For FinOps Analysts
The bridge that gets Finance to actually use your output.
Your FinOps reports are technically correct and operationally ignored. Ternary keeps the cloud rigor you already deliver and adds the Finance-grade output layer the CFO and FP&A will actually read. One dataset serving both audiences.
Attending Gartner Finance Symposium? Reserve a meeting.
Built for the FinOps workflow – read by Finance
Multi-cloud normalization
AWS, Azure, and GCP billing ingested, normalized, and mapped to a shared cost taxonomy that feeds the same ledger Finance reads from.
FinOps + Finance alignment
Stop producing reports Finance ignores. Your unit economics and showback flow straight into the Finance ledger, so both sides argue from the same numbers.
Anomaly detection
Real-time alerts on spend spikes, idle resources, and prepaid credit burn, so governance kicks in before the surprise invoice instead of after.
Commitment optimization
Savings plans and reserved-instance coverage modeled against actual utilization, with scenario planning Finance can underwrite.
Cost-to-outcome gap
We are very immature in AI attribution. Engineering is using Cursor, Glean. We track it at cost centers at best. When we get bill shock, that’s when governance kicks into gear.”
Sr. Manager, Cloud Infrastructure FP&A
Enterprise SaaS
Ternary as data hub
I’m already pulling third-party AI vendor data into Ternary via BYOD to get a unified cost view. I’m also downloading Ternary CSVs and enriching them in Claude, looking at biggest movers, identifying outliers, and building 12-month forecasts.
Head of FinOps
Enterprise Contact Center SaaS
Before & after Ternary
Before
Reports Finance technically receives but operationally ignores
After
Showback and unit economics flowing into the same ledger Finance trusts
Before
Manual CSV exports and one-off reconciliations every month-end
After
Normalized multi-cloud + AI data piped directly into FP&A workflows
Before
Bill shock surfaces governance after the invoice lands
After
Real-time alerts on spend spikes, idle resources, and credit burn rate
Before
Commitment decisions made on gut feel and vendor pressure
After
Savings plan and RI coverage modeled against actual utilization
Ready to govern your full technology estate?
Experience Technology Investment Intelligence in minutes. Query our pre-loaded enterprise sample ledger directly inside Claude, ChatGPT, or your LLM – or speak with our team about connecting your live multi-cloud estate.
Zero commitment. Pre-loaded with sample enterprise data.
Finance-first data model
Judgment infrastructure
SAP · Oracle · Workday integration
Token-level AI attribution
Vendor lock-in avoidance