Independent Study Finds Starburst Platform Delivers 45% Lower TCO and 414% ROI for Data and AI Workloads

New updates have been reported about Starburst.

Starburst has released an independent Economic Validation Study from Enterprise Strategy Group (ESG), now part of Omdia, indicating that its Data & AI Platform can cut total cost of ownership by 45% over three years versus alternative data platforms and point tools. Based on interviews with existing customers and ESG’s economic modeling, the analysis concludes that organizations using Starburst reduce infrastructure complexity, consolidate tooling, and accelerate AI and analytics initiatives, with one customer reporting the retirement of 15 applications and five cloud services after standardizing on Starburst. ESG modeled a data‑driven SaaS company with $210 million in annual revenue and found that Starburst’s platform delivered a three‑year return on investment of 414%, driven by cost savings, productivity gains, reduced downtime, and faster time to insights.

Key findings include a 67% reduction in development and integration costs through elimination of custom connectors, a 65% drop in data processing expenses such as ETL and egress, an 85% reduction in storage costs enabled by query‑in‑place and decoupled compute and storage, and a 39% cut in cloud and infrastructure spending through better price‑performance and autoscaling. ESG also quantified up to $1.7 million in avoided downtime risk and potential incremental revenue of $17.3 million to $185 million over three years from faster innovation and AI adoption. The report credits Starburst’s federated, open data architecture—built to access data where it resides across clouds, on‑premises systems, and data lakes—with enabling these economics while improving governance and security, and notes that customers are achieving up to 10x query performance improvements and scaling AI workloads without proportional increases in staff or infrastructure. CEO Justin Borgman framed the findings as validation that unifying access to distributed data and minimizing data movement can both control costs and speed AI initiatives for large enterprises.