AI & LLM Cost Visibility
Unified Cost Observability for AI, LLM, and Modern Workloads
As AI adoption accelerates across enterprises, spending on LLM, Kubernetes, Databricks, Snowflake, and AI infrastructure is growing rapidly, but financial visibility into these costs remains fragmented and opaque. Teams are left guessing where AI spend is going, which models are expensive to run, and which workloads are driving cost spikes.
AquilaClouds Andromeda™ provides a unified AI financial control plane that delivers real-time cost observability across LLM platforms, AI infrastructure, Databricks, Kubernetes, Snowflake, and AI-native environments, giving organizations the ability to understand, attribute, and govern every dollar of AI and ML spend through a single platform.
- AI and LLM cost observability across APIs, models, and AI infrastructure
- Real-time spend analytics and trend analysis for AI workloads
- Cost attribution for AI/ML across teams, projects, and business units
- Kubernetes, Databricks, Snowflake, and modern AI platform visibility
- Compute workload cost tracking and anomaly detection
- LLM token-level usage cost tracking and per-model cost analysis
- Actual vs forecasted AI spend comparison
- Business-aligned AI cost reporting for finance and leadership
- Executive-level AI financial summaries and ROI intelligence
Improve AI and cloud cost transparency
Reduce dependency on manual reporting: Traditional AI cost reporting relies on manually built dashboards, scheduled exports, and siloed spreadsheets that are often outdated by the time they are reviewed. Andromeda eliminates this dependency by enabling on-demand, AI-generated financial reports that pull from live, unified data across all AI and LLM environments. Teams no longer need to wait for monthly reporting cycles or rely on data engineers to produce custom extracts. Accurate AI financial intelligence is always available, on demand.
Reduce manual reporting effort
Improve operational productivity: By automating the most time-consuming aspects of cloud financial operations including data aggregation, report generation, and anomaly triage Andromeda frees FinOps practitioners, engineers, and finance teams to focus on higher-value strategic work. Agent Sherlock handles repetitive analytical tasks through AI-driven orchestration, reducing the operational burden on teams and enabling them to manage larger, more complex cloud and AI environments without proportionally scaling headcount.
Enable faster operational and executive decision-making
Simplify access to AI financial insights: AI cost data is often locked behind complex tooling, proprietary dashboards, and technical expertise that many business stakeholders do not possess. Agent Sherlock democratizes access to AI financial intelligence by allowing any team member — from a data scientist to a CFO — to interact with LLM and AI infrastructure spend data through plain language. This removes the technical barrier to insight, enabling broader organizational participation in AI cost governance and financial accountability.
Create a single source of truth for AI financial operations
Improve operational productivity: By automating the most time-consuming aspects of cloud financial operations including data aggregation, report generation, and anomaly triage Andromeda frees FinOps practitioners, engineers, and finance teams to focus on higher-value strategic work. Agent Sherlock handles repetitive analytical tasks through AI-driven orchestration, reducing the operational burden on teams and enabling them to manage larger, more complex cloud and AI environments without proportionally scaling headcount.
Turn AI and LLM spend into proactive financial control.
Without centralized visibility, organizations struggle to understand AI cost growth, identify ownership of LLM usage, or optimize spending across models and platforms. Andromeda enables organizations to move from reactive reporting to proactive AI financial control.