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Model-Serving Platforms Reach 6.1% of AI-Spending Businesses, While Frontier Spend Holds

The early shift is showing up in specialized deployments and model-serving platforms, while direct business spending still favors Anthropic and OpenAI.

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Model-Serving Platforms Reach 6.1% of AI-Spending Businesses, While Frontier Spend Holds

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Model-serving platforms that offer open-source and Chinese-developed models reached 6.1 percent of AI-spending businesses in Ramp’s customer data in July, up from 4.5 percent in January. That is a meaningful early shift toward cheaper, more adaptable deployment options—but it is not evidence that frontier-model vendors are being displaced. Ramp is counting businesses paying for these platforms, not tokens processed or total enterprise adoption, so the figure is directional rather than a market census. The appeal is practical: open-weight models can be fine-tuned for narrow workflows, while giving companies more control over cost and data. Thomson Reuters built Thomson-1 for document review on Snowdon, which adapted Alibaba’s Qwen model, replacing Anthropic’s Claude for those tasks. Harvey says its Tenet system, post-trained on Moonshot AI’s Kimi K3, outperformed its base model and named U.S. frontier systems on complex legal agentic work. Pricing helps explain the interest. Z.AI lists GLM-5.3-Flash at fifteen cents per million input tokens and fifty cents per million output tokens. Yet direct spending still favors the leaders. In July, Anthropic held 43.5 percent of Ramp’s business AI spending, and OpenAI held 39.7 percent; both shares increased. The key question is whether these specialized deployments remain an efficiency layer—or eventually pull meaningful spending away from premium providers.

Story brief

3 key points

Ramp’s customer spending data points to broader experimentation with model-serving platforms, but not clear displacement of frontier vendors. The share of AI-spending businesses using these platforms rose from 4.5% in January 2026 to 6.1% in July, while Anthropic and OpenAI still captured 43.5% and 39.7% of July business AI spending. The practical signal is deployment flexibility: companies can adapt cheaper...

  1. 01

    Ramp’s measure tracks paying businesses, not token volume or total enterprise adoption, so 6.1% is directional rather than a market census.

  2. 02

    Thomson Reuters’ Thomson-1 adapts Alibaba’s Qwen and replaces Claude for document review.

  3. 03

    Harvey says Tenet, post-trained on Kimi K3, beat its base model and named U.S. frontier models on complex legal agentic tasks.

The share of AI-spending businesses paying for platforms that offer open-source and Chinese-developed models rose to 6.1% in July. The movement may indicate growing enterprise interest in lower-cost, adaptable model options, even as direct spending remains concentrated with frontier-model vendors.

Ramp’s AI Index put the share at 4.5% in January 2026. The increase measures businesses paying for model-serving platforms, not the volume of work they send through them; those platforms offer access to open-source and Chinese-developed models.

Ramp tracks token and subscription spending across its customer base, so the figure is a directional measure of its customers’ buying rather than a census of enterprise AI.

Open foundations offer a different operating model

Fortune describes open-weight models as generally cheaper than proprietary alternatives, with more opportunity for fine-tuning on particular datasets and greater control over how company data is handled. Moonshot AI’s Kimi K3 is an open-weight model characterized as close to leading proprietary systems in coding and agentic work, or multistep tasks a model carries out.

Z.AI said its GLM-5.3-Flash costs $0.15 per million input tokens and $0.50 per million output tokens, adding another low-cost Chinese option.

Platform buyers increased over six months

AI-spending businesses paying for model-serving platforms
4.5%6.1%
share of businesses

Ramp recorded a 1.6-percentage-point increase in the share of AI-spending businesses paying for these platforms.

Two specialized deployments show the appeal

Thomson Reuters built Thomson-1, an in-house document-review model based on Snowdon, which adapted Alibaba’s open-source Qwen model. The company said Thomson-1 will take over document-review tasks that previously ran on Anthropic’s Claude.

Harvey took a related but different route. It said Harvey Tenet was post-trained on Kimi K3 and outperformed its base model and named U.S. frontier systems on complex legal agentic tasks. Harvey had previously customized closed models from Anthropic, OpenAI and Google.

The leading vendors still command direct spending

Ramp said open-source growth had not yet directly reduced spending shares for either leading provider. In July, Anthropic held 43.5% of business AI market share after gaining 1.1 percentage points, while OpenAI held 39.7% after a 0.23-point gain.

The contrast may be sharpest at premium pricing. Fable 5 was priced at roughly $10 per million tokens, about twice the cost of GPT-5.6 Sol. It accounted for 6% of Anthropic tokens and 11.4% of its dollars spent, versus 25% of OpenAI tokens and 23% of spending for GPT-5.6 Sol. Ramp lead economist Ara Kharazian argued that businesses may not pay a premium for the best model when a cheaper system is sufficient.

Editorial analysis

Our Read

The signal is less about immediate displacement than about procurement leverage. Thomson Reuters moving a defined document-review workload from Claude to Thomson-1 suggests that an open foundation can be adapted for a specific business task. That is a bounded example, not proof that every workflow will move. The more immediate constraint is buyer behavior: Ramp still shows Anthropic and OpenAI leading direct spending. Watch whether more companies shift defined workloads to specialized open models—and whether that eventually appears as a direct decline in the leading vendors’ spending shares.

Citation desk / original work

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Finding 01

The signal is less about immediate displacement than about procurement leverage.

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Sources

  1. fortune.comChinese open-source AI is starting to win over U.S. businesses | Fortune