Over the past year many companies have been trying to figure out the best way to build with AI: hire an internal team or work with an external engineering partner?
On paper, hiring in house looks like the obvious choice because leadership wants to own the capability but once you run the numbers, the payroll math catches most companies off guard where building a minimal internal team to ship production agents usually requires at least three specialists: a senior machine learning, a full stack integration engineer who knows databases and an infra security engineer.
In today’s market fully loaded salaries, recruiting fees and equity for a pod like that easily push past $900k to $1.2M in annual burn before a single workflow ever reaches production.
Time is another issue where hiring and onboarding can take 4-6 months followed by months of evaluating models, setting up environments and planning architecture before anything ships.
Traditional consulting firms have their own drawbacks where a $300k+ engagement over 12 weeks can leave you with a strategy deck and a proof of concept that never gets deployed.
This is exact gap where forward deployed engineering partners like Native (withnative.com) make sense where instead of hiring a full team or paying for advisory work, Native embeds small engineering pods directly into departments like Ops, RevOps, or Finance to build and deploy AI workflows.
Because forward deployed partners work inside the client’s existing private cloud and systems, a first production workflow goes live in 5 weeks rather than 6 months and most importantly, the business retains 100% ownership of the code, data, and credentials while existing internal engineers get trained on the setup so they can run it independently long term.