AI vendors are fighting for market share in a market where buyers increasingly refuse to stay loyal. New spending data from Ramp shows Anthropic widening its lead over OpenAI among U.S. businesses in July. Anthropic reached 43.5% paid adoption, up 1.1 percentage points from June. OpenAI reached 39.7%, a gain of just 0.23 points. Yet an earlier Ramp analysis found that 52% of customers paying either Anthropic or OpenAI were paying both. Ramp called many of those customers “free agents,” a sign that many companies are still hedging their bets rather than committing to a single provider.
That matters because vendor loyalty shapes far more than market share. Companies that keep multiple AI options open can push harder on price, data retention, security terms and performance. They can send different workloads to different models and avoid building every critical process around one supplier. As AI moves deeper into production, that flexibility could become a meaningful source of negotiating power.
The bigger question is whether this behavior survives once AI becomes harder to untangle from corporate systems. Enterprise software has a long history of becoming sticky after companies train employees, connect data and build workflows around it. AI could end up following the same path. Or buyers may keep treating models as interchangeable components, forcing vendors to compete for each new workload instead of relying on permanent lock-in.
No AI Vendor Lock-In
Companies once treated artificial intelligence providers like permanent utilities. This is because typical cloud and enterprise software rewards commitment. A company picked a database, cloud provider or CRM system, built around it and spent years trying to avoid the pain of moving somewhere else. Generative AI is developing under a different set of rules.
Anthropic’s rise has been sharp. In March, 30.6% of businesses in Ramp’s index paid Anthropic, compared with 35.2% for OpenAI. One month later, Anthropic moved ahead for the first time, reaching 34.4% as OpenAI fell to 32.3%. By June, the gap had widened again, with Anthropic at 42.4% and OpenAI at 39.5%, and by July Anthropic pushed to 43.5%. This is showing acceleration for Anthropic versus OpenAI in the market.
Ramp reported in May that 52% of customers using Anthropic or OpenAI used both, and 43% percent of Anthropic customers had switched from another generative AI vendor. Ramp contrasted that behavior with software categories such as CRM, ERP and HR systems, where moving vendors can require lengthy migrations and painful retraining.
Ramp has been tracking that behavior for years. Its 2024 analysis found that only 3% of OpenAI customers used Anthropic at the start of that year. By September, the figure had reached 22%. The arrival of new Claude and GPT models produced sharp changes in spending patterns inside months, not years.
For enterprise technology executives, that creates an unusual procurement challenge. The vendor sitting at the top of a benchmark today may not hold that spot next quarter. A lower priced model may be good enough for one workload, while another may be worth a premium for coding or research and yet another may win approval on security or data handling. The buyer’s job becomes less about finding a permanent winner and more about deciding which dependencies are worth creating.
AI Spending Keeps Growing, Even As Allegiances Shift
The market itself is still expanding. Ramp says 50.4% of businesses in its index paid for AI in March 2026, the first time the figure crossed 50%. By July, paid adoption had reached 55.7%. Ramp gets its data and builds the index from corporate card and invoice payments rather than survey responses, which makes it a useful measure of businesses that are actually spending money on AI services.
A rising adoption rate does not mean that spending is unlimited, however. Ramp’s August report points to signs that buyers are becoming more selective about how much they will pay for frontier capability. Its clearest example is Anthropic’s Claude Fable 5.
Anthropic launched Fable 5 on June 9, describing it as its most capable generally available model at the time. The company priced it at $10 per million input tokens and $50 per million output tokens, putting it well above cheaper models intended for routine workloads. Anthropic paired that capability with unusually strict safeguards and a new retention policy for its most capable systems.
Three days after Fable 5 appeared, Anthropic suspended access following a U.S. government export control directive. Access returned globally on July 1 after those controls were lifted. Anthropic spent part of the interruption modifying its safeguards following scrutiny of a reported jailbreak technique. Even with that disruption, the spending numbers are still striking.
Ramp reported in August that Fable 5 accounted for only 6% of Anthropic tokens purchased by businesses during its first month back and 11.4% of dollars spent on Anthropic models. Ramp compared that with OpenAI’s GPT 5.6 Sol, which represented 25% of OpenAI tokens and 23% of spending in its data. The message for vendors is that benchmark supremacy does not mean that customers will always pay for the most expensive or capable model.
The Multimodel Company Is Already Emerging
Another Ramp dataset offers a glimpse of where this purchasing model may go.
As of June, 5.8% of AI spending businesses in Ramp’s data used multi-model serving platforms, services that provide access to many open source and proprietary models. This is in addition to maintaining direct subscriptions with the big U.S. labs. Among businesses using model serving platforms, 85.8% still used OpenAI, 93.2% used Anthropic and 96.4% used at least one of the two.
This means that AI systems are being seen more as interchangeable commodities than they were before. A software team may want a frontier model for a difficult coding task and a cheaper model for repetitive processing. A legal department may care more about contractual data controls, while finance may care about unit economics. Security may reject a system that engineering loves. Once AI spreads through a large company, the idea of a single companywide “best model” starts to look less useful.
But with multivendor purchasing, things can quickly become multivendor chaos. Every new provider creates another API, pricing system, security review, contract and failure mode. Swapping models sounds simple until a production workflow has been tuned around one model’s behavior. Output formats differ and tool calling can behave differently. Prompts that work beautifully with one system can degrade on another.
That could mean keeping model specific code away from the core application, developing shared evaluation tests before changing providers and measuring cost by workload rather than comparing headline token prices. Procurement teams can negotiate retention, deletion and security terms before a model becomes embedded in critical systems. None of this requires a company to rotate vendors every quarter. It gives the company the option.
The Illusion of Permanent Vendor Lock-In
We’re still in the early, experimental days of AI usage in enterprises. A company experimenting with several coding assistants is not making the same decision as a bank embedding a model into a regulated customer workflow. Early adoption encourages experimentation, while production creates friction. More AI applications will acquire switching costs as they become attached to internal data, evaluations, controls and employee routines. So the low loyalty visible today may not last forever.
Yet Ramp’s numbers make one point difficult to ignore. Enterprise AI purchasing has not settled into the familiar pattern of one vendor, one stack and one long relationship. Anthropic can lead paid adoption in July and still share customers with OpenAI. Open source access can rise without displacing either company. The lesson is that no one should be resting on their laurels or crowning a winner yet.