Amazon Web Services announced $1 billion in June to fund a dedicated forward-deployed engineer organization, while Microsoft followed in July with $2.5 billion for what it calls a "Frontier Company," according to a report published October 3, 2026 by The Register. The spending spree centers on a consulting model that embeds vendor engineers inside customer organizations to build AI applications — a practice multiple companies say they've used for years but only recently branded with uniform terminology. The report finds the approach traces back to Palantir, where the term was coined in 2007, though analysts warn it carries risks of vendor lock-in and technical debt.
The forward-deployed engineer model sends top consultants to clients without tying them to a specific project scope or charging hourly fees for their services. Instead, these engineers receive a mission to build something valuable using new technology and authority to make it happen quickly. Cloud monitoring vendor DoIT has used the practice since 2011 but only adopted the "forward-deployed engineer" name in the final quarter of 2025, when it renamed its Customer Reliability Engineering practice. Services companies broadly embraced the term as they began building AI proofs of concept for customers. AWS began a similar approach in 2017 with its ML Solutions Lab, where the cloud provider would loan data scientists to customers for proof-of-concept work, sometimes calling the arrangement a "secondment," "resident architect," or "resident scientist."
Palantir's chief technology officer Shyam Sankar claims he coined the term after a 2007 conversation in which CEO Alex Karp compared French restaurants to engineering organizations and asked Sankar "to build that, but for engineering." Sankar wrote on his personal Substack that forward-deployed engineers "embed alongside our customers and work to ensure our software solves their problem and not some proxy for their problem." He added they're "crazy enough to get on a last-minute plane to Iraq, they're smart enough to ship quality, same-day code." Ryan Sheehan, a senior vice president at $16 billion global solutions integrator SHI, told The Register at VMware's September Explore conference that "we have been doing FDE for a long time," noting the term makes him laugh because the underlying practice isn't fundamentally new.
Both Gartner and Forrester acknowledge the model's value but caution that it can create vendor dependency and technical debt if organizations don't train their internal teams alongside the embedded consultants. Gartner warns that internal teams "may become operators of a black box rather than architects, making it difficult to innovate without the vendor," and notes the approach relies heavily on manually designed structures and hard-coded domain knowledge that could become obsolete as large language models advance. Forrester's June best practice report states that without appropriate guardrails on technology architecture and stack choices, "teams can fall into a bespoke trap, where highly tailored solutions deepen vendor dependence and make future change costly." The model differs from conventional consulting because forward-deployed engineers often have full-stack access to the customer's environment and the vendor's codebase, allowing them to build and iterate in real time without a predetermined path.
Omdia chief analyst Jay McBain predicts all of the forward-deployed engineer investment "will fall back into the channel by 2031 when this matures," noting that vendors are simultaneously investing heavily in partner enablement because 82 percent of partners report they aren't ready to quickly grow their AI services. McBain explains that none of the companies' investors want to dilute a low-value services business into overall revenue, making the current push a temporary measure. The report concludes that in a few years the industry will likely look back at how Palantir rebranded an old form of tech services under a new name and the rest of the sector followed the same recipe. The timing may prove particularly ironic given that organizations already struggle to distinguish genuine innovation from repackaged convention, and the pressure to demonstrate AI competence can override the discipline to build transferable internal capability.