Why are monthly credits restricted, often locking users out of the service or forcing the purchase of more credits after only using 75% of their allowance mid-cycle? (Focuses on the reported restrictive usage policy that prevents customers from fully utilizing what they've paid for).

The users complaint regarding monthly credits being restricted mid-cycle, locking users out of service with a portion of the allowance remaining, stems from a critical—and often poorly communicated—intersection of Billing Policy, System Quotas, and the Variable Cost of Resources.

This is not a simple expiration of a credit balance, but rather a simultaneous exhaustion of one or more hidden limits or the activation of safety mechanisms that were designed to prevent catastrophic overspending, but which feel punitive to the user.

Here is a comprehensive breakdown of the multiple, intersecting reasons why a user might get locked out of service with, for example, 25% of their Monetary Credit Balance still showing:

I. The Critical Distinction: Monetary Credits vs. System Quotas

The primary source of confusion is the difference between what the customer sees (a single, large pool of Monetary Credits) and the System Limits that the cloud provider enforces to manage its own infrastructure.

A. The Monetary Credit Balance (What you see)

This is the dollar amount or "Compute Points" you have on your account. When you purchase a $500 monthly subscription or redeem a $300 promotional code, this is the amount that decreases with every charge.

B. The System Quotas (What you hit)

Cloud providers enforce strict, non-monetary resource limits to protect the stability of their infrastructure and manage risk. These are often the true bottleneck:

1. Usage Quotas (Non-Monetary Limits): These limits are applied to specific, high-demand resources and are often set extremely low by default, especially for new accounts or those still using credits/promotions.

   • Core Quota: A limit on the number of CPU cores or, critically, GPU cores you can provision at any one time in a specific region. GPUs are expensive and scarce. A user might have $1,000 in credits, but if their GPU core quota is set to '1', they can only run one high-end machine. Attempting to start a second will result in an error, even with remaining credits.

   • IP Address Quota: A limit on the number of public-facing IP addresses a project can use.

   • API Rate Limits: A limit on how many calls can be made to a specific service's API per minute. Rapid or excessive calls hit this limit instantly, regardless of the credit balance.

2. Credit Risk Quotas: For accounts using promotional credits or a "spending limit" feature (common in free trials and educational accounts), the provider imposes an implicit, risk-based cap on resource usage.

   • The Exposure Model: A cloud provider calculates its maximum potential loss based on the resources you provision. If an expensive GPU instance costs $5.00/hour, and there are 30 days left in the cycle, the total potential cost is $5.00 \times 24 \times 30 = \$3,600$. If your Monetary Credit Balance is only $1,000, the provider will restrict your ability to launch that resource because the credit risk exposure exceeds the available balance, even if you still have 25% of your credits remaining.

   • The Forced Shutdown: When your credits dip below a certain threshold (say, 15-25%), the system may preemptively shut down running resources to ensure they don't consume the entire remaining balance and result in an unbillable charge to the provider.

II. The Variable Value of a "Compute Credit"

The user believes that $1.00 in credits represents a fixed amount of work, but the value is highly variable. The service lock-out often occurs because the user has unexpectedly started consuming a high-cost resource.

The Mid-Cycle Shock: A user starts the month with a large pool of credits, runs a few cheap CPU workloads, and is feeling safe. Mid-cycle, they launch a powerful, expensive GPU machine for a large training job. This single action starts consuming credits at $5x$ to $10x$ the rate of their previous usage. The remaining 25% of the credit balance is eaten up in hours, leading to the lock-out. The system shut down because the rate of consumption had reached an unsustainable level, exhausting the money before the clock ran out.

III. Policy and Commercial Explanations

A. Non-Transferable and Non-Rolling Credits

For monthly subscription plans, the credits are typically designed to expire at the end of the billing cycle. This policy is a core part of the recurring revenue model:

• Discouraging Stockpiling: If credits rolled over, users could save up several months of credits, run a massive workload once, and then downgrade their subscription, harming the provider's monthly revenue predictability.

• Encouraging Continuous Use: By making credits "use-it-or-lose-it," the provider incentivizes the customer to keep their workloads running, leading to increased monthly usage and eventually transitioning from subsidized credits to full-rate billing.

B. The Spending Limit Feature (Safety/Paywall)

Cloud services, particularly those aimed at individuals, developers, or trial users, have a Spending Limit feature enabled by default.

• Preventing Bill Shock for the User: This feature is often marketed as a protection for the customer. When the dollar-based usage hits 100% of the set budget (which is exactly what the credits cover), the system performs a hard shut-down of all non-essential resources to ensure the user doesn't receive a massive surprise bill at the end of the month.

• Forcing Commitment: When the spending limit is hit, the only way to continue using the service is to disable the spending limit and switch to a full "Pay-as-You-Go" account, which requires the user to link a credit card. This is a deliberate commercial gate designed to convert the user from a subsidized/free tier to a fully committed, billable customer.

Summary of the "75% Remaining" Lockout

A user being locked out with 25% of their monetary credits remaining is usually the result of hitting a non-monetary system quota or having a very rapid rate of spending on an expensive resource (like GPUs or network egress) that triggers a credit risk guardrail intended to prevent the user from over-drafting their account before the monthly cycle ends. The solution lies in proactive monitoring and requesting increases to the specific system quotas.