Take Control of Your
AI & Cloud Economics
With One Financial Platform
Andromeda unifies multi- and hybrid-cloud (AWS, Azure, GCP, OCI, Huawei Cloud, VMware), Databricks, Kubernetes, Snowflake cost visibility, AI/LLM workload cost intelligence, and end-to-end FinOps + BillOps workflows in a single financial control plane, purpose-built for enterprises and MSPs managing serious cloud and AI spend.
The Problem
AI & Cloud financial complexity
has outpaced every tool your team is currently using
As AI workloads scale and multi-cloud estates grow, finance, engineering, and operations teams are flying blind since they are using spreadsheets for billing, siloed dashboards for cost visibility, and guesswork for forecasting.
Runaway LLM Spend
LLM token usage and costs are unpredictable and nearly impossible to forecast with existing tools. Teams can’t attribute which products, teams, or experiments are driving AI cost growth until the invoice arrives.
AI, and Multi & Hybrid-Cloud Cost Blind Spots
Cost data is often fragmented across AWS, Azure, GCP, OCI, Huawei Cloud, on-premises VMware environments, and modern platforms such as Databricks, Kubernetes, Snowflake, AI/LLM workloads, and Microsoft 365 licenses. As a result, finance and technology leaders lack a single, trusted financial view for chargeback, cost allocation, governance, and strategic decision-making across increasingly complex cloud and AI environments.
Billing & Margin Leaks for MSPs & Resellers
Usage-based billing is complex: unbilled services, misapplied rate cards, and manual reconciliation cycles erode margins. MSPs and cloud resellers lose revenue and spend days every month reconciling customer invoices.
These challenges can’t be addressed with another isolated dashboard or point tool. They require a platform that unifies financial data, AI/ML-driven intelligence, and operational workflows across cloud spend, AI workloads, and billing in one place. That’s what Andromeda is built to do.
The Andromeda Platform
Cloud Financial Management
Multi-& hybrid-cloud cost visibility, allocation, showback/chargeback, and forecasting across all cloud environments and business units.
Billing & Revenue Assurance for MSPs
Usage rating, invoicing, reconciliation, and margin analysis consolidated in one place, eliminating revenue leakage and reducing billing cycle time.
AI & ML Cost Intelligence
Dedicated tracking of AI, LLM, Kubernetes, Databricks, Snowflake workload costs with anomaly detection, attribution, and forecast accuracy improvements powered by ML models.
Automated FinOps & BillOps Workflows
Policies, budget alerts, and integrations that automate recurring FinOps reviews, invoice checks, savings execution tasks, approval workflows, and billing lifecycle operations.
Platform Capabilities
How Andromeda informs, optimizes,
and operates your cloud & AI financials
Andromeda applies AI and ML across three operational modes which gives finance, engineering, and operations teams the visibility to understand spend, the intelligence to reduce it, and the workflow automation to act on it across cloud, AI workloads, and billing operations simultaneously.
- Unified cost and usage views across AWS, Azure, GCP, OCI, Huawei Cloud, and VMware breaking down spend by service, region, team, and resource tag in a single financial dashboard.
- Cost allocation to business units, projects, and customers including attribution of AI / LLM / Kubernetes / Databricks / Snowflake workloads by team, product, or initiative for accurate internal accounting.
- Showback and chargeback reporting with unit economics for services and AI initiatives, enabling finance teams to hold business units accountable for their cloud and AI consumption.
- Forecasts and budget vs. actuals tracking for cloud and AI spend, giving leadership real-time visibility into whether teams are trending over or under their financial targets.
- Infrastructure rightsizing: Reduce costs through right-sizing compute, storage and networking resources, database instances, Kubernetes clusters, nodes, pods/containers, and more.
- Reduce idle resources: Identify idle resources through utilization of data analysis.
- Commitment Management: Analyze usage patterns to identify opportunities to purchase, resize, or reduce commitment plans such as Reservations and Savings Plans. Proactively alert teams on upcoming expirations to prevent unexpected cost increases and maintain optimal coverage.
- Budget guardrails: Implement policies and alerts to prevent runaway AI & Cloud spends in cost centers.
- Automated workflows: Schedule regular FinOps reporting, initiate approval workflows for savings recommendation executions, trigger review process for generated invoices, execute policy-driven actions.
- Proactive governance: Shift from reactive firefighting to automated financial governance, including real-time budget tracking and chargeback/showback reporting across business units.
- Billing & Revenue Assurance: Consolidate and reconcile usage-based bills for AI & Cloud consumptions, customize pricing and SKUs, and automated bill generation through ERP system integration to eliminate revenue leakage and reduce billing cycle time.
32%
Average reduction in cloud spend identified and addressed with AndromedaFinAI
4×
Improvement in finance and FinOps team productivity across cloud and AI cost reviews
68%
Reduction in time spent on billing operations and reconciliation for MSPs and resellers
$2.1M
Median annualized savings surfaced in first 90 days on the Andromeda platform
Platform Capabilities
Frequently Asked Questions
What is AquilaClouds Andromeda™, and how is it different from a generic cloud cost management tool?
Andromedas Aquila Clouds’ Agentic AI platform for AI & Cloud Financial Management Platform a purpose-built financial control plane that unifies FinOps, BillOps, and AI and LLM cost intelligence in a single system of record. Unlike generic cloud cost tools that focus narrowly on infrastructure spend across one or two clouds, Andromeda is designed to handle the full financial complexity of modern enterprises and MSPs: multi- and hybrid-cloud cost allocation and forecasting, usage-based billing and margin analysis for resellers, and dedicated tracking of generative AI and LLM workload costs that generic tools weren’t built to handle. The platform also includes Sherlock, a conversational FinOps agent embedded within Andromeda, not a standalone product that lets finance and engineering teams query their cloud and AI financial data in natural language and trigger workflows directly from the answers. The result is a financially grounded platform where visibility, optimization, and operational automation exist in one place rather than across five disconnected dashboards.
How does Andromeda help organizations control AI and LLM costs?
It provides dedicated tracking of AI and LLM workload costs. It features anomaly detection, granular attribution by team/project/product, and ML-powered forecast accuracy improvements to prevent unpredictable AI spend growth.
Can Andromeda support FinOps for AI workloads alongside traditional cloud services in the same platform?
Yes. Andromeda unifies multi- and hybrid-cloud infrastructure costs across AWS, Azure, GCP, OCI, Huawei Cloud, and VMwarewith AI/LLM/ workload cost intelligence into a single financial control plane, enabling centralized visibility, allocation, optimization and reporting. The platform also supports Kubernetes, Databricks, Snowflake, Microsoft 365 license cost management.
How does Andromeda support MSPs and cloud resellers with billing, margin analysis, and revenue assurance?
The platform automatically consolidates cloud and AI usage data across multi- and hybrid cloud environments, and maps it against customizable pricing SKUs for invoicing, reconciliation, and margin analysis. It streamlines the entire billing lifecycle operations, eliminates revenue leakage (such as unbilled services) and reduces manual billing cycle times for complex, usage-based multi-cloud billing scenarios.
What types of AI/ML-driven insights does Andromeda provide for cloud and AI spend?
Andromeda uses machine learning across three financial intelligence layers. First, anomaly detection continuously monitors cloud and AI workload spend patterns to identify statistically significant deviations. Second, the platform’s forecasting models learn from historical usage patterns across multi-cloud, Kubernetes, and AI workloads to produce more accurate forward-looking spend projections than rule-based tools. Third, AI cost optimization surfaces prioritized rightsizing, commitment, and efficiency recommendations based on actual usage data, ranked by financial impact so teams work on what delivers the most savings. All of these insights are accessible through Andromeda’s dashboards and through Sherlock, the conversational agent embedded within the platform, which can explain anomalies, summarize forecast variances, and walk teams through optimization opportunities in plain language.
How does the Sherlock agent work inside Andromeda, and what can teams ask it about cloud and AI finances?
Sherlock is a conversational FinOps agent embedded directly in the platform. Users can query cloud and AI financial data in natural language—such as asking what is driving a specific spend spike—and trigger optimization workflows or draft briefs based on the agent’s responses.
How does Andromeda fit into an existing practice or Cloud Center of Excellence?
It acts as the “system of record” that replaces siloed dashboards and spreadsheets. It integrates automated FinOps reviews, budget alerts, and optimization recommendations into a single workflow, allowing teams to shift from reactive firefighting to proactive financial governance.
What data sources and environments can Andromeda connect to?
The platform is built for multi- and hybrid-cloud environments, supporting AWS, Azure, GCP, OCI, Huawei Cloud, and on-premises VMware infrastructures. It also provides cost visibility and optimization for modern platforms and services including Databricks, Kubernetes (EKS/AKS/GKE), Snowflake, Microsoft 365 licenses, Microsoft 365 Copilot, Anthropic, OpenAI, Gemini, Azure AI Foundry, and other AI/LLM ecosystems.