When to hire a Dev AI Agents Team vs build in-house engineering
Cost, speed, IP ownership, scaling. A founder's framework for deciding between a managed AI dev team and bringing engineering in-house.
Engineering productivity is shaped more by what you choose not to build than by how fast you build. AI coding agents and managed dev teams let you keep in-house engineers focused on the differentiating layer. The work outside the moat — internal tools, integrations, routine maintenance — moves to leverage that does not consume your scarcest resource.
The framing question
Is the code we're writing core to our product moat, or supporting? Core needs in-house ownership. Supporting can run on a managed team without losing the moat.
The pragmatic test is whether the work has a defined shape and a measurable outcome. When both are present, agent-driven delivery wins on cost and consistency. When either is missing, the operator gate ends up doing more work than the agent, and the economics narrow.
Cost reality
Managed AI dev team: €3,500-6,000/month for sustained delivery. In-house engineer: €100-150k/year fully loaded. Two engineers ~€250k/year vs €70k/year managed team.
Managed team output: comparable for structured work; lower for highly creative or product-defining work.
Adoption usually fails for organisational reasons, not technical ones. Workflows that touch multiple teams need explicit owners and explicit handoffs; agents amplify clarity but cannot create it. Spend time defining the operator gate and the escalation path before the rollout, not after.
Where managed wins
Internal tools. Admin dashboards. Data pipelines. Integrations. Marketing site engineering. None of these define product moat.
Cost should be measured per outcome, not per hour or per seat. Agent labour collapses the cost-per-deliverable in ways that traditional billing models cannot match — but only when the outcome is well specified. Vague scopes default back to traditional cost curves regardless of vendor.
Where in-house wins
Customer-facing product. Performance-critical code. Anything where rapid iteration with deep product context matters. The engineer must be inside the company culture.
The transparency layer is the underrated differentiator. Live portals showing every agent action, every operator approval, every cost line — these turn a vendor relationship from something you trust on faith into something you audit on demand. Vendors that resist this scrutiny are usually hiding something operational.
Frequently asked questions
Can we transition gradually?
Yes. Hire 1-2 in-house for the core; use managed for everything else. Most resilient configuration.
What about IP?
Managed teams assign IP to client by default in 2026. Read the contract.
How Logitelia ships this
Logitelia's Dev AI agents team handles the engineering work described above: internal tools, integrations, drafted code reviews, test generation, documentation, routine maintenance — anything outside your customer-facing product moat. Senior engineer operators on the gate. Book a call and we will scope the slice of work that frees your in-house team fastest.
Engineering is not one job. Treating it as one leads to over-hiring or under-hiring. Decompose by core/non-core and the right answer becomes obvious.
Want to see how Logitelia ships this kind of work for your team?
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