Codex now offers more flexible pricing for teams

# Codex now offers pay-as-you-go pricing for teams Source: [https://openai.com/index/codex-flexible-pricing-for-teams/](https://openai.com/index/codex-flexible-pricing-for-teams/) OpenAIWe’re making it easier to just build things\. Starting today,**teams on ChatGPT Business and Enterprise can add Codex\-only seats to their workspaces with pay\-as\-you\-go pricing**, giving full access to Codex without a fixed seat fee\. Now, small groups can begin pilots, prove value in a few critical workflows, and easily expand from there\. We’re also making Codex pricing easier to understand\. Codex\-only seats have no rate limits, and usage is billed on token consumption\. This gives you a clearer view of how usage turns into spend and makes it easier to track costs across budgets, workflows, and teams\. Teams that need broad ChatGPT access can continue using standard ChatGPT Business seats that do include Codex usage limits\. To make that path more accessible,**we’re lowering the annual price of ChatGPT Business from $25 to $20 per seat**\. To support your adoption,**eligible ChatGPT Business workspaces can receive $100 in credits for each new Codex\-only team member that joins and starts using Codex**, up to $500 per team, for a limited time\. To activate the offer, add Codex\-only seats to your workspace or create a[new ChatGPT Business workspace⁠\(opens in a new window\)](https://chatgpt.com/codex/team/start)1\. These changes are designed to make adoption easier at a time when Codex usage across teams is already accelerating\. Already, more than 9 million paying business users rely on ChatGPT for work and more than 2 million builders now use Codex every week\. Within ChatGPT Business and Enterprise, the number of Codex users has grown 6x since January\. Teams at companies like Notion, Ramp, Braintrust, and Wasmer are already using Codex to accelerate their engineering workflows\. Across these teams, Codex provides faster execution, more repeatable workflows, and a clearer path from individual AI experiments to broad adoption\.