Build, buy, or embed?
Three ways to source AI capability â each right in different situations. Here’s an honest framework for choosing, including the cases where embedding isn’t the answer.
Each is right — somewhere.
Build in-house
Best when AI is core to your product and you can hire and retain senior AI engineers.
Slow and expensive to staff; hard to hire; you carry the reliability burden alone.
Buy off-the-shelf
Best when a commodity workflow fits a mature SaaS product almost exactly.
Generic by design; your edge cases and data model rarely fit; limited control.
Embed a partner
Best when you need a specific workflow shipped fast, built into your stack, and kept reliable.
Not the move if the need is trivial, or if you must own deep in-house capability long term.
The same five questions, three ways.
We’ll say it plainly: if AI is core to your product and you can hire senior engineers, build in-house. If a commodity tool fits your need exactly, buy it. Embed a partner like Gigabit when you need a specific workflow shipped fast and kept reliable — often as the bridge to building in-house.
Build vs buy vs embed, answered
Should we build an in-house AI team or embed a partner?
Build in-house when AI is core to your product, the work is continuous, and you can actually hire and retain senior AI engineers — which is hard and slow right now. Embed a forward-deployed partner like Gigabit when you need a specific workflow shipped fast and kept reliable, without carrying the full hiring and reliability burden yourself. Many teams embed first to ship, then build in-house capability over time.
When is buying an off-the-shelf tool the right call?
When your need is a commodity workflow that a mature SaaS product already does almost exactly, and you don’t need deep customization or control. The moment your edge cases, data model, or integrations matter, off-the-shelf starts to strain.
Can Gigabit help us build in-house capability, not just deliver?
Yes. Our Embedded AI Teams work alongside your engineers and level them up as they ship — so embedding can be a bridge to in-house capability, not a dependency.
What's the fastest path to a working AI system?
Embedding. A forward-deployed partner can have a production workflow live in weeks, versus months to hire a team or the generic constraints of an off-the-shelf tool.
Not sure which path fits?
Tell us the workflow and the constraints. We’ll give you an honest read â even when the answer is build or buy.