Your analysts spend 80% of their time on infrastructure and 20% on analysis. MinusOneDB delivers 100–1000x better cost per query so they can focus on insights that move the business.
Calculate Your SavingsThe real cost isn’t the monthly bill — it’s the strategic opportunities you can’t pursue because data exploration is too expensive, too slow, or too risky.
| Strategic Impact | What It Looks Like Today | Hidden Business Cost |
|---|---|---|
| Decision Paralysis | $10K–$100K warehouse queries force “safe” analysis only | Strategic pivots delayed 3–6 months |
| Competitive Blindness | Cost and performance limits breadth to dozens of queries instead of millions | Insights never discovered |
| Innovation Bottleneck | Product ideas die because proving them requires prohibitive analysis | Potential features never tested |
| Organisational Silos | Each department builds separate data marts to control costs | Cross-functional insights missed; duplicate infrastructure |
| Talent Underutilisation | Data scientists spend 80% of time on infrastructure, <1% on insights | $200K+ analysts doing plumbing work |
Traditional warehouses rely on compute-intensive table scans. MinusOneDB takes a fundamentally different approach.
Comprehensive pre-indexing shifts heavy work to the write/ingest phase. Queries traverse optimised indexes in seconds at a fraction of the cost.
True streaming ingest — each write is index-visible within ~2 seconds. No micro-batch lag or complicated ETL pipelines.
Any dataset at any scale can be rebuilt from object store in ~3 hours — essential for DevOps at scale and data sovereignty.
REST API + JS SDK integration means a web engineer can build what used to require a dedicated data engineering team.
Traditional BI tools require pre-aggregated, purpose-built data marts. This creates engineering overhead, analytical rigidity, and irreconcilable reports. MinusOneDB eliminates the compromise.
| Traditional BI Limitation | Impact | MinusOneDB Approach |
|---|---|---|
| Memory constraints | BI tools crash when queries return millions of rows | Native query interface processes billions without in-memory bottlenecks |
| Aggregation dependencies | Pre-calculated aggregations limit exploratory analysis | Real-time aggregations at the storage layer |
| Fixed data models | Datamarts must be pre-designed for specific analytical paths | All queries execute directly against raw data via REST API |
| Connection timeouts | Long-running queries against raw data frequently timeout | Constant-time queries regardless of data scale |
Teams want to democratise data access with natural-language tools, but when individual queries cost tens of thousands of dollars, these efforts get shut down fast. MinusOneDB’s capacity-based pricing changes the equation.
LLM-powered agents issue dozens to hundreds of exploratory queries in minutes, surfacing anomalies and “unknown unknowns” for every stakeholder — not just the few who know how to query. Where a typical complex query costs hundreds to thousands on traditional warehouses, the same exploratory session on MinusOneDB costs pennies.
Continuous monitoring across all business metrics simultaneously. No more choosing which KPIs to watch because query budgets are tight.
Executives ask complex questions in plain English. The system explores, evaluates, and surfaces answers — without generating a terrifying query bill.
Automated market analysis runs continuously. Pricing optimisation responds to competitor moves in real time with daily granularity across all channels.
When query costs drop 100–1000x, entirely new strategic capabilities become economically viable across every department.
Low-risk, high-impact. No rip-and-replace — coexist with current systems and move workloads as value is proven.
Honest assessment matters. Here’s where we’d recommend alternative or hybrid approaches.
Coexist with current systems during transition. Connect existing BI tools to targeted subsets while using MinusOneDB’s native interface for complex analyses.
Identify highest-value analytics use cases and migrate them first. Move workloads as value is proven, with clear ROI metrics at each stage.
Leverage existing BI tools and analyst skills. Export specifically scoped result sets to feed existing visualisation tools via the REST API.
