Traditional cost analysis tools require you to know exactly which dashboard to open, which filters to apply, and how to interpret the results. But what if you could just ask a question?
The Problem with Traditional Cost Analysis
Consider this scenario: your VP of Engineering asks, "How much did we spend on database services in the APAC region last quarter?" In a traditional workflow, you'd need to:
1. Open the cost reporting tool
2. Select the right date range
3. Filter by service category
4. Filter by region
5. Export the data
6. Possibly pivot in a spreadsheet
This takes minutes to hours, depending on the tool. With natural language queries, it takes seconds.
How Text-to-SQL Works
OCIFinOps uses AI to convert plain English questions into SQL queries that run against your cost data. The process works in three steps:
1. Understanding the Question
The AI model parses your question to identify:
What: you're asking about (cost, usage, count)
Which dimensions: to filter on (service, region, compartment, time)
How: to aggregate (sum, average, group by)
2. Generating the Query
Using knowledge of your database schema and OCI cost report structure, the AI generates a precise SQL query. For example:
"What's my most expensive service this month?" becomes:
SELECT product_service, SUM(cost_my_cost) as total_cost
FROM cost_data
WHERE interval_usage_start >= DATE_TRUNC('month', CURRENT_DATE)
GROUP BY product_service
ORDER BY total_cost DESC
LIMIT 1
3. Returning Results
The query runs against DuckDB (our embedded analytical database) and results are returned instantly — along with the generated SQL for full transparency.
Example Questions You Can Ask
Here are real questions our users ask daily:
•"What's my total spend this month compared to last month?"
•"Which compartment has the highest compute costs?"
•"Show me the daily trend for Object Storage costs in the last 30 days"
•"What are my top 5 most expensive resources?"
•"How much am I spending on networking across all regions?"
•"What percentage of my spend goes to databases?"
Why This Matters
Natural language queries democratize cost data. Instead of requiring specialized knowledge of cost reporting tools, anyone in the organization can get answers. This means:
Finance teams: can pull numbers for budgeting without engineering help
Engineering managers: can check their team's spend without navigating complex dashboards
Executives: can get high-level summaries instantly
The goal is to make cloud cost data as accessible as asking a colleague a question.