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AGENT-READY DATA FOUNDATIONS

More intelligence, per dollar.
Same performance.

Embeddable, high-velocity compute engines and end-to-end intelligence for frontier organizations to process data and act on analytics at scale in any lakehouse, cloud, or on-prem environment.

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PROVEN AT ENTERPRISE SCALESQL analytics · Near real-time streaming · AI workloads
Query Engine
100M
queries per day
1,000+ QPS sustained, under 2s at p95
Low latency queries · Semantic retrieval
Ingest Engine
1.5M
events ingested per second
< 5s source to Iceberg
Live ingestion · Streaming AI enrichment
AgentFlow
3-15x
lower tokens and warehouse compute
Governed agent workflows · Graph engineering
Up to 50%lower TCO at same or better performance
TRUSTED BY TEAMS RUNNING MISSION-CRITICAL DATA
PRODUCTS

Your stack is fast, until your bill scales faster than your analytics.

We are building agent-ready data foundations that close the event-to-action gap, one delay at a time, on open standards with unit economics and performance that hold at scale.

PeopleAgentsApps
Your perimeter · any cloud
open tablesIceberg · Delta · Hudi
CatalogsGlue · Unity · Polaris
Your object storageS3 · ADLS · GCS · on-prem
Your data platformSnowflake · Databricks · Self-managed

Query Engine

High-concurrency SQL and AI analytics on your open tables, scales in 1-vCPU increments.

Explore
ETL Engine

Clean, normalize, and model raw data into business-ready tables.

Private beta
Ingest Engine

One unified pipeline tool. Real-time streaming ingest and events queryable in seconds.

Explore AgentFlow

Governed agents that anticipate before you ask and act while it counts.

Explore
DO MORE WITH EVERY DOLLAR

Every dollar goes up to 2–3x further.

We drop into your existing stack to deliver the same or better performance at a fraction of the cost, so you can compound your enterprise intelligence at the velocity agents move.

Ask more.

All data and analytics, less spend.

  • Full grain, no downsampling.
  • Stays fast at high concurrency and powers high velocity agent traffic.
Up to 50%
lower TCO at same or better performance
Query EngineIngest Engine

Anticipate more.

Agents, always on.

  • Streaming data is queryable in 15 seconds, with sub-second ingest latency.
  • No per-GB indexing fees, keep every event instead of rationing retention.
1,000+
QPS under 2s
AgentFlowQuery Engine

Automate more.

Answers become actions.

  • Half the AI model calls on the same workflow, pay for what runs.
  • Every step, tool call and approval recorded for agents to learn and automate the actual work.
up to95%
fewer tokens per run vs. a single agent
AgentFlow
COSTS WE REMOVE

70-80% of data infrastructure spend is in the compute bill. So are the bottlenecks.

BOTTLENECKS WE REMOVE
  1. Concurrency ceilings1,000+ QPS, no coordinator queue
  2. Slow queriesp95 under 2s on near real-time data
  3. Brute-force scalingCapacity in 1-vCPU increments
  4. Agent query stormsSteady performance under fan-out
  5. Step-jump billingPay for the vCPUs you use
  6. Budget blow-upsPredictable costs at any velocity
ATOMIC ARCHITECTURE

Scale without breaking. Wasted compute, eliminated.

Not another Spark or Trino engine. Novel deep tech primitives invented from the ground up, engineered to scale capacity 1-vCPU to deliver compute utilization with surgical precision, not with wasteful warehouse reserves.

Fixed warehouse, fixed allocation.Compute is reserved by warehouse size, not by what each query needs.warehouse 1 · medium · concurrency 4Q1Q2Q3Q4UNUSED RESERVED COMPUTEwarehouse 2 · medium · concurrency 4UNUSED RESERVED COMPUTEQ5QUERIES012345WAREHOUSES12UNUSED, RESERVEDn/a68%▸ Queries arrive in different shapes: tall, wide, small, large.▸ Most of the reserved warehouse sits idle.▸ Q5 arrives. All four slots are taken; the warehouse turns it away.▸ A whole second warehouse spins up, for one small query.e6data: compute sized to each query.No fixed cluster. Capacity scales one vCPU at a time.Q1Q2Q3Q4Q5above the line: compute you no longer pay forQUERIES012345ALLOCATIONper queryUNUSED, RESERVEDn/a~5%▸ The same five queries. e6data fits compute to each one as it arrives.▸ Everything above the line is compute you no longer pay for.UtilizationThe same workload, two ways to pay for it.Fixed warehouse68% of reserved compute idle32% usede6data~5% idle; you keep the difference~95% usedIllustrative workload.
UTILIZATIONThe same workload,two ways to pay for it.Fixed warehouse32% used68% reserved, idlee6data~95% used~5% idleCompute sized to each query, one vCPU at a time.Illustrative workload.
SAME DEMAND · TWO ALLOCATION MODELS DemandIn useIdle
TRADITIONAL WAREHOUSE fixed allocationUTILIZATION–IDLE–
E6DATA dynamic allocationUTILIZATION–IDLE–

e6data's adaptive allocation reaches about 95% compute utilization versus a fixed warehouse, cutting wasted compute to roughly 5%.

Scale up or out: 1 vCPU at a time. No T-shirt sizes.

SCALE UPone heavy query
XS2
S4
M8
L16
XL32
e623
Warehouse bills32 vCPU
e6data bills23 vCPU
Idle capacity you'd pay for with the warehouse: 9 vCPU (28%)
SCALE OUTconcurrent queries and agents
E6DATA · every query starts now, sized to fit
q1
q2
q3
q4
q5
q6
WAREHOUSE · fixed 32-vCPU clusters
C132
C2off
Warehouse bills32 vCPU
e6data bills6 vCPU
Waiting: warehouse 0 queries · e6data 0

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.

Atomic vs. step-jump scaling.Step-jump scaling buys capacity in blocks. Atomic scaling adds one vCPU at a time.153045600QUERIES PER SECONDtime →PRODUCTION LOAD PATTERNLoad, queries per secondStep-jump scaling, legacy enginesAtomic scaling, e6data$50$75$100$25$16$74 AT PEAK67% UNUSED, PAID FOR~28% UNUSEDLEGACY BILL, AT PEAKn/a$25$50$75$100E6DATA BILL, AT PEAKn/a$74UNUSED COMPUTEn/a67%67% vs ~28%▸ Legacy engines buy capacity in blocks: every step up is paid for whether the load uses it or not.▸ e6data adds capacity one vCPU at a time as load arrives, and releases it as the load ends.▸ Same load. The difference is what you pay for and do not use.Illustrative workload.
PRODUCTION LOAD PATTERNAtomic vs. step-jump scaling.15304560queries per secondStep-jump scaling, legacy enginesAtomic scaling, e6dataLoad67%unused · legacy~28%unused · e6data$100bill at peak · legacy$74bill at peak · e6dataIllustrative workload.
Step-jump scaling with legacy engines ($25 → $100) vs atomic scaling with e6data ($15 → $74), against the same production workloads.

Parallel and distributed

No central coordinator, parallel queries fan out independently for sustaining high velocity as concurrency grows.

No copies, no migrations

Compute comes to your regions and clouds. Your data stays put, eliminating hefty egress fees by up to 99%.

Exact use and billing

Scale in precise 1 vCPU units, each service sized and scaled on its own. No compute overprovisioning at peak. No empty stadium with the lights on.

Comparison: atomic vs step-jump scaling, cost and QPS under production workloads+−
How does e6data save you up to 50% 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 query load added / larger load vCPUs e6data adds
W/O E6DATA
COARSE SIZING
BASE QUERY LOAD AND CONCURRENCY

Base size Lx1 (~160 vCPUs). Cost per hour = $25/hr x 1.

‘L’
$25/hr
STEP JUMP SCALING
20% MORE QUERIES
Increase in concurrency / QPS

Smallest unit of scaling is L. Autoscaling kicks Lx1 to Lx2: 100% more cost for a 20% load increase.

‘L’
$25/hr
‘L’
$25/hr
AVG QUERY SIZE UP 20%
Some queries are larger

Cost rises 100% (from $25/hr to $50/hr) despite load rising only 20%.

‘XL’
$50/hr
20% LARGER AND 20% MORE QUERIES
Ad hoc workloads

Cost rises 300% (from $25/hr to $100/hr) despite load rising only 44%.

‘XL’
$50/hr
‘XL’
$50/hr
W/ E6DATA
ATOMIC SIZING
BASE QUERY LOAD AND CONCURRENCY

Granular services sized to fit. Base ~100 vCPUs. Cost per hour = $15.6/hr x 1.

$15.6/hr
100 vCPUs
ATOMIC SCALING
20% MORE QUERIES
Increase in concurrency / QPS

Fine-grained scaling keeps cost commensurate: a 20% load increase costs 20% more.

$18.7/hr
120 vCPUs
AVG QUERY SIZE UP 20%
Some queries are larger

Resource increase tracks load: 20% more work costs 20% more.

$18.7/hr
120 vCPUs
20% LARGER AND 20% MORE QUERIES
Ad hoc workloads

Resource increase tracks load: 44% more work costs 44% more.

$22.5/hr
144 vCPUs
CASE STUDY · NASDAQ SAAS PRODUCTS

15M customer-facing queries
a day at 60–80% lower TCO

Production dashboards served off open tables, side by side with the customer's existing platforms in place. Lower cost, same infrastructure.

Read the case study See the benchmarks
p95 1.2s
query latency at 15M queries a day
KEEP YOUR LEVERAGE

Your data. Your rules.

Your data, context and agent history stay in your perimeter, on open formats, in any cloud or on-prem. Every agent run adds to a record of how your business works that you keep and compound.

Serverless or in your VPC. You choose.AWS S3 · AWS Glue · Unity CatalogAzure ADLS · OneLake · PurviewGoogle GCS · Iceberg REST · PolarisAny object storage · Hive Metastore · Ranger

No new trust boundary

Compute comes to your data.

No governance changes

Your policies stay in force.

No query rewrites

Re-point, don't re-platform.

No new drivers

Connects like Postgres.

No new trust boundary

Compute comes to your data.

No governance changes

Your policies stay in force.

No query rewrites

Re-point, don't re-platform.

No new drivers

Connects like Postgres.

Compatible with what you already run.

See full compatibilityShow less
SQL dialects and data platforms
DatabricksSnowflakeBigQueryRedshiftAthena / TrinoSpark SQLANSI SQL
BI and connectors
TableauPower BIQlikAI agentsReporting toolsAPIsJDBCODBCPython
Catalogs and governance
Unity CatalogHive MetastoreAWS GluePolarisIceberg RESTRangerOPAPurview
Table formats
Delta LakeIcebergHudiParquetJSON
Storage
AWS S3Azure ADLSGoogle GCSOneLakeAny object storage
Deployment
AWSAzureGoogle CloudIn-VPCOn-premAir-gapped

Connects over open catalog APIs, Iceberg REST and Unity REST, the same way Apache Spark does. Zero query rewrites, no data movement, no governance changes.

Enterprise-grade security, inside 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.

Row- and column-level policies, IAM roles and audit logs keep working as they are, enforced from Ranger, OPA or Lake Formation.

Security and Trust Center
  • SOC 2 Type II badge
    SOC 2 Type IIAudited controls, independently attested
  • ISO 27001 badge
    ISO 27001Certified information-security management
  • GDPR badge
    GDPRData residency and processing rights
YOUR PERIMETERBI toolsappsagentse6dataquery enginecatalog · policiesopen tablesobject storagequeries in · results out · data staysYOUR PERIMETERBI toolsappsagentse6dataquery enginecatalog · policiesopen tablesobject storagequeries in · results out · data stays
ACCOUNT FOR EVERY ACTION

Govern every query and agent step. Audit every record.

Every statement is logged with who ran it, from which tool, what it scanned, and how long it took. Every agent run leaves a durable audit record: steps, tool calls, approvals, errors and tokens. Every batch lands whole or not at all.

COST LEDGER · LAST 60sgrouped by actor
  • agent:ops-watch$0.023936%
  • analyst:priya$0.016225%
  • finance:ap$0.011718%
  • tenant:acme$0.009414%
  • pipeline:orders$0.00497%
ACTORACTIONvCPU·sTOKENSCOST
agent:ops-watchquery · checkout p955.0640$0.0031
tenant:acmedashboard · 42 tiles18.20$0.0094
finance:apagentflow · step 03 approval0.41,208$0.0117
pipeline:ordersingest · snapshot 01959.60$0.0049
analyst:priyanotebook · cohort join31.50$0.0162
agent:ops-watchagentflow · open incident0.82,034$0.0208
TOTAL$0.0661
AGENTFLOW · RUN 7F2ARUNNING IN YOUR VPC
Hold any vendor payment invoiced 5% above contract.
  1. Query open invoicesdone
  2. Flag 3 over thresholddone
  3. Approval from AP leadapproved
  4. Hold payments in ERPdone
4 tool calls1 approval3,812 tokens$0.042
ESTIMATE BEFORE RUN · ≤ $0.05within budget

Cost per workload and agent step

Queries and agent steps governed

Every step, tool call, and approval recorded

USE CASES

What teams run on e6data.

Far less spend. Far more output.

B2B SAAS · NASDAQ-LISTED

Customer-facing reporting

Analytics offered across basic to premium tiers, at 15M+ queries a day.

60–80%
lower TCO than the incumbent warehouses
2xfaster p95
$12M+saved over 3 years

No changes to data, governance, apps or queries.

GLOBAL MEDIA

Executive ad-hoc queries

Interactive reporting for executives and analysts.

200 → <10
minutes to insight
60%lower cost
$4M+saved over 3 years

1,000+ hours of analyst time back.

SECURITY ANALYTICS · FORTUNE 100

Real-time security analytics

Firewall customers moved to a cloud security data lake.

25 → 1,000+
QPS under 2s, a goal the previous engine missed
50%+lower cost
0%failed queries, vs. 34% before

8x faster at p50, 10x+ at p95.

FINTECH

Always-on security telemetry

900K events per second, through spikes above 5x.

<5¢
per GB, ingest, index and 30-day retention
1.2 PBa month
<15sevent to query-ready

Runs in their own account, on their own storage.

SECURITY OPERATIONS

SIEM policy simulation

Replay real traffic against detection rules before they ship.

7–30 days
of every event queryable, no sampling
<15sto query-ready
<5¢per GB

Query through SQL and Grafana.

BFSI · DATA RESIDENCY

Regulator-ready sovereign retention

100% of ICT logs captured, kept in-region, queried in place.

<$0.10
per GB, ingest and index
Zerocold-archive rehydration
180 daysretained in-region

Illustrative sizing for a public-sector bank.

QUERY ENGINE · SIX ENTERPRISES
Subscription management leader15x faster1,000+ QPS80% lower TCO
Global networking giant9x faster125+ QPS80% lower TCO
Global customer engagement platform6x faster175+ QPS60% lower TCO
Leading market research firm5x faster300+ QPS60% lower TCO
Iconic global media brand3x faster50% lower TCO
World's largest quick-commerce firm2x faster50% lower TCO
AI-READY WITHOUT REPLATFORMING

Go surgical, or go end to end.

Open standards let you upgrade under the hood, fast. You pick the scope.

  1. 01SurgicalClose one acute gap in cost or performance pain. Prove ROI in weeks, repeat.
  2. 02End to endSolve a whole business problem on the full stack, from ingest to action with governed agents.
Book a demo

Questions

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. Total cost of ownership came in up to 50% lower on those workloads, because compute is sized to each query instead of over-provisioned. We commit to a cost target in writing.

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.

Why do agents break warehouse economics?

Warehouses are priced for people. We bill per vCPU.

Does our data leave our environment?

No. It stays in your perimeter.

How do we know what an agent will cost?

You see cost per run before it runs.

Who is it built for?

Data engineers, security operations, supply chain, FinOps, business analytics, and SaaS analytics teams.

STOP WAITING. START ACTING.

See your numbers.

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

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