The AI Feature Comparison Template for Better Build-vs-Buy Decisions

1. Capability Fit

How well does each option meet your functional requirements today and in the foreseeable future? For Buy: evaluate on your actual data with your actual use cases, not on the vendor's demo dataset. Request a proof-of-concept with your data before scoring. For Build: be honest about your team's ML capabilities. Scoring a 5 on capability fit for build when your team has never trained a production model is not evaluation, it is wishful thinking. Score high if the option meets 90%+ of requirements out of the box. Score low if significant customization or workarounds are needed.

2. Data Control

Who owns, processes, and retains the data? For Buy: read the DPA carefully. Determine whether the vendor uses your data to train their models (most foundation model providers do unless you pay for a private instance). Understand where data is stored, how long it is retained, and what happens to your data if you terminate the contract. For Build: you retain full control but accept full responsibility for data security, privacy compliance, and pipeline maintenance. Score high if you retain full ownership and the data handling meets your compliance requirements. Score low if data control gaps create regulatory or competitive risk.

3. Integration Complexity

How much engineering effort is required to integrate the option into your existing product and infrastructure? For Buy: evaluate API design quality, SDK availability, authentication patterns, error handling, and how well it fits your existing architecture. A vendor with a great model but a terrible API will cost you more in integration engineering than you save in ML engineering. For Build: evaluate the end-to-end pipeline: data preparation, model training, model serving infrastructure, monitoring, and CI/CD for model updates. Building an AI feature is not just building a model — it is building and maintaining the entire MLOps stack. Score high if integration is straightforward with your existing systems. Score low if it requires significant architectural changes or new infrastructure.

4. Total Cost of Ownership (3-Year)

What is the fully loaded cost over a 3-year horizon, including all direct and indirect costs? For Buy: API costs at current volume, API costs at projected volume (model what happens at 3x and 10x), annual contract value, integration engineering costs, ongoing maintenance and monitoring costs, and cost of the team time to manage the vendor relationship. For Build: engineering headcount (ML engineers, data engineers, MLOps), infrastructure costs (GPU compute for training, inference serving, storage), data acquisition and labeling costs, ongoing model maintenance and retraining, and opportunity cost of the engineering time. Score high if total 3-year cost is clearly favorable. Score low if costs are uncertain, front-loaded, or scale unpredictably.

5. Vendor Risk

What happens if the vendor changes pricing, degrades quality, gets acquired, or shuts down? For Buy: evaluate the vendor's financial stability, market position, customer concentration, and contractual protections. Determine whether you have price caps, SLA guarantees, and data portability rights. Assess the risk of the vendor deprecating the specific model or API you depend on. For Build: vendor risk is low by definition (you are the vendor), but you accept technology risk — the risk that your in-house model cannot keep pace with commercial alternatives. Score high if risk is well-mitigated through contracts, diversification, or low dependency. Score low if a single vendor decision could significantly impact your product.

6. Time to Value

How quickly can each option deliver a production-ready feature to users? For Buy: the fastest path to an initial version, but customization and optimization take longer than vendors claim. Factor in time for: vendor evaluation, contract negotiation, integration, testing, and the inevitable back-and-forth on model behavior that does not match expectations. For Build: slower to initial version, but once the infrastructure is in place, iteration cycles can be faster because you control the entire stack. Factor in time for: team hiring or upskilling, data preparation, model development, infrastructure setup, and production hardening. Score high if the option delivers production-ready value within your required timeline. Score low if the timeline exceeds your market window or stakeholder patience.