Why does outcome pricing require FDEs?

Because billing per result means the vendor's revenue depends on the result showing up, and someone has to make it show up on the customer's real data and systems. Natalie Meurer's framing from inside Sierra is that the question of how you guarantee the outcome has one answer, and it is forward deployed engineering. Seat pricing never needed a guarantor. Outcome pricing cannot function without one.

What is the deployment-to-product loop?

The FDE builds the fast, overfit version for one customer. A product team with the explicit job of generalizing takes what was built and, when the same pattern shows up at three customers, turns it into a feature or a configuration. At Palantir this is how ingestion, visualization, and the app builder became Foundry components. Without an owner for the second half, the loop starves and every engagement is a services contract.

What is the buyer's lock-in objection?

Andrew Ng's version: letting a vendor's FDEs wire your processes to their stack significantly reduces optionality, and your own AI engineer on staff is the better bet. Plank adds that dependence deepens while the FDE helps, capability does not transfer without documentation, and deployment is only about 20 percent of an AI system's lifetime cost. All three are real and all three are solvable in the contract.

What should a buyer demand in the contract?

Three things. The observation record of what the agents did in your org belongs to you and ports with you. Every configuration the FDE builds is readable and exportable, not buried in vendor code. Enablement is a deliverable with a test: your own engineer can change the deployment without the vendor by day 60. A vendor who will not agree has told you what their moat is.

When should a vendor stop sending engineers?

When the product's configuration surface has absorbed enough that the customer, or their own AI engineer, can deploy from the product. Sierra's no-code studio and Airbus distributing the FDE function across its own customer engineers are both versions of the endpoint. A company in year four with the same FDE ratio as year one has not been running a forward deployed model.

Do Forward Deployed Engineers Scale?

Do forward deployed engineers scale, or is it just consulting?

THE SHORT ANSWER

Only if two things happen, and most companies adopting the title in 2026 have set up neither. What the FDE builds for one customer has to be generalized so later customers need less of an engineer, and the pricing has to capture the outcome the engineer guaranteed. Palantir ran the model for a decade with more FDEs than product engineers until 2016 and, per Plank's figure, roughly $1.5 million in revenue per employee with almost no traditional sales force, because the FDEs were the sales motion and what they built became Foundry. Sacra flagged implementation intensity as Sierra's biggest scaling risk, and Sierra's answer was to move workflow building into a no-code studio so each deployment needs less engineering. The buyer's objection is real: Andrew Ng warns that a vendor's FDEs reduce optionality, and Gartner expects enterprises to abandon FDE-heavy engagements on cost. The fix is contractual: the observation record and every configuration belong to the customer and port with them. The signal to watch is FDE hours per deployment for the same customer class. Falling means the loop is closing. Flat means you are running a services firm and calling it product.

The honest version of the answer is that forward deployment does not scale by itself. It scales when it is attached to a product that absorbs what the engineers learn, and it turns into consulting when it is not.

Palantir's decade of running it proves both halves. The cost was real: more embedded engineers than product engineers for years. The return was real too, because the loop closed and each deployment made the next one cheaper. Companies copying the model in 2026 mostly have the cost side set up and the loop side missing, which is why Gartner expects a wave of enterprises walking away on price.

The handbook's economics chapter above has the full argument, including the number I would watch: FDE hours per deployment, quarter over quarter, for the same class of customer. That single line tells you whether you are running the model or paying for it.

Last reviewed 2026-09-17 · 1 min read

Do forward deployed engineers scale, or is it just consulting?

SOURCES

THE LONG VERSION

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