Build it, buy it, or have it built?
Three ways to get an AI agent into production — 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.
What each path costs to hold.
Buying is priced by the vendor, per seat or by usage, so it is left out. The other two can be put side by side.
The Gigabit column is arithmetic on our published prices. The in-house column is one client’s hiring cost, from the HR-tech case study, not a market average.
The default is stacked against DIY
Start from the base rates. 95% of enterprise generative-AI pilots deliver no measurable P&L impact (MIT, 2025), and Gartner expects more than 40% of agentic-AI projects to be canceled by the end of 2027 on cost and unclear value (Gartner, 2025). Adoption is near-universal but shallow — 91% of mid-market firms use generative AI, yet only 25% have it integrated into core operations (RSM, 2025). The failure mode is almost never the model; it's the engineering, evals, and operations around it — the part a from-scratch team discovers the expensive way.
Decide it in one afternoon
Four questions settle most cases: Is this agent your core product? Do you have (or can you hire) senior AI engineers? How much time pressure are you under? How high is the reliability bar? Core-product-plus-strong-bench points to build; common-workflow-plus-speed points to buy; differentiated-but-no-bench points to embed. If you want the answer scored against your specifics, start with the AI Readiness Assessment, or de-risk with a fixed-price two-week Sprint that returns a scoped plan before you commit.
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 partner-built, answered
Should I build or buy an AI agent?
Buy or partner unless the agent is your core product and you can hire senior AI engineers. MIT's 2025 study found buying from or partnering with specialized vendors succeeded about 67% of the time versus internal builds succeeding roughly a third as often — the operational tail (evals, drift, integration) is where DIY usually stalls.
Is it cheaper to build AI agents in-house or have a partner build them?
On published numbers, usually a partner. A Gigabit agent is built for a flat fee from $8,000 and run for $3,000–$20,000 a month, so three years of operations cost $108,000–$720,000. One senior AI engineer cost one of our clients $195K–$220K a year fully loaded, or $585K–$660K over the same three years. In-house wins when agents are your core product and the work never stops.
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 — even when the answer is build or buy.