ChatGPT Enterprise Gets Granular Spend Controls and Usage Analytics
On June 18, 2026, OpenAI announced a significant upgrade to ChatGPT Enterprise administration: new credit usage analytics and updated spend controls. These features address one of the biggest friction points organizations face when scaling AI -- keeping costs predictable without killing adoption.
The Problem OpenAI Is Solving
As ChatGPT and Codex spread across organizations, the old binary model (either a user has access or they don't) breaks down. Some teams need heavy Codex usage for shipping features, while others only need occasional ChatGPT queries for research or drafting. Without granular controls, admins face a difficult choice: give everyone unlimited access and accept unpredictable bills, or restrict access broadly and frustrate power users who drive the most value.
The new controls fix this with a three-tier system:
1. Workspace-wide default limits -- a baseline credit budget for every user 2. Group-level limits -- different caps for different teams (e.g., engineering gets more, marketing gets standard) 3. Individual overrides -- custom limits for specific power users who need extra capacity
Users can see their credit usage against their available budget and request additional credits directly in the interface, including context about what they're working on. Admins can approve or deny these requests, keeping control while allowing flexibility.
New Analytics: A Unified View
The Global Admin Console now brings ChatGPT and Codex credit usage into a single, unified view. This is a significant improvement -- previously, ChatGPT Enterprise and Codex billing were handled separately, making it hard for large organizations to understand their total AI spend.
Admins can now:
• Track credit usage and consumption trends over time
• Identify top users and emerging usage patterns
• Break down spend by user, by product (ChatGPT vs. Codex), and by model
• Export data through a unified Cost API for deeper analysis in existing financial dashboards
For organizations with hundreds or thousands of users, the unified view makes cost attribution and chargeback to business units much more practical.
Real-World Feedback
Zipline co-founder Ryan Oksenhorn provided a testimonial in OpenAI's launch post:
> *"We asked the team at OpenAI to build usage analytics to help find and train-up folks who haven't adopted Codex, and for granular usage controls to keep spend predictable. These new tools are helping us faster scale productivity of our employees while keeping safeguards in place."*
This is a revealing quote -- it highlights that usage analytics serves a dual purpose: not just cost control, but also identifying under-adopters who might benefit from more training or access.
Availability and Next Steps
The new analytics and controls are available immediately for all ChatGPT Enterprise admins. Users in those workspaces can view their own credit usage by navigating to workspace settings.
Combined with the earlier custom role usage limits released earlier this year, organizations now have a comprehensive toolkit for managing AI spend at scale. The custom roles feature let admins set different limits for different user roles; the new updates add group-level controls and individual overrides on top of that.
Practical Takeaways for Enterprise Admins
If you manage a ChatGPT Enterprise deployment:
1. Start with workspace defaults. Set a reasonable baseline limit that covers typical usage for most employees. 2. Layer group-level rules. Create higher limits for engineering teams doing heavy Codex work, and lower limits for occasional users. 3. Use individual overrides for power users. Rather than raising limits for everyone, let power users request additional credits with context. 4. Monitor trends via the Cost API. Pump AI spend data into your existing financial dashboards for chargeback and budgeting. 5. Watch for under-adoption too. As Zipline highlighted, analytics can reveal who hasn't adopted the tools yet -- potentially uncovering training opportunities.
Sources
• OpenAI: New usage analytics and spend controls