How Impossible Foods Achieved Up to 20% Cloud Cost Savings with Aquila Clouds
Impossible Foods partnered with Aquila Clouds to gain control over its complex, multi-cloud environment spanning Azure and GCP. Facing challenges in visibility, AI workload cost tracking, and financial governance, the food tech giant adopted Aquila Clouds' FinOps platform. The result: up to 20% cloud cost savings, complete cost visibility, and automated insights that improved operational efficiency and empowered R&D teams.
01 Intended Audience
- This case study is crafted for CIOs, FinOps leaders, cloud engineering teams, and digital transformation stakeholders evaluating cloud cost management solutions for multi-cloud environments.
02 Their Business at a Glance
- Impossible Foods, a California-based pioneer in plant-based meat and food technology, is valued at approximately $7B and operates in 20,000+ locations worldwide. With a team of 700+, this food tech leader is redefining meat through science — driving sustainability and innovation at a global scale.
To support its fast-paced innovation, Impossible Foods runs a complex multi-cloud environment — utilizing Microsoft Azure for production and Google Cloud Platform (GCP) for research and development. As cloud usage scaled, the company faced mounting challenges in cost control, visibility, and cross-team accountability.
Multi-Cloud Footprint (Assessment of a suborganization)
- 4,355+ Resources: Large-scale resource management
- 51 Services: Broad service adoption added complexity
- 2 Azure & 62 GCP Projects: Fragmented, multi-account environment
This vast and distributed footprint presented significant challenges in gaining unified cost visibility, aligning usage with business goals, and efficiently managing financial governance across departments — prompting the need for a smarter, centralized FinOps solution.
03 Challenges
The challenges that brought Impossible Foods to Aquila Clouds:
Multi-Cloud FinOps & Cost Observability Gaps
1. Visibility & Governance Deficiencies
- Lack of FinOps Tooling: No dedicated tools in place to effectively monitor, govern, or optimize cloud spend across environments.
Example: The IT team was managing payments for GCP (used by R&D) without any visibility into usage. AI-related bills kept growing, and they had to manually segregate costs before gaining insights or optimizing spend. - Fragmented Multi-Cloud View: Disconnected cost and usage data across Azure and GCP hindered unified analysis and financial accountability.
- Missing Unified Dashboards: Absence of a centralized “single pane of glass” to track, manage, and optimize organization-wide cloud spend.
2. Limited AI Workload Cost Transparency
- Service and Resource-Level Gaps: Inability to obtain granular cost breakdowns for AI workloads impeded tracking and optimization.
- Lack of Model-Level Visibility: Teams lacked insight into specific AI/LLM models in use (e.g., various ChatGPT versions), making it hard to align spend with business value.
- Misaligned Model Selection: Without usage and cost transparency, teams inconsistently chose models, leading to inefficiencies in performance and cost.
3. Spend Control Challenges
- Uncontrolled AI Cost Surges: Spiky cost increases during operations like customer sentiment analysis or large-scale data processing occurred due to lack of spend limits or visibility.
- Limited Controls in Shared Environments: Multi-tenant instances (e.g., on shared Azure AI) lacked real-time tools for monitoring or capping spend, unlike privatized instances.
- Manual Cost Allocation: Mapping spend across departments, teams, or projects was manual and error-prone, limiting financial accountability.
4. Operational Inefficiencies
- Manual Cost Analysis Workflows: Teams relied on labor-intensive processes to extract, consolidate, and compare cost data due to the absence of automation.
- No Resource-Level Drill-Downs: Lack of granularity slowed efforts to isolate cost drivers and implement optimization actions.
Optimization Barriers
- Insights Identified but Not Executed
- Unrealized Cost Optimization Opportunities: Multiple actionable savings were discovered but not implemented, often due to process gaps or lack of ownership.
- Execution & Alignment Challenges
- Lack of Visual Justification for Rightsizing: Teams struggled to persuade app owners to downsize resources due to the absence of historical metric trends—something Aquila Clouds uniquely addressed.
Cloud Native Cost Management Platform Limitations & Operational Friction
- Disjointed Tooling
- Lack of a Unified View: Users rely on multiple systems to evaluate cloud spend—no consolidated “single pane of glass” for cost analysis.
- Native Tool Fragmentation: Azure and GCP require separate dashboards or external integrations (e.g., BigQuery, Looker) to achieve end-to-end visibility.
- Manual, Time-Intensive Workflows
- Export-Heavy Processes: Users frequently resort to exporting data as spreadsheets or CSVs, applying custom filters manually to perform basic analysis.
- Limited In-UI Data Access: For example, Azure surfaces detailed cost data only via exports—its default UI lacks depth and flexibility.
- Insufficient Cost Granularity
- Surface-Level Breakdown: Azure provides only daily or monthly views, without tag-level, BU-specific, or custom label-based segmentation.
- Limited Business Mapping in GCP: While GCP supports breakdowns by project, service, and SKU, it lacks built-in support for shared cost allocation, custom tags, or financial hierarchy mapping
- Unintuitive Interfaces & Lack of Intelligence
- Design Not Tailored for FinOps: Native UIs in both Azure and GCP are not optimized for detailed financial insight or multi-dimensional analysis.
- Missing Smart Capabilities: Platforms lack embedded automation, forecasting, or actionable insights to enable proactive cost governance.
04 Solution
- Insights Identified but Not Executed Consolidated usage and cost data from Azure and GCP (via Azure Plan/Cost Details API) into a single-pane-of-glass view, enabling centralized visibility across cloud platforms.
- Granular AI Workload Cost Insights Delivered deep, resource- and service-level visibility into AI workload costs. Empowered teams to identify cost drivers, understand allocation breakdowns, and generate detailed reporting.
- Automated Cost Intelligence Eliminated manual cost analysis through automated assessments, improving accuracy and reducing operational overhead. Strengthened FinOps governance with scalable data models and real-time reporting.
- AI-Powered Optimization Recommendations
- Identified opportunities for VM rightsizing (up/down-scaling)
- Detected idle instances and orphaned snapshots for cleanup
- Flagged unattached storage volumes driving unnecessary costs (Identified volumes that were left behind after VM decommissioning — typically hard to trace without centralized visibility.)
- Recommended reservation candidates to optimize long-term savings
- Accurate Chargeback & Showback Enabled precise cost attribution to projects, teams, and AI workloads through robust chargeback and showback mechanisms.
- Customizable, Role-Based Dashboards Delivered tailored dashboards for engineering, finance, and executive stakeholders — enhancing visibility, alignment, and cross-functional collaboration.
05 Results
ROI Highlight
- Cloud Spend Analyzed: $207,703.44
- Total Benefit Realized: $34,887.24
- 📈 Return on Investment (ROI): 16.79%
— a 17% ROI win!
- 17–20% Cloud Cost Reduction: Delivered via proactive resource optimization and automation, without impacting performance.
- $34.9K Total Financial Benefit:
- $27.2K from instance rightsizing & commitments
- $7.7K from idle resource cleanup
- 100% Cost Visibility: Achieved detailed, resource-level transparency across Azure and GCP.
- Zero Manual Effort: Automated insights generation removed the need for manual analysis.
- Accurate Forecasting: Enabled budget planning and reporting for AI workloads.
- Improved Operational Efficiency: Faster detection and resolution of underutilized resources.
- Stronger Financial Control: Enhanced governance with precise cost attribution.
- Empowered R&D: Transparent usage tracking allowed teams to innovate with confidence.
- Unified Governance: Streamlined multi-cloud management and compliance.
06 Conclusion
Impossible Foods’ partnership with Aquila Clouds demonstrates how AI-driven cloud financial management platforms can empower companies to take full control of their multi-cloud environments. By enabling granular visibility, intelligent cost allocation, and actionable optimization, Aquila Clouds has become a critical tool in Impossible Foods’ journey toward sustainable cloud usage and financial discipline.