Scale without breaks. Wasted compute, eliminated.
e6data invented new primitives from the ground up, deeply engineering to scale capacity 1-vCPU at a time and eliminate wasted compute from jump step cluster costs.
Decentralized services, speaking through defined contracts.
Each service sized and scaled on its own.
Atomic sizing scales capacity by 1 vCPU, not step jumps.
No central coordinator, no bottleneck, no single point of failure.
Comparison: atomic vs step-jump scaling, cost and QPS under production workloads+−
Legacy engines scale in step jumps, so cost climbs in blocks from $25 to $100 while capacity sits idle. Atomic scaling follows the same load in 1-vCPU increments, from $15 to $74 at peak.
How does e6data save you 50-60% on compute cost?+−
T-shirt sizing scales in coarse step jumps, so cost outruns load. Atomic sizing scales by the vCPU, so cost tracks load. The same four scenarios, side by side.
Base size Lx1 (~160 vCPUs). Cost per hour = $25/hr x 1.
Smallest unit of scaling is L. Autoscaling kicks Lx1 to Lx2: 100% more cost for a 20% load increase.
Cost rises 100% (from $25/hr to $50/hr) despite load rising only 20%.
Cost rises 300% (from $25/hr to $100/hr) despite load rising only 44%.
Granular services sized to fit. Base ~100 vCPUs. Cost per hour = $15.6/hr x 1.
Fine-grained scaling keeps cost commensurate: a 20% load increase costs 20% more.
Resource increase tracks load: 20% more work costs 20% more.
Resource increase tracks load: 44% more work costs 44% more.
One installation between the tools you keep and the tables you own
e6data fits beneath the platforms and dashboards you already run and executes their heaviest work directly on your open tables. Nothing moves, or gets rewritten.
The e6data engine powers our suite of products
15M customer-facing queries a day at 60-75% lower TCO
Production dashboards served off open tables, side by side with the customer's existing platforms in place. Lower cost, same infrastructure.
Why it holds up
Products with proven results and measurable performance outcomes to check for yourself, before you buy.
See the benchmarks →On your data, wherever it lives
Run in your cloud, your VPC, on-prem, or a sovereign environment, on the open tables you already govern. Compute comes to the data, so egress drops by ~99%.
A fraction of the cost
Scaling in 1-vCPU increments kills over-provisioning: you pay for the compute a query needs and nothing around it. Full-grain data, no down-sampling.
Drop in, don't rip out
e6data works alongside Databricks, Snowflake, and Trino on the same tables. No migration, no rewrites, no governance changes; the exit is re-pointing, not re-platforming.
Real-time by default
Streaming data is queryable in ~15 seconds, with sub-second ingest latency. No per-GB indexing fees, so you keep every event instead of rationing retention.
Your data never leaves your perimeter
e6data deploys serverless or into your cloud account, VPC, on-prem, or air-gapped environment, and queries run where the tables live. Zero data movement, and your existing governance and residency controls stay in force.
Security and trust →"We achieved 1,000 QPS concurrencies with p95 SLAs of < 2s on near real-time data & complex queries. Other industry leaders couldn't meet this even at a far higher TCO."
The questions your team will ask
Plain answers to the objections a champion hears internally.
Does this involve migrating data out of Snowflake or Databricks?+−
No. There is zero data movement: the engine comes to your tables, wherever they live. Your platforms, catalogs, and governance stay exactly as they are.
Can it handle high concurrency?+−
Yes: 1,000+ QPS with p95 under 2 seconds, in production and on the record. There is no coordinator as a single point of failure, so concurrency scales without a bottleneck.
Won't this cost more?+−
It runs alongside what you have, so you move only the workloads where it wins. Customers see up to 50% lower compute cost on those workloads, because per-vCPU scaling removes over-provisioning.
How much effort will this take to implement?+−
Point it at your existing tables and connect through standard interfaces such as JDBC and your BI tools. No query rewrites, no pipeline changes, and production results on the first workload in weeks.
Where does it fit in my stack?+−
Between the tools your teams use and the open tables you own. Dashboards, notebooks, and agents keep their interfaces; e6data executes the heavy queries underneath, alongside Databricks, Snowflake, or Trino.
Will it work in my environment?+−
Any cloud, your VPC, on-prem, hybrid, air-gapped, or sovereign. It reads any open format, including Iceberg, Delta, and Hudi, with any catalog.
See your numbers on your workloads
Reach out to book a demo, share challenges you're facing, and tell us how this fits into what you're currently working on or thinking about. Not ready for a conversation?
Problems we're solving →