GitHub Copilot Premium Requests: Control Your Spend Now

On December 2, 2025, GitHub removed the default $0 budgets for many enterprise and team accounts, changing how GitHub Copilot premium requests are governed and billed. If you run engineering or platform teams, this isn’t an abstract policy tweak—it directly impacts how AI coding features are consumed, throttled, and costed inside your organization. The good news: with a few deliberate moves, you can protect budgets without slowing developers down.

What changed in GitHub Copilot premium requests?

Before December 2, many enterprise and team accounts relied on a pre-set $0 budget to block premium requests beyond the included allowance. That safety net is gone for accounts created before August 22, 2025. Practically, premium usage is now controlled by a paid usage policy toggle and explicit budgets you set—at the organization or enterprise level, and increasingly by product SKU. Pro and Pro+ individual plans aren’t affected.

Two implications matter immediately. First, if your policy is set to allow paid usage and you haven’t configured a budget cap, premium request charges can accrue unexpectedly when new features roll out or usage spikes. Second, GitHub is moving premium usage to dedicated SKUs (for example, coding agent and Spark). That unlocks granular visibility, but it also means you must decide which teams get access to which SKUs—and at what budget levels.

Here’s the thing: those toggles and SKUs can either be a cost-control superpower or a governance mess. The difference is whether you put a playbook in place now, while teams are still adjusting to the new defaults.

Who’s affected and who isn’t?

If you’re an enterprise or team account that had an account-level $0 budget in place before August 22, 2025, you’re in scope for the change. Your charges now flow based on your premium request paid usage policy and any budgets you actively configure. Individual Pro and Pro+ subscribers keep their $0 default, so freelancers and personal accounts won’t feel a thing.

For larger orgs, the impact depends on three patterns we see in the wild:

First, “shadow adoption”: teams quietly enabled premium features during pilot phases and never circled back to re-baseline budgets. When the guardrail disappears, small leaks become large invoices. Second, “max access”: everyone gets all features turned on for productivity. Great for velocity, fragile for finance. Third, “strict deny”: policies default to disabled everywhere. Predictable costs, but you risk a local tool sprawl as devs bounce to unmanaged AI tools.

To land in a sustainable middle, you’ll segment access, set budgets by SKU, and give teams self-serve observability so engineers can calibrate their own usage before finance has to step in.

How the new SKUs work (and why they matter)

GitHub is splitting premium usage across dedicated SKUs so you can track and limit specific AI capabilities rather than lumping everything together. Think of this as migrating from a single, coarse meter to multiple precise meters. You’ll typically see a bundled budget option for simplicity and individual SKU budgets for control. The practical effect is that you can grant, say, the coding agent to your platform group while keeping Spark to a subset of DX researchers running prompt experiments—each with its own cap and alerting thresholds.

That granularity helps with three hard problems. First, chargeback and cost allocation to product lines becomes credible because the usage is separable. Second, security teams can selectively gate the riskiest capabilities behind additional review without blocking everyday completions. Third, procurement can negotiate more intelligently once you can show SKU-specific utilization curves over time.

But there’s a catch: granularity without policy discipline creates thrash. Keep your SKUs mapped to roles and project stages, not personalities. Tie access to identity groups in your IdP, and make budget changes part of change management—not a Slack ping.

The 7-step Copilot spend control playbook

Here’s a battle-tested rollout plan you can execute this week. It’s designed to be pragmatic for a 50–5000 engineer org.

1) Freeze, then learn

Set premium request paid usage policy to Disabled at the top level, then grant exceptions via groups. This gives you a quiet runway to learn current behavior from usage reports without surprise charges.

2) Segment seats by outcome

Create three groups: “Core Devs” (standard completions and chat), “Power Users” (coding agent enabled), and “Experimenters” (agent + Spark). Assign developers by role, not seniority. Limit the Experimenters cohort to 5–10% initially.

3) Define budgets per SKU

Use individual SKU budgets with alerts at 75/90/100%. Start with conservative monthly caps for the power and experiment cohorts. For everyone else, keep bundled budgets in “monitor-only” mode while you collect baseline data.

4) Wire usage to ownership

Tag seats by cost center and team. Export usage to your data warehouse and surface SKU-level dashboards. Make these visible to team leads so they can self-correct patterns before finance has to intervene.

5) Set policy recipes

Write policy-as-code checklists: who can enable a SKU, who approves budget increases, and what tests or reviews are required when a team hits 90% for the month. Keep approvals inside your normal change window to avoid weekend surprises.

6) Close the loop with impact metrics

Measure effects beyond raw usage: PR cycle time, defect rate, and time-to-merge for repos that opt into agent features. If the metrics improve, you’ve got justification to expand access. If not, throttle back and retrain on prompts or workflows.

7) Educate, then enable

Publish a short guide for developers on what counts as a premium request, how to tell in-IDE when a feature is premium, and how to check personal usage. Clarity reduces both overage and fear-driven underuse.

If you want a deeper walkthrough of the policy changes themselves, see our practical primer in what Dec 2 changed for Copilot billing. For a broader view of how AI tooling rollouts evolve in production, our round-up on the new reality of premium requests is a useful companion.

How much will this cost? A modeling approach you can trust

Pricing for premium requests and SKUs can vary by plan and contract. Rather than guess a universal rate, anchor on your own numbers and run a quick sensitivity analysis. Here’s a simple spreadsheet model you can build in ten minutes:

Columns: Team, Seats, SKU (bundled or specific), Premium Request Rate (your contracted per-request or per-unit rate), Estimated Premium Requests per Seat per Month, Budget Cap, Alerts, and Owner. Rows: one per team or product area.

Formulas: Multiply Seats × Estimated Requests × Rate for a baseline. Then add a 20% variance band for growth or new features. Compare that estimate to your budget cap per SKU. If the estimate exceeds 80% of the cap, either increase the cap with approval or reduce the enabled cohort. Track actuals monthly and adjust seat segmentation quarterly.

Two pragmatic tips: first, use observed usage from a 2–4 week pilot to replace estimates. Second, when negotiating, bring SKU-level utilization and outcome metrics (cycle time, defect rate). Procurement conversations go smoother when you tie cost to delivered engineering outcomes, not just “hours saved.”

People also ask: quick answers

What exactly counts as a premium request?

In short: requests that invoke higher-cost capabilities beyond the base Copilot experience. Examples include certain agentic actions, longer-context operations, and some advanced reasoning or Spark-style features. You’ll see these metered separately in usage reports and, increasingly, by dedicated SKUs.

Can I block premium requests entirely?

Yes. Set the premium request paid usage policy to Disabled at the enterprise or org level. You can still enable exceptions for specific groups using role-based access and individual SKU budgets.

Should I use a single bundled budget or per-SKU budgets?

If you’re small or early in rollout, a bundled budget is simpler. As soon as you enable the coding agent or other advanced features for more than one team, switch to per-SKU budgets. It’s the only way to allocate cost cleanly and avoid “noisy neighbor” problems between teams.

How do I prevent month-end bill spikes?

Use alert thresholds at 75/90/100% on every active budget and route them to both the team lead and platform engineering. Consider a soft-disable at 100% that flips to hard-disable on the third alert within a quarter. That gives teams one “oops” per quarter without blowing up your forecast.

Will this slow my developers down?

It shouldn’t. The pattern that works is “deny by default, enable by cohort.” Give agent features to the teams that can convert them into improved cycle time, then expand based on data. Pair access with training on prompts and workflows—it’s amazing how much waste disappears when devs understand which actions are premium.

A rollout checklist you can copy

Use this to move from scramble to steady state in one sprint:

• Audit: Export premium request usage for the last 30–60 days; group by team and feature.
• Policy: Set paid usage to Disabled at the top level; whitelist two pilot teams.
• Budget: Create per-SKU budgets with alerts; set conservative caps.
• Identity: Map SKUs to IdP groups; tie requests for access to an internal form.
• Dashboards: Publish SKU usage, trends, and caps vs. actuals weekly.
• Training: 30-minute workshop on premium features and usage visibility.
• Review: After two weeks, compare engineering outcomes to justify expansion.

Where this is headed (and how to prepare)

Expect more capabilities to land as distinct SKUs over the next quarters. That’s useful for control and forecasting, but it requires intentional product management of your internal AI stack. Treat Copilot alongside any other platform component: set SLOs, define owner teams, and build feedback loops with developers. As usage concentrates in agent workflows, move guardrails closer to the IDE with templates, policy hints, and clear “this is premium” affordances for developers.

Also expect integration with your broader cloud cost story. If you’re building on AWS, the cross-cloud networking moves happening right now make it easier to stitch together AI services across providers. For instance, see our practitioner’s guide on what architects should do with AWS Interconnect and the follow-up on what changes with Google as a launch partner. The lesson carries over: tighter controls, clearer contracts, better observability.

What to do next this week

For platform or engineering managers:

• Flip paid usage to Disabled globally, then re-enable for two pilot cohorts.
• Create per-SKU budgets with alerts; publish dashboards to team leads.
• Map access to IdP groups; require a short justification to join a higher-SKU cohort.
• Run a two-week agent pilot; measure cycle time and merge rate deltas.

For finance, procurement, and leadership:

• Collect three months of SKU-level usage and outcome metrics before renegotiation.
• Fund a “shared innovation” budget for 5–10% of seats to test new features safely.
• Adopt a “one-in, one-out” rule for premium features until ROI is demonstrated.

Need help turning this into a living policy with dashboards and guardrails? Our team has implemented this pattern for orgs ranging from fintech scale-ups to multi-division enterprises. See what we do for platform and AI engineering or reach out via our contact page. And if you’re keeping up with the broader wave of AI launches (Nova models, Forge, and more), our running analysis on the Bybo blog will keep you sharp without the noise.

Bottom line

The default $0 safety rail for many enterprise and team accounts is gone, and that changes how you run AI-assisted development at scale. Embrace the new model: disable globally, enable by cohort, budget by SKU, expose clear usage, and tie access to outcomes. With those habits in place, you’ll keep spend predictable while giving your best teams the headroom to move faster.