Agents create query volume dashboards never did
Concurrency spikes unpredictably as agents fan out into many parallel queries.
USE CASE
Scale your querying agent
Agents query in bursts and in parallel, a concurrency pattern that step-jump engines price badly. e6data was built for the concurrency agents demand, scaling by the vCPU on your open tables.
IN PRODUCTION
1,000+QPS at p95 < 2s
1 vCPUper scaling increment
PROBLEMS THIS SOLVES
The problems this solves
Latency degrades under that concurrency
Legacy engines trade throughput for latency; agentic analytics needs both at once.
Cost scales with every new agent
Per-cluster pricing punishes exactly the growth you are trying to enable.
Bursts scale out, not into a queue
Agent orchestrators, RAG apps, and tools keep their interfaces. e6data replaces the engine underneath, so parallel calls fan out instead of waiting on a coordinator.
agent orchestrator
W/O E6DATA
RAG apptools & functionsAPIsCOORDINATOR QUEUEany governance · any catalogany table formatany cloud · on-prem · hybrid
agent orchestrator
W/ E6DATA
RAG apptools & functionsAPIse6dataNO COORDINATOR · FANS OUT BY 1 VCPUany governance · any catalogany table formatany cloud · on-prem · hybrid
Every agent call runs on compute sized in 1-vCPU increments, so idle agents cost nothing between bursts.
RUNS WITH YOUR DATA STACK
No custom glue code needed
LAKEHOUSE
Queries directly, with zero data movement.
TABLE FORMAT
Iceberg, Delta, and Hudi, at full performance.
CATALOG
Plugs into any catalog, no rule rewrites.
APPLICATION
Connects to any BI, RAG app, or agent.
GOVERNANCE
Inherits your existing controls and policies.
Concurrency the way agents actually query
proof: 1,000+ QPS with p95 under 2s
Concurrency without a coordinator
No central driver to bottleneck, so thousands of parallel agent calls scale out instead of queueing behind each other.
proof: 1,000+ QPS with p95 under 2s
proof: scales in 1-vCPU increments
Per-call economics
Each burst runs on compute sized in 1-vCPU increments, so idle agents cost nothing between calls and spikes do not over-provision.
proof: scales in 1-vCPU increments
proof: zero data movement
Vector search on your tables
Semantic search on unstructured data with cosine similarity, on the same open tables. No separate vector store to sync.
proof: zero data movement
RESULTS
Built for agentic concurrency
p95 < 2sunder high concurrencyLatency holds flat as agent traffic climbs, so answers stay fast enough to act on.
1,000+QPS sustainedThe engine serves the query volume a fleet of agents generates, not just human clicks.
1 vCPUscaling incrementCompute grows and shrinks with demand, so you pay only for the concurrency in use.
POWERED BY · QUERY ENGINE
This use case runs on the e6data Query engine
Kubernetes-native SQL and AI analytics on your open tables: 1,000+ QPS at p95 under 2s, scaling by the vCPU with no coordinator single point of failure.
Questions your team will ask
Plain answers for the evaluator in the room.
Why do agents strain a normal engine?
Agents fire many small queries in parallel bursts. Step-jump engines answer a burst by doubling the whole cluster, so cost climbs far faster than load. e6data fans out in 1-vCPU increments instead.
Do I need a separate vector database?
No. Vector search runs on the same engine and the same open tables with cosine similarity, so there is no second store to sync or govern.
Will it run in my environment?
Any cloud, your VPC, on-prem, hybrid, air-gapped, or sovereign. Agents connect through JDBC, the Python connector, or your existing APIs.
TALK TO US
Book a demo on your own 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.
Problems we’re solvingThanks.
An actual person replies, usually within one working day.