policy template you can copy

Use this lightweight framework to align engineering, security, and finance without meetings that drag:

• Default model set: Allow included models (GPT‑4.1/4o) for everyone. Disallow all premium models except one low-multiplier default.
• Tiered access: Create three groups—Default (no premium), Power (low multiplier only), Research (all models). Tie group membership to a ticketed request with an expiry date.
• Budget policy: Enable paid usage with a per-SKU cap that equates to no more than $3 per active seat monthly. Hard stop at budget exhaustion.
• Alerting: 75%/90%/100% thresholds go to the engineering ops channel and finance. Require acknowledgment in chat for 90% alerts.
• Review cadence: First business day each month: rotate three high-usage users back to Default unless they renew justification.

A concrete example (numbers you can reuse)

Say you have 200 developers. You enable paid usage and set a $600 monthly budget for the “Copilot premium” SKU. That’s effectively $3 per seat. You allow o4‑mini (0.33×) to everyone in the Power group (40 users) and keep Opus 4 (10×) to the Research group (10 users). If every Power user triggers 100 premium interactions, that’s roughly 3,300 premium requests—about $132 at $0.04 each. If your Research users each do 20 Opus interactions, that’s another 2,000 premium requests—$80. You’re still under $250 with headroom. If usage spikes, the cap stops the bleeding and sends an alert.

Telemetry that matters (and where it lives)

Usage reports are your friend. Pull them weekly during rollout, then monthly. Watch:

• Top users by premium requests: Talk to them; they’re your best signal for model quality and workflow gaps.
• Model distribution: If a high-multiplier model dominates, consider a training session to match the task to the right model.
• Time-of-month spikes: Some teams sprint at month-end. Nudge them earlier so you don’t pay rush premiums after allowances are spent.
• SKU drift: If one AI product starts consuming your pooled budget, set a per-SKU ceiling immediately.

Gotchas we’ve seen in real rollouts

• Budget exists but policy disabled. Teams assume the budget protects them, but if paid usage is disabled, users just get blocked. That might be what you want—just document it.

• Model access set globally, not by group. One enthusiastic admin flips on every model for everyone. The next morning, finance calls. Use groups.

• New SKUs appear quietly. As GitHub adds dedicated SKUs for different AI products, your single pooled budget won’t stretch. Mirror your org chart with cost centers and split budgets accordingly.

• Assuming the old $0 budget is still there. After December 2, treat that assumption as unsafe. Verify in settings today.

• Allowances vs. rate limits. Unlimited included models doesn’t mean infinite throughput. Communicate that to manage expectations during crunch time.

Let’s get practical: your 7‑day rollout plan

Day 1: Audit policy toggles, budgets, alerts. Disable paid usage or set a cap. Restrict high-multiplier models to a small group.

Day 2: Download past usage, identify top 10 users, and host a 30‑minute office hours on “which model when.”

Day 3–4: Set per-SKU budgets. Move three volunteers into the Research group with time-bound access. Capture feedback.

Day 5: Publish a one-page policy to your engineering handbook. Link to the request form for premium access.

Day 6: Dry-run the alert workflow. Trigger a 75% alert (lower your test budget), ensure the on-call acknowledges, and that finance sees it.

Day 7: Reset test budgets to production values. Put a recurring first-business-day budget review on the calendar.

How this fits your broader AI platform strategy

Billing hygiene isn’t exciting, but it’s culture. You can give teams autonomy without opening the corporate wallet. Pair clear model access tiers with budget caps, and you’ll spend where it matters—on complex, high-leverage tasks—while keeping everyday productivity free on included models. If you’re rolling out other AI tools, standardize the same guardrails so your finance team sees a single playbook instead of a zoo of exceptions.

We’ve been helping teams write these playbooks alongside platform upgrades and cloud tuning. If you want a deeper, vendor-agnostic plan, our What We Do overview explains how we align engineering needs with cost controls. For a news-driven deep dive on this specific change, read our explainer on the December 2 switch. And if you’re refreshing your AI posture broadly, we also covered policy shifts in Google’s AI mode playbook to help unify your approach across tools.

What to do next

• Open your Copilot organization or enterprise settings and set Premium request paid usage to Disabled—or set a small, intentional budget with hard stop.
• Restrict high-multiplier models to a named group. Default everyone to included models plus one low-multiplier option.
• Enable per-SKU budgets and alerts to multiple recipients. Test the alert flow.
• Download usage, meet your top users, and document “which model when.”
• Schedule a first-of-month budget and policy review. Make it muscle memory.

Need a second set of hands?

If you’d like us to pressure-test your settings, write the short internal policy, or codify the budget/alert wiring, we can help. Start with our services page, skim the relevant sections in What We Do, or just contact us with your current Copilot plan and model list. We’ll send back a one-pager with recommended settings you can implement the same day.

Treat this December 2 shift as a chance to right-size access, not a reason to clamp down. Put crisp guardrails in place, keep the fast paths open for work that deserves it, and you’ll get the best of GitHub Copilot without the budget whiplash.