Breaking: DoiT to acquire Select, expanding AI-driven CloudOps and FinOps automation into data workloads, starting with Snowflake
DoiT, a leading cloud solutions partner based in Santa Clara, has announced the acquisition of Select, a developer of automated optimization tools for the Snowflake data platform.The deal, whose financial terms were not disclosed, will fold select’s technology into DoiT’s flagship Cloud Intelligence suite and relaunch as PerfectScale for Snowflake.
The integration aims to give customers “deep visibility” into Snowflake usage and apply policy-driven automation to reduce waste without compromising performance or data quality.DoiT says the move broadens its automation-first approach, previously used to optimize cloud infrastructure, to data workloads managed on Snowflake.
DoiT’s chief executive,Vadim Solovey,described the acquisition as a natural extension of the company’s FinOps philosophy. He said PerfectScale for Snowflake will unify data-platform optimization with budgeting, cost allocation, anomaly detection and automated workflows, delivering AI-assisted recommendations that align cloud and data telemetry for safer, higher-impact actions.
The announcement comes as DoiT continues its push into AI-driven cloud operations. It is part of a broader strategy that has already seen the company invest $250 million in related technology, including cloud and data-security capabilities.
DoiT is already recognized as a major cloud channel partner for AWS, Google Cloud and Microsoft Azure. It sits on industry rankings, including the CRN Solution Provider 500.
Other highlights of DoiT’s growth plan include prior acquisitions that expanded its automation offerings: CloudWize, a multi-cloud security platform, acquired in October; PerfectScale, a Kubernetes management provider, acquired in February 2025; and livediagrams, a cloud-optimization startup, purchased in january 2025. The Select deal marks a formal extension of DoiT’s FinOps growth into data-platform economics,beginning with Snowflake.
In its release, DoiT noted that data platforms like Snowflake have become a fast-growing but opaque portion of cloud spend. Teams often contend with fluctuating warehouse usage, inefficient queries and opaque billing, areas the company believes its expanded platform can address.
Select’s origin and technology
Table of Contents
- 1. Select’s origin and technology
- 2. Key facts at a glance
- 3. evergreen insights
- 4. What DoiT Brings to the Table
- 5. Why Snowflake Cost‑Optimization Matters
- 6. How Select’s technology Enhances DoiT CIP
- 7. Key Features of the Integrated Snowflake Cost‑Optimization Module
- 8. Tangible Benefits for Enterprises
- 9. Implementation Workflow
- 10. Real‑World Example: Global Retailer’s savings Journey
- 11. Practical Tips to Maximize Snowflake Savings
- 12. Future Roadmap and Industry Impact
Select was founded by data engineers with a mission to cut data-platform waste without adding engineering burden. Its tools continuously detect misconfigurations, optimize resource usage and enforce real-time policies, translating telemetry into actionable business outcomes. The leadership says the team will join DoiT to continue building and expanding PerfectScale for Snowflake, aiming to broaden support to additional data platforms beyond Snowflake.
Current Select customers will not see changes to contracts, pricing, support channels or product functionality as the transition proceeds.
Key facts at a glance
| Item | Details |
|---|---|
| Buyer | DoiT, Santa Clara-based cloud solution provider |
| Target | Select, developer of automatic optimization for Snowflake |
| New product name | PerfectScale for Snowflake (integrated into DoiT Cloud Intelligence) |
| scope | Data-platform optimization, extending CloudOps/FinOps automation to data workloads |
| Financial terms | Not disclosed |
| Recent related moves | CloudWize (Oct), PerfectScale (Feb 2025), LiveDiagrams (Jan 2025) |
External context: Snowflake continues to be a major focus for enterprises seeking cost governance and performance optimization, a trend reinforced by industry analyses on cloud financial management and data-platform spend.
What could the convergence of data-platform optimization and cloud FinOps mean for your organization’s budgeting and governance? Will other data platforms beyond Snowflake be next in line for this approach?
evergreen insights
As data workloads grow, enterprises increasingly demand unified controls that span both infrastructure and data services. Automating optimization across compute and data layers can definitely help reduce waste, improve performance and increase governance. Expect more tooling vendors to seek cross-domain visibility, automations and policy-driven actions that link cloud costs with data usage in real time.
Two reader questions to consider: How dose your team balance cost savings with data performance and quality when adopting automated optimization tools? Which data platforms would benefit moast from a unified cost-and-performance control plane?
Share your thoughts in the comments and tell us which platform you’d like to see next integrated into an automated data-op solution.
For further reading on cloud cost management and data platform optimization, you can explore resources from the Snowflake ecosystem and industry standards bodies on FinOps.
Readers can follow related developments and expert analyses as this integration progresses, with updates on industry platforms and enterprise deployments.
.### DoiT’s Strategic Acquisition of Select
Date: 2026‑01‑08 18:33:57 | Source: archyde.com
Overview of the Deal
- Acquirer: DoiT International, a leading multi‑cloud consultancy and platform provider.
- Target: Select, the Snowflake‑focused cost‑optimization startup founded in 2022.
- Transaction Value: Undisclosed (estimated €35‑45 M based on market sources).
- Purpose: Embed Select’s automated Snowflake cost‑optimization engine into DoiT’s Cloud Intelligence Platform (CIP) to deliver end‑to‑end cost governance for data‑warehouse workloads.
What DoiT Brings to the Table
| Capability | How it Supports Snowflake Optimization |
|---|---|
| Multi‑cloud visibility | Consolidates usage data from AWS, azure, GCP, and Snowflake into a single dashboard. |
| AI‑driven proposal engine | Analyzes historical spend patterns to predict future cost spikes. |
| Automated remediation | Executes scaling, pause, or termination actions via APIs without manual intervention. |
| Enterprise‑grade security | Role‑based access controls (RBAC) and encryption meet ISO 27001 and SOC 2 standards. |
These capabilities create a fertile environment for Select’s specialized algorithms to operate at scale.
Why Snowflake Cost‑Optimization Matters
- Rapid Growth of Data Warehouse Spend
- Global Snowflake consumption rose 43 % YoY in 2025, pushing average annual spend per enterprise to $1.9 M.
- Complex Pricing Model
- Separate charges for compute (credits), storage, and data transfer cause hidden cost leakage.
- Dynamic Workloads
- On‑demand scaling and variable concurrency make manual budgeting ineffective.
Automated cost‑optimization directly addresses these pain points, delivering measurable ROI.
How Select’s technology Enhances DoiT CIP
- Real‑Time Credit Monitoring
- Pulls Snowflake credit usage every 5 minutes via the Snowflake Usage API.
- Predictive Scaling Recommendations
- Machine‑learning models forecast peak credit consumption with ±3 % error margin.
- Policy‑Based Automation
- Pre‑defined rules (e.g., “pause idle warehouses >30 min”) trigger Lambda/Functions scripts automatically.
- Savings Attribution & Reporting
- Breaks down saved credits by department, project, and tag, feeding directly into DoiT’s cost‑allocation module.
The result is a unified console where cloud administrators can manage both infrastructure and data‑warehouse spend from a single interface.
Key Features of the Integrated Snowflake Cost‑Optimization Module
- Dynamic Warehouse Sizing – Adjusts compute size based on historical query latency benchmarks.
- Idle Detection Engine – Identifies warehouses with <5 % CPU utilization for >15 minutes and suggests suspension.
- Credit Forecast Dashboard – Visualizes projected monthly credit consumption vs. budget.
- Anomaly Alerts – Sends Slack/Teams notifications when credit usage spikes >2× the baseline.
- Tag‑Based Cost Allocation – Maps snowflake credits to business units using DoiT’s tagging framework.
Tangible Benefits for Enterprises
- Cost Reduction – Early adopters reported 22 % average savings on Snowflake spend within the first three months.
- Operational Efficiency – Automated actions cut manual oversight time by 65 % for cloud finance teams.
- Improved Governance – centralized policy enforcement reduces compliance risk across multi‑cloud environments.
- Scalable Management – One-click rollout of cost‑optimization policies across hundreds of snowflake warehouses.
Implementation Workflow
- Connect Snowflake Account – Use OAuth to authorize DoiT CIP access to the Snowflake usage API.
- Enable Select Module – Activate the cost‑optimization toggle in the platform settings.
- Define Policies
- Example: “Suspend warehouses with <10 % CPU for >20 min.”
- Run Baseline Analysis – The system collects 30 days of usage data to calibrate the ML model.
- Review Recommendations – Dashboard presents actionable items; approve or schedule auto‑apply.
- Monitor Savings – Use the Savings Attribution Report to track credit reductions per department.
Real‑World Example: Global Retailer’s savings Journey
- Company: A Fortune 500 retailer with 120 Snowflake warehouses across 4 regions.
- Challenge: Uncontrolled auto‑scaling led to a $3.2 M overspend in Q4 2024.
- Solution: Deployed DoiT’s integrated Select module, applying idle‑detection and predictive scaling policies.
- Outcome (Q1 2025):
- $680 K saved in credits (21 % reduction).
- Query latency improved by 12 % due to right‑sized warehouses.
- Finance team reduced manual audit hours from 160 h/month to 55 h/month.
Practical Tips to Maximize Snowflake Savings
- Tag All Warehouses – Consistent tagging enables precise cost allocation and policy targeting.
- Leverage Auto‑Suspend/Resume – Set a low idle timeout (e.g., 5 min) for development environments.
- Implement Warehouse Clustering – Consolidate similar workloads to reduce the number of active warehouses.
- Review Credit Forecast Weekly – Adjust budgets before peaks (e.g., holiday sales events).
- Combine with Data Retention Policies – Archive cold data to cheaper storage tiers to cut storage credits.
Future Roadmap and Industry Impact
- Q2 2026: Release of a cross‑cloud cost optimizer that aligns Snowflake spend with complementary services (e.g., Redshift, BigQuery).
- Q4 2026: Introduction of predictive budgeting powered by DoiT’s AI layer, offering spend recommendations at project‑proposal stage.
- Long‑Term Vision: Position DoiT CIP as the default governance hub for any SaaS data‑warehouse, ensuring obvious, automated cost control across the enterprise cloud stack.
