Leaked internal documents published by TechCrunch reveal OpenAI pays Microsoft recurring, substantial sums to run its models on Microsoft Azure AI, reportedly in the hundreds of millions of dollars annually. The files also break down per inference charges what OpenAI pays each time a model generates a response, making visible how quickly cloud compute cost and runtime cost can balloon as user activity scales. Could these disclosures reshape how businesses budget for and buy advanced AI services?

Background why cloud and inference pricing matter

Modern large language models create two distinct cost buckets. First are licensing and development outlays for model training and software. Second are ongoing runtime costs the cloud compute and inference charges incurred whenever a model answers a prompt or powers a product feature. For many organizations that second bucket is the largest and most variable. Until now the exact magnitude of those runtime payments between a leading model developer and a leading cloud provider has been largely private.

Key findings from the leaked OpenAI documents

• Scale of payments The documents indicate OpenAI makes payments to Microsoft that amount to hundreds of millions of dollars per year under a multiyear revenue share agreement.
• Revenue share structure The arrangement is described as an ongoing revenue share rather than a flat fee or one time purchase tying Microsoft returns to product usage and growth.
• Inference cost transparency The files break down per inference costs the charge incurred each time a model generates output and show how unit costs compound across millions of calls.
• Cost sensitivity to scale Small per call charges become a dominant line item once usage reaches high volumes especially for consumer facing or embedded enterprise features.
• Official posture Public statements from both companies emphasize the strategic OpenAI Microsoft partnership but do not confirm the specific figures in the leaks. OpenAI points to investments in efficiency and custom hardware as steps to reduce dependence on third party cloud compute.

Implications for procurement product and finance teams

These disclosures convert abstract concerns about AI operating expense into concrete commercial terms. For enterprise buyers this matters for how to model total cost of ownership and make procurement decisions.

• Product design and monetization Visibility into runtime cost helps explain paywalls usage limits and tiered features. Charging by usage or gating advanced features lets vendors manage variable bills and align AI pricing with operating cost.
• Total cost of ownership shifts Access fees or model licenses are only part of the equation. For scaled deployments cloud compute cost and inference charges can become the primary recurring expense. Organizations that ignore runtime spend risk major budget overruns.
• Vendor dependency and price volatility A multiyear revenue share with a single dominant cloud provider can create vendor lock in. If cloud pricing or contract terms change customers may face indirect impacts through higher product prices or reduced feature availability.
• Operational and team impacts Product teams may optimize UX and feature sets to reduce costly inference calls for example by batching requests caching results or precomputing responses for common queries.

How buyers should respond

Businesses evaluating AI vendors should treat infrastructure pricing as a first class negotiation item. Below are tactical steps to analyze and control runtime cost.

• Model the total cost of ownership including per inference and cloud charges not just license fees.
• Seek transparent unit pricing and escalation limits in vendor contracts and negotiate caps or predictable tiers.
• Consider architectural choices that reduce runtime usage such as caching smaller specialist models client side inference where feasible and batching requests.
• Evaluate multicloud or hybrid architectures and on premises or specialized hardware to avoid single provider risk.
• Track inference metrics closely and surface them to finance and product leadership on a monthly basis.

Conclusion

The TechCrunch leak reveals more than a headline number. It highlights a structural truth of modern artificial intelligence the ongoing cost of running models at scale is a fundamental economic constraint that shapes AI pricing product design and vendor relations. For companies planning AI deployments the takeaway is clear analyze and calculate runtime cost include it in vendor evaluations negotiate transparency into contracts and optimize architecture and operations to control scale cost. As AI adoption grows cost visibility will be as important as model capability in deciding who wins in the market.