A CIO at a mid-sized asset manager is evaluating two AI platform options: (A) a best-in-class third-party vendor platform offering pre-built models and API-based integration, and (B) an internally built platform developed by the firm's data science team. The firm currently manages $25 billion AUM and expects to double AUM within three years. Which scalability evaluation framework best guides this build-vs-buy decision?

The build-vs-buy decision for AI platforms in asset management is one of the highest-stakes technology strategy choices a CIO makes. Neither option is universally superior — the right answer depends on a structured evaluation across multiple scalability dimensions specific to the firm's context, growth trajectory, and competitive strategy.

Computational elasticity refers to the platform's ability to scale inference and training workloads as AUM and data volumes grow. Vendor cloud platforms typically offer elastic infrastructure that scales on demand; internally built systems may require significant capital expenditure in compute infrastructure to match this. For a firm doubling AUM in three years, this dimension often favors vendor platforms unless the firm already has significant ML infrastructure.

Data pipeline throughput assesses whether the platform can ingest, process, and serve increasing volumes of market data, alternative data, and client data as the business grows. Internally built pipelines can be optimized for the firm's specific data assets, but require dedicated data engineering resources to maintain at scale.

Model governance scalability is frequently underestimated: as the number of models in production grows, can the platform support versioning, lineage tracking, monitoring, and documentation at institutional scale? Vendor platforms increasingly offer MLOps tooling; internal builds must replicate this. For regulated firms, model governance infrastructure is not optional.

Organizational talent capacity is the human factor: can the firm attract and retain the data scientists, ML engineers, and quantitative researchers needed to sustain an internally built platform competitive with a well-resourced vendor? This often shifts the calculus toward hybrid or vendor-led approaches for mid-sized firms without deep technology talent pipelines.

Options A and B represent common cognitive biases — the assumption that vendor or internal is categorically superior. Option D introduces a cost-minimization heuristic that ignores architectural lock-in, long-term TCO divergence, and the strategic value of proprietary capability. CFIA candidates should apply a structured, multi-dimensional framework rather than relying on cost or vendor-dependency heuristics alone.