GitHub Copilot premium requests are about to matter a lot more. Beginning December 2, 2025, GitHub is phasing out legacy $0 premium‑request budgets for many enterprise and team accounts. That means your paid‑usage policy—not an old static budget—will decide whether premium usage is blocked, allowed, or capped. If you’re responsible for engineering spend or developer experience, this is the week to audit settings, create budgets per tool, and train your team on what counts as a premium request.
Here’s the thing: premium requests touch the heaviest features and models—agentic coding runs, certain chat tasks, and tools like Spark. They’re limited per user per month, they reset at 00:00:00 UTC on the first of the month, and overages bill at a predictable per‑request rate. With the December change, guardrails that quietly kept costs at zero may vanish unless you act.
What exactly is a premium request in Copilot?
In GitHub’s world, a “request” is a metered interaction with Copilot. Most everyday completions and included chat models on paid plans don’t consume premium requests. But when developers invoke heavier capabilities—like the coding agent tackling a multi‑file change, or Spark orchestrating a deeper reasoning workflow—those interactions pull from a monthly pool of premium requests instead of being unlimited.
Two practical nuances catch teams off‑guard: first, model multipliers. Some models or features count as multiple premium requests for a single action. Second, dedicated SKUs. Since November 2025, premium usage for tools like the coding agent and Spark is tracked separately. That’s great for cost visibility, but it means you must create budgets per SKU rather than assuming one global budget covers everything.
Allowances, costs, and reset times (the numbers you need)
If you’re building a forecast, start with these reference points:
- Free plan: up to 50 premium requests per month.
- Pro: 300 premium requests per month.
- Pro+: 1,500 premium requests per month.
- Business: 300 premium requests per user per month.
- Enterprise: 1,000 premium requests per user per month.
When you exceed your allowance, additional premium requests typically bill at $0.04 each. Multipliers apply: if a feature costs 4 premium requests per run and you’re in overage, that’s $0.16 per execution. Budgets and alerts are your friend because counters reset at the start of each month—00:00:00 UTC—not on your local midnight. If your teams are globally distributed, that detail matters for end‑of‑month work sprints.
On the SKU front, Spark is a handy mental model. A single Spark prompt can consume a fixed number of premium requests rather than one. The coding agent may vary based on model and task depth. The takeaway: your mix of tools and who uses them will drive cost far more than seat count alone.
Why December 2 changes the risk profile
Prior to this change, some organizations relied on account‑level $0 premium‑request budgets as a catch‑all. That band‑aid goes away for accounts created before August 22, 2025. After December 2, whether you incur charges depends on the state of your premium request paid‑usage policy and the budgets you’ve set for each premium SKU.
In practice, the removal of those $0 budgets reduces hidden protections. If your policy allows paid usage and you have no per‑SKU budget caps, you can rack up thousands of paid requests in a single week of heavy agent use. Conversely, if your policy is disabled, your power users may be blocked mid‑task. Neither surprise is good.
H2: GitHub Copilot premium requests explained to developers
Developers mainly want to know two things: “Will this task consume premium requests?” and “What happens when I hit the cap?” Here’s the shorthand we put into team handbooks:
- Use included models for routine chat and completions; they don’t burn premium requests.
- Agentic tasks and specific advanced models consume premium requests—sometimes more than one per run.
- When you run out, you can still use included models; advanced tasks will be blocked unless your org allows paid usage or you wait for the monthly reset.
It helps to nominate a Slack channel where engineers can see budget alerts and request a temporary bump. That keeps velocity up without turning finance into a help desk.
People also ask: Do unused premium requests roll over?
No. Each user’s allowance resets monthly; nothing rolls over. That’s why teams often see a spike in agent usage the first week of the month and a drop‑off at month‑end as developers ration requests. If your usage pattern is lumpy—say, a migration cutover or a code‑freeze—you should temporarily raise budgets around that event rather than paying overage rates reactively.
People also ask: How do I stop surprise charges?
Set your premium request paid‑usage policy to “Disabled” globally until you’ve configured budgets, alerts, and education. Then enable paid usage with caps where it makes sense. This one‑time switch prevents drift while you roll out a sustainable plan.
A practical governance blueprint: SLAM it
We use a four‑step framework—SLAM—to operationalize premium requests across engineering orgs:
S — Set policies
Decide at the organization or enterprise level whether paid usage is enabled, disabled, or capped. If your default is enabled, define budget caps per SKU (coding agent, Spark) before you flip the switch. Document which teams are allowed to initiate large agent runs (e.g., platform, SRE) and who approves budget increases.
L — Limit with budgets
Create account‑level budgets and per‑SKU budgets with 50/80/100% alerts. Set alert recipients to Slack channels owned by engineering, not just finance, so developers see the signal and self‑throttle. For high‑variance teams, add a secondary alert at 120% to catch true overage and kick off an incident‑style review.
A — Assign ownership
Appoint an engineering budget owner per business unit and a central administrator to monitor reports weekly. Publish a one‑pager: what counts as premium usage, how to check remaining balance, and how to request more. Make it part of onboarding and refresh it each quarter as model lineups and multipliers evolve.
M — Monitor and tune
Review usage by team and by SKU every Friday. Identify your top users and ask if their workflows can shift to included models for routine tasks. Consider whether those heavy users should be on a higher plan tier, or whether a temporary project budget is smarter. Add seasonality to your forecast—end‑of‑quarter release pushes consume more premium capacity.
Edge cases and gotchas we see in the field
Multiple licenses: power users in multiple orgs must select a “Usage billed to” entity, or their premium requests can appear to vanish. Educate them. Also, individual users who subscribed to Pro or Pro+ via mobile generally can’t buy additional premium requests; if they need to, migrate them to web billing. Finally, watch UTC resets: a North America team working late on the 31st may get a “free reset” at 7 p.m. Eastern; that can be a feature, not a bug, for carefully scheduled runs.
Model drift matters, too. Included models may change and multipliers can be updated. Don’t hard‑code assumptions into processes or dashboards; build your governance on alerts and monthly reviews rather than one‑time documentation. And if you use Spark heavily, assume higher request consumption per prompt until you verify the exact rate in your reports.
What this means for engineering leaders and CFOs
For leaders, this is a culture and controls moment. If you clamp down too hard, you’ll push developers away from the tools that actually move delivery metrics. If you go laissez‑faire, December’s change can turn into an ugly finance ticket by January. Treat Copilot like any other cloud service: align policy to business value, not to fear or hype.
For CFOs, the predictability is actually better than it looks. Premium requests are granular, fixed‑price units with SKU‑level visibility. That’s easier to manage than surprise SaaS overages tied to elastic compute. But it only works if you flip on alerts and review them on a cadence. No alerts, no control.
H2: GitHub Copilot premium requests and your 2026 plan
December is about preventing surprises. Early 2026 should be about optimization. Expect GitHub to keep refining included models, expand agent capabilities, and adjust multipliers. Bake that into your budget scenarios. If your devs are leaning on agentic workflows for test generation, refactors, or dependency upgrades, capture the time saved and set performance targets tied to those use cases. The spend is easier to defend when you can point to cycle time or reliability wins.
Implementation checklist: 90 minutes to safe defaults
Here’s a fast sequence we’ve used with teams moving ahead of the Dec 2 switch:
- Open Copilot settings and confirm your premium request paid‑usage policy. If you’re not sure yet, set it to Disabled.
- Create an account‑level budget with alerts at 50/80/100%. Route alerts to a Slack channel owned by engineering leadership.
- Create per‑SKU budgets for coding agent and Spark. Use the same alert thresholds.
- Identify your top 10% users from usage reports. Interview them for use cases; decide whether they need Enterprise tier or temporary budget bumps.
- Publish a one‑page guide: included vs premium features, how to check balances, how to request more. Pin it in your engineering handbook.
- Schedule a 20‑minute weekly review for December to watch the trend while the new policy settles.
People also ask: Should startups enable paid usage at all?
Usually yes, with caps. Most startups benefit from agent assistance during migrations, test scaffolding, and documentation. Start with an account‑level cap that’s comfortably under 1% of monthly cloud spend and revisit monthly. If you’re pre‑revenue or in stealth, leave paid usage off until you’ve trained your team on included models and you have a path to measure the impact of premium features.
A quick scenario to sanity‑check your settings
Say your 60‑engineer org is on Copilot Business (300 premium requests per user). Ten power users lean on Spark during a crunch. Each runs 30 Spark prompts this week at four premium requests per prompt. That’s 1,200 premium requests, well within the included pool per user. But if five users hit their monthly 300 and keep going for two more days at 50 prompts each, that’s 250 overage requests. At $0.04 per request, you’re staring at $10 in overage—hardly catastrophic. The bigger risk is when an automation script or a novel agent workflow multiplies across a team; that’s why alerts and per‑SKU budgets matter more than raw price.
What to do next (this week)
Don’t wait for December 2 to test your setup. Do these now:
- Verify your premium request paid‑usage policy. Flip to Disabled until budgets and alerts are live.
- Create per‑SKU budgets for coding agent and Spark, plus an account‑level cap.
- Route alerts to engineering‑owned channels and publish a one‑pager for devs.
- Review usage reports, identify top users, and right‑size plan tiers or budgets.
- Schedule a Friday 15‑minute review during December to keep the trendline honest.
Where this intersects with broader platform planning
If this feels like cloud FinOps meets developer productivity, you’re not wrong. The same playbook we use for runtime upgrades and policy shifts applies here: pilot with power users, measure, then scale. If you’re planning runtime migrations or AI policy changes in parallel, coordinate the budgets and comms. We’ve documented this approach in our recent pieces on managing platform and policy shifts—have a look at our deep‑dive on the December 2 Copilot switch and our broader guidance in what we do for engineering teams. If you want examples of how we execute and measure changes, our portfolio case notes and blog are a good starting point.
FAQ for owners and admins
Can we just block everything and move on?
You can, but you’ll trade cost risk for productivity drag. Block by default while you set budgets and education, then enable paid usage with caps for the teams that benefit most.
How do we forecast for 2026?
Assume a base where 10–20% of engineers consume the majority of premium requests. Tie budget increases to measurable gains—faster code reviews, fewer defects, or shorter release cycles—and review quarterly as model lineups evolve.
What about compliance and privacy?
Treat AI policy like any other developer tool policy: document allowable data classes, limit access by role, and audit usage. If you’re subject to stricter controls, put premium requests behind a request process that captures a Jira ticket and an approver.
Zooming out, this is a small but decisive shift in how AI developer tools are governed: less “free until it isn’t,” more explicit choices about value and cost. Make those choices this week so the December 2 change is a non‑event—not an incident.
If you want help pressure‑testing your Copilot setup or designing sensible defaults that won’t whiplash your developers, our team works on pragmatic guardrails, usage analytics, and training. Explore our AI tooling governance services or get in touch for a quick working session before the switch.
