When to Build vs Buy Your ML Infrastructure

The build vs. buy decision in ML infrastructure is more consequential than in most software domains. ML infrastructure is expensive to build, expensive to operate, and expensive to migrate away from. A wrong build decision locks up six months of senior engineering time on infrastructure that a managed service could have provided in a week. A wrong buy decision creates vendor lock-in that constrains your architecture for years.

Most AI startups make this decision component by component, under time pressure, without a coherent framework. The result is a patchwork of homegrown and managed infrastructure that nobody designed holistically - some components built because “we thought we’d need full control”, most of them now operational overhead that the team resents.

The Framework

The build vs. buy decision for any ML infrastructure component comes down to four questions:

1. Is this a source of competitive differentiation?

If your competitive advantage comes from unique ML infrastructure capability, building may be justified. If the infrastructure is commodity - experiment tracking, pipeline orchestration, basic feature serving - buying almost always wins. Most AI startups are not in the ML infrastructure business. Their competitive advantage is in the models, the data, and the product. Infrastructure that doesn’t contribute to that differentiation is overhead.

2. What is the total cost of ownership of building?

Build decisions are systematically underestimated. The estimate includes: initial build time, testing, documentation, onboarding the team onto the new system, ongoing maintenance, debugging production incidents, and upgrading as requirements change. A feature store built in-house doesn’t have a one-time cost - it has a permanent cost that grows as the system becomes more critical.

3. Does a managed service meet your actual requirements?

Managed services have limitations. They may not support your specific scale, your privacy requirements, your latency targets, or your integration requirements. The question is whether a real managed service meets your real requirements - not whether a theoretical custom-built system would meet hypothetical future requirements.

4. What is the migration cost if you choose wrong?

Some components are easy to migrate (experiment tracking). Others are expensive (feature stores that production models depend on, model registries with years of lineage). Weight the buy vs. build decision partly by the cost of reversing the decision.

The Decision Matrix

Experiment Tracking

Buy. Weights & Biases, MLflow (managed via Databricks or self-hosted), and Comet are mature managed options. The differentiated capability in experiment tracking is the UI, the comparison features, and the integrations - not the storage backend. Building a bespoke experiment tracking system is almost never justified.

The one exception: highly regulated environments where data residency requirements prevent sending experiment metadata to a managed service. In this case, self-hosted MLflow behind your network boundary is the right answer - not a custom-built system.

Feature Store

Situation-dependent. Feature stores are infrastructure where the build vs. buy decision genuinely depends on your requirements. Managed feature stores (Feast, Tecton, Hopsworks, Vertex AI Feature Store, SageMaker Feature Store) range from open-source self-hosted to fully managed cloud services. The buy option is almost always correct for early-stage companies.

The case for building a custom feature store is narrow: you have specific requirements that no managed option meets (unusual latency targets, very large-scale online serving with cost constraints that managed options can’t satisfy, or specific data residency requirements). Most early-stage companies that build custom feature stores do so because they underestimated the maturity of managed options, not because their requirements genuinely require custom infrastructure.

Model Serving

Situation-dependent. For simple serving requirements, FastAPI with a containerised model is a legitimate starting point. For production serving at scale - high throughput, batching, multi-model serving, GPU utilisation - managed options (Triton Inference Server, BentoML, Seldon, Ray Serve, SageMaker endpoints) provide capabilities that would take months to build correctly.

The mistake we see repeatedly: companies build a FastAPI serving layer that works at small scale, then spend three months optimising it as throughput requirements grow, ultimately building something less capable than Triton out of the box. Buy the serving infrastructure; build the models.

Data Pipeline

Buy the orchestration, build the transforms. Pipeline orchestration (scheduling, dependency management, failure handling, retry logic) is solved by Airflow, Prefect, Dagster, and similar tools. Build your data transformation logic on top of these tools, not around them. Custom pipeline orchestration is never justified at startup scale.

ML Monitoring

Buy. Arize AI, WhyLabs, Evidently AI, and cloud-native options (Vertex AI Model Monitoring, SageMaker Model Monitor) provide drift detection, data quality monitoring, and model performance tracking that would take months to build well. The one thing monitoring tools can’t provide is ground truth feedback from your production system - building the ground truth collection pipeline that feeds into a managed monitoring tool is the right division of responsibility.

Model Training Infrastructure

Buy the compute, build the training code. GPU compute (AWS, GCP, Modal, Lambda Labs, CoreWeave) is infrastructure where the managed options are clearly superior to anything you’d build. Your training code should be portable and should not depend on custom infrastructure. Vendor lock-in risk in training compute is lower than in other components because training jobs are more easily migrated than serving infrastructure.

The Common Mistake

The most common ML infrastructure build vs. buy mistake we see is building commodity infrastructure at Series A because “we might need full control later”, then spending Series B engineering budget maintaining infrastructure that managed services have commoditised.

The right time to build custom ML infrastructure is when you have evidence - from production scale, from specific capability gaps in managed services, from measured unit economics - that a custom-built solution is justified. Not before.

Start with managed services. Migrate to custom infrastructure when you have a specific reason, not a general preference for control.

Talk to us about your ML infrastructure strategy before you commit to building something you should be buying.