$1 billion from AWS and $750 million from Google Cloud are accelerating “forward deployed engineer” programs that place vendor engineers inside enterprise teams to speed AI and cloud rollouts.
Those engineers work for the cloud providers, not the customer, and are incentivized to recommend architectures, managed services and workflows that deepen use of a single platform.
The author warns that enterprises often skip independent evaluation of multicloud, open-source or rival options, leaving production AI systems optimized for one vendor rather than business needs.
Two to three years later, that dependence can turn costly: some companies end up with cloud bills 15 to 20 times higher than necessary because migration off proprietary services is too expensive.
The recommended safeguards are independent architecture oversight, a documented exit strategy and continuous cost benchmarking before accepting “free” embedded engineering help.
Is 'free' engineering from cloud giants becoming the costliest trap in enterprise AI adoption?
Are new 'neoclouds' and open-source the keys to escaping big tech's expensive walled gardens?
Forward Deployed Engineering in 2026: Accelerating Enterprise AI Adoption and the New Risks of Vendor Lock-In
Overview
In 2026, major cloud providers are making a strategic shift in enterprise AI adoption by introducing Forward Deployed Engineering (FDE) programs. These programs are quickly becoming standard for vendors serving large customers, as the integration of advanced, agentic AI solutions now depends on close collaboration and co-innovation with clients. As AI becomes more deeply embedded in core business operations, the need for specialized, on-the-ground engineering support is growing. AWS has embraced this model, focusing on achieving specific business outcomes, highlighting how FDEs are transforming both the delivery and impact of enterprise AI solutions.