PP&A designed the study in three cohorts and completed it in six weeks in the fall of 2025. The first cohort covered six in-depth interviews with founders and infrastructure leads at AI startups running inference workloads. Those conversations traced how startups evaluate cloud infrastructure during proofs of concept, where they trade control for operational simplicity, and what triggers a move from managed APIs to self-hosted models.

The second and third cohorts added eight interviews, split between former users of the client's platform now working at other companies and practitioners on commercial and open-source experimentation platforms. PP&A built the interview guide around a seven-step experiment lifecycle: launching experiments, managing configurations, monitoring jobs, managing artifacts, reproducing experiments, comparing results and managing infrastructure. For each step the team captured what works well, what does not, and how the client's platform compares.

In parallel, PP&A ran desk research to size the ML experimentation platform market, map four provider categories (commercial software, open-source tools, cloud-native services and orchestration-first platforms), document pricing and consolidation trends, and profile the three leading competitors. The team delivered two reports: a first-cohort readout on inference infrastructure and a final report covering the market, the competitor profiles, the user journey analysis, the platform's positioning and prioritized recommendations.

The first cohort delivered a clear warning. AI startups rejected Kubernetes-centric infrastructure as too complex and chose specialized providers that trade configurability for radical simplicity. Simplicity decoded into four requirements: a code-native Python interface instead of configuration files and cluster tooling, consumption-based billing with true scale-to-zero, inference performance that works out of the box, and a seamless path from training checkpoint to production endpoint. Startups adopted the client's service only once they reached significant scale, and that switching moment was drifting later as the specialized providers improved. The recommended response was a Python software development kit that hides cluster complexity while preserving the cloud's ecosystem advantages.

The final report answered the platform question directly. External experimentation platforms are tracking-only: they log experiments but cannot launch jobs or manage compute, so teams spin up accelerators by hand, coordinate through chat channels and track configurations in spreadsheets. The client's platform is the only solution that combines launching, tracking and infrastructure management. It scales from a handful of workers to thousands with a one-line change and preserves every configuration automatically. Former users confirmed that their experiments elsewhere were far less extensive. The market stood in the low single-digit billions of dollars in 2024, with growth above 35 percent a year.

PP&A recommended an open-source launch after closing three gaps: dry-run validation to remove the 30-to-60-minute cycle that simple errors cost researchers, proactive alerting so that nobody has to babysit running jobs, and integration with distributed code repositories. A second wave followed: reusable configuration templates, deployment tracking, storage cost governance, post-experiment analytics and intelligent compute recommendations. Together these gave the client a minimum viable product definition and a position to take share before cloud providers build competing solutions.

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