Google Limits Meta's Access to Gemini AI Due to Capacity Constraints

By: Harsh Vardhan

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Updated on: 29-Jun-2026 12:00 PM

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​Google has restricted Meta's access to its Gemini artificial intelligence models due to limited computing capacity. Meta, the parent company of Facebook, requested more Gemini AI resources than Google could provide. This shortage has delayed several of Meta's internal AI projects, according to a Financial Times report.

Key Highlights

• Google limited Meta's access to Gemini AI due to infrastructure constraints.
• Meta delayed several internal AI projects and tightened employee use of AI tokens.
• Gemini supports Meta's safety operations, customer service, and productivity tools.
• Meta is shifting some workloads to its Muse Spark model to reduce reliance on external AI.
• Google and other AI firms face industry-wide challenges in securing enough computing capacity.

Capacity Shortage Impacts Meta's AI Projects

Google informed Meta around March that it could not meet all of Meta's Gemini AI capacity requests. The infrastructure limitations have forced Meta to adjust its internal AI operations. The restrictions remain in place, affecting Meta more than other enterprise customers because of its high demand for AI computing resources.

Meta has been one of Google's largest Gemini customers. The company's scale of usage made it especially vulnerable to the current capacity crunch. Other Google enterprise clients have also faced restrictions, but Meta's needs were significantly higher.

The shortage has disrupted and delayed several of Meta's AI initiatives. As a result, Meta has tightened internal controls on AI usage. The company has encouraged employees to use AI tokens—units measuring AI model usage—more efficiently. This move aims to reduce AI costs while operating under the new limits.

Gemini's Role and Meta's Response

Gemini has become an important tool within Meta. The company uses Google's AI models for automating safety operations, such as detecting scams and removing harmful content. Gemini also supports internal customer service tools, advertising assistants, coding workflows, and productivity tasks. Meta uses Gemini alongside other models, including Anthropic's Claude.

Meta initially chose Gemini because it outperformed the company's own Llama open-source models in several enterprise applications. However, Meta has recently started shifting some workloads to its new Muse Spark model. Insiders believe Muse Spark is now competitive enough to reduce Meta's reliance on external AI providers.

Industry-Wide Infrastructure Challenges

The restrictions on Meta highlight a broader infrastructure challenge across the AI industry. Companies are spending tens of billions of dollars on chips, data centers, and power to meet growing demand. However, many still struggle to secure enough computing capacity for AI models and services.

Demand for inference computing—the processing power needed each time an AI model answers a query—has risen sharply. Businesses are deploying chatbots, coding assistants, and AI agents at scale, increasing pressure on infrastructure.

To address these needs, Google has expanded its infrastructure. The company reportedly signed a deal worth about $920 million per month to lease additional computing capacity from SpaceX, owned by Elon Musk. AI startup Anthropic has also entered a similar arrangement.

Google has acknowledged ongoing capacity constraints. During the first-quarter earnings call in April, CEO Sundar Pichai said Google Cloud revenue surpassed $20 billion for the first time. He noted that revenue could have been higher if Google had enough infrastructure to meet demand. "Obviously, we are compute-constrained in the near term," Pichai said. "Our Cloud revenue would have been higher if we were able to meet the demand."