$3M+ saved annually in customer-facing analytics
A NASDAQ-listed SaaS company with 68,000+ customers serves 10 million+ in-app dashboard queries a day on one lakehouse. TCO fell by ~60% once fully deployed, with zero-failure SLAs intact.
| METRIC | BEFORE | WITH E6DATA |
|---|---|---|
| Total cost of ownership | 2x cost jump every 18 months | ~60% lower, $3M annualized |
| p95 latency, web dashboards | 30s in early testing | 1.5-2s at 60 QPS peak |
| Failure SLAs on schedules and unloads | non-negotiable requirement | zero failures, met across workloads |
| Copies of data (web vs exports) | separate copies per workload | one lakehouse, web and non-web |
"The moment e6data was able to meet the web SLAs, it became a very easy decision to make one data lake that serves both web and non-web, using e6data as a query engine."
One lake for web and non-web
The bake-off ran e6data against three other engines on real production traffic. The web SLA was the bar; the bill was the tiebreaker.
- 01The stakes
In-app dashboards serving 10 million+ queries a day, on highly normalized data with 10+ table joins per query, against a sub-2-second p95 requirement. Customer-facing analytics is the product, not a feature.
- 02The wall
Compute cost doubled every 18 months on the incumbent engine. The team also wanted a lakehouse architecture for AI and ML use cases, interoperability, and an exit from vendor lock-in, without giving up functionality.
- 03The evaluation
Before committing, the team set the bar: the criteria that would decide it - p95 latency under real concurrency, cost at scale, security and private-link connectivity, and ease of adoption - measured on their own data rather than synthetic benchmarks.
- 04The bake-off
Four engines, five criteria: performance, zero-failure SLAs, cost, ease of adoption, and security including private link connectivity. Evaluation started on data exports, then replayed tens of millions of production queries against the dashboard workload.
- 05The result
p95 went from 30 seconds in early testing to 1.5 to 2 seconds at 60 QPS peak, with every failure SLA met. TCO fell ~60%, worth $3M a year fully deployed, and one lakehouse now serves web dashboards and exports from the same tables.
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