Meta Builds Internal AI Gateway to Control Soaring AI Costs

Meta Platforms is deploying new controls to rein in rapidly escalating artificial intelligence costs, including a centralized tracking platform and spending limits tied to employee usage.

The company shared plans with approximately 6,000 employees this week detailing how it will monitor and manage AI consumption across teams, according to a memo reviewed by The Information. The initiative comes as Meta projects it will spend billions of dollars on internal AI use alone by 2026.

Why it matters

Meta's move to formalize AI spending oversight reflects a broader challenge facing technology companies: the gap between AI adoption rates and cost management infrastructure. As enterprises race to integrate AI tools, many are discovering that unmonitored usage can quickly balloon into budget-threatening expenses. Meta's approach—building internal tracking systems and encouraging proprietary tools over third-party services—may signal how other large organizations will need to balance innovation with fiscal discipline.

AI Gateway to centralize tracking

Meta has developed an internal platform called AI Gateway that will serve as a centralized dashboard for tracking AI activity and spending across the organization. The system will provide teams with real-time visibility into their AI resource consumption, something the memo acknowledged is currently limited.

The platform will include automated alerts designed to flag unusual spending spikes, giving managers early warning when usage patterns deviate from expectations. By 2027, Meta plans to implement a more structured framework that includes formal budgets, allocation decisions, and supporting tools for managing AI tokens—the units that measure AI model usage.

Shift toward internal tools

Alongside the tracking infrastructure, Meta is encouraging employees to reduce reliance on third-party AI coding tools and instead use MetaCode, the company's in-house coding assistant previously known as Devmate. This strategic shift aims to keep more AI spending within Meta's own ecosystem while potentially reducing per-token costs associated with external services.

The memo indicated that AI adoption inside Meta has accelerated rapidly, creating the need for more sophisticated spending controls and usage limits. The company expects to roll out these new tools and controls to employees in the coming weeks.

Industry-wide cost pressures

Meta is not alone in grappling with AI expense management. Uber Technologies reportedly exhausted its entire planned 2026 AI coding budget within just the first four months of this year due to unexpectedly high token consumption, illustrating how quickly AI costs can outpace projections.

These details were first reported by The Information based on an internal Meta memo distributed earlier this week.