Build vs Buy: When to Stop Building Your Own MLOps Platform

Every engineering organization with more than ten ML engineers has, at some point, decided to build its own MLOps platform. About 30% finish. About 5% are still using what they built three years later. The other 95% quietly migrate to a vendor and never write the postmortem.

Here's the cost model that should have been built before the decision.

The honest scope of 'just orchestration'

Teams that propose to build start with 'we just need pipeline orchestration on top of Airflow or Argo'. Six months later the scope has expanded to model registry, artifact storage, drift monitoring, experiment tracking, deployment automation, RBAC, audit logging, and an internal annotation tool because the opensource ones don't integrate. Every piece is reasonable. The aggregate is a platform team.

The 18-month TCO

A realistic in-house build for a 50-engineer ML org. Engineering: 4 senior engineers × 18 months × $180K loaded cost = $1.08M. Infrastructure: $80K/year hosting + compute. Tools and licenses (still need orchestrator, monitoring, etc.): $60K/year. Maintenance overhead in year 2+: 2 engineers permanently dedicated. Total year 1 + 2 + 3: roughly $2.4M, with the ongoing line-item that doesn't go away.

A managed MLOps subscription serving the same workload: $60K-$180K/year depending on tier. Total over 3 years: $180K-$540K. Difference: roughly 5x in cash, plus the 2 dedicated engineers permanently doing platform maintenance rather than building product.

When building actually makes sense

Three legitimate cases. First, regulated workflows that no vendor supports — air-gapped defense applications, certain pharma manufacturing, classified government. Second, scale that's outside the vendor envelope — Meta, Google, and Tesla have unique requirements that no off-the-shelf platform addresses. Third, when the platform is the product — Databricks builds platforms because their company is a platform company. If you make automobiles or run a hospital, you're not in this category.

The hidden cost: opportunity cost

The four engineers building your platform are not building your model. The two engineers maintaining the platform are not improving model accuracy. Most ML organizations have more impactful work to do than building infrastructure that 50 vendors already provide. The opportunity cost is the engineers' hours allocated to platform rather than product, compounded over years.

Migration risk is the most cited objection — and it's mostly false

Teams hesitate to buy because they fear vendor lock-in. In practice, modern MLOps platforms are mostly built on standards — MLflow model format, OCI containers for deployment, OpenAPI for inference. Migration between vendors is a 2-week project at most. The actual lock-in is in your team's habits, not the data formats.

The decision framework

Three questions. Does any vendor's offering cover at least 80% of your workflow today? If yes, buy — the 20% gap is cheaper to bridge with integrations than to recreate everything. Are you a platform company? If no, buy. Will the in-house build pay back its engineering investment in less than 24 months? It almost never does. Buy.

Intellabel's MLOps and Enterprise tiers exist for exactly this case — teams that need pipelines, deployments, monitoring, and audit trail without three engineers permanently dedicated to platform work. If you're more than six months into a build that hasn't shipped, the conversation worth having is what migration looks like, not what month 12 of building looks like.

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