When our cloud costs started outpacing growth, I knew we had to make a decisive call on “build vs buy.” Buying a FinOps platform would have been faster on paper, but it wouldn’t internalize our operational nuance. Building an agentic AI layer on top of our cost, telemetry, and product usage data promised not just dashboards—but compounding leverage. Less than a year later, our homegrown approach outperformed off‑the‑shelf alternatives on speed, precision, and organizational adoption.
The aspiration was clear from the outset: See how Amplitude scaled FinOps with AI agents—cutting manual work, accelerating insights, and turning a one-person function into a cost optimization engine. We set that as a bar for both outcomes and operating cadence, then translated it into a roadmap grounded in first principles.
Our build vs buy analysis hinged on three factors. First, cloud cost optimization is only as good as the context it carries; we needed deep hooks into our pricing, feature flags, and deployment frequency to reason about unit economics in real time. Second, we required agentic AI workflows that could detect anomalies, recommend actions, and close the loop—not just visualize waste. Third, governance mattered: privacy‑by‑design, data governance controls, and transparent decision logs were non‑negotiable under our AI Strategy and product management leadership standards.
We architected a retrieval‑first pipeline to blend billing exports, usage telemetry, and observability signals with product and GTM metadata. Agent workflows ran on top: one agent built driver trees that explained spend shifts by service, customer cohort, and environment; another specialized in anomaly detection with confidence scoring; a third agent proposed commitment strategy, rightsizing, and schedule adjustments. Each recommendation linked back to source data for auditability.
From a delivery standpoint, we treated the system like a product, not a tool. A product trio (PM, engineering, and FinOps) ran continuous discovery interviews with stakeholders, instrumented eval‑driven development for agent prompts, and shipped improvements via CI/CD weekly. We optimized prompt engineering for decision clarity over verbosity and codified acceptance criteria: time‑to‑insight, actionability, and measurable savings per recommendation.
The impact was immediate and then compounding. Manual effort on month‑end analysis shrank as agents pre‑triaged drift and surfaced root causes with suggested remediations. Insights arrived continuously, not as end‑of‑month surprises, which meant engineering could fold changes into regular sprints. What started as a one‑person FinOps function evolved into a cost optimization engine embedded across teams—product, SRE, and finance—all speaking a shared language of drivers, tradeoffs, and outcomes.
Along the way, we learned where building truly beats buying. If your architecture, pricing model, and growth loops are unique—and they usually are in consumption SaaS—agentic AI amplifies institutional knowledge in a way generic platforms can’t. Conversely, if you lack clean tagging, clear ownership, or basic observability, investing there first will raise ROI on any approach, built or bought.
My advice if you’re at this crossroads: define success in terms of decisions changed, not reports shipped. Start with a thin slice—anomaly detection plus one high‑leverage remediation path—then iterate. Keep humans in the loop for executive sign‑off until your confidence intervals and post‑action telemetry prove reliability. With the right guardrails and focus, in‑house AI FinOps can move faster than the market and pay for itself well within a year.
Inspired by this post on Amplitude – Perspectives.
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