# Should I hire an AI agency or an in-house ML engineer?

> For a first AI project, an outside team is usually the better bet: you get a shipped system without committing to a full-time salary while you're still learning what you need. Hiring an in-house ML engineer makes sense once AI is core to your product, you have a proven system to own and extend, and there's enough steady work to keep a specialist busy. The two aren't rivals — the pattern that works is agency first to ship, in-house next to own.

_Source: https://plenaura.com/answers/ai-agency-vs-hiring-ml-engineer · Last updated: 2026-06-03 · Plenaura_

The honest version of this question separates two different situations. If you have not yet shipped an AI system, hiring a full-time ML engineer means paying full-time compensation while you're still discovering what you need — and one engineer can't ship a product alone. An outside team gives you a working system on an agreed scope and timeline, and a reference point for what the ongoing work actually looks like before you commit a salary line to it.

The in-house case is real and worth stating plainly, because a good partner will tell you when it applies. Hire in-house when AI is central to your product rather than a supporting capability, when the work is continuous (models to retrain, data pipelines to tend, features to extend every week) rather than a defined project, and when you already have a proven system that a permanent owner can deepen. At that point the daily proximity and accumulated context of an employee beats re-engaging an outside team for every change.

Weigh the true cost of each path, not the headline. Per Built In's 2026 US salary data, the average machine learning engineer earns about $162,080 in base salary and $212,022 in total compensation — and in high-cost markets the base alone runs higher. Add recruiting, tooling, and management overhead, and remember one specialist still needs frontend, infrastructure, and product design around them to ship anything a user touches. An agency, by contrast, is priced per project against a defined scope, so you're comparing a fixed salaried team-of-one-plus-overhead against a scoped deliverable.

The pattern that avoids regret is sequential, not either/or: ship a focused first system with an outside team, then hire in-house once there's a proven system worth owning full-time. This is also why code ownership matters when you pick an agency — if you own 100% of the code, models, and documentation with no lock-in, your eventual in-house hire can pick the system up and extend it without ever calling the original builder. Choose a partner that hands everything over, precisely so the in-house step later is an easy one.