// Kubernetes Operator · Apache Spark™

Spark at full speed.
Now with AI.

The Quanton Operator extends the standard Spark operator. 4× faster execution. No vendor lock-in. Deploy in minutes with Helm.

Create an account → View docs

quanton — ask me anything

$ helm upgrade --install quanton-operator \

oci://registry-1.docker.io/onehouseai/quanton-operator \

--namespace quanton-operator \

--create-namespace \

-f onehouse-values.yaml

✔ Release "quanton-operator" deployed

✔ SparkApplication CRD registered

✔ Quanton engine active

— Ask a question below (e.g. "how fast is it?") —

// Works with

// Features

⚡

Unmatched Speed

Vectorized execution and SIMD-accelerated columnar processing. Up to 4× faster than open-source Spark on TPC-DS benchmarks.

☸

Runs Anywhere

Kubernetes native drop-in CRD-based operator. Your existing Spark jobs work unchanged.

🧠

Adaptive Intelligence

Continuously analyzes your Spark workloads to diagnose issues, adapt to changing conditions, and recommend high-impact optimizations in real time.

// Benchmarks

open-source-spark │ quanton

TPC-DS 10TB · EKS · 32 vCPU

0.0×

Faster Execution

vs. open-source Spark on TPC-DS, TPCx-BB, TPC-DI, LakeLoader

Compute Cost Reduction

achieved by Fortune 500 companies in production

Petabytes

Data Processed Per Day in Production

with enterprise hardened Spark infrastructure

// AI co-pilot

AI assistance
for every Spark job.

Quanton comes with AI assistance for Spark, watching every job, diagnosing issues in real time, and guiding you from your first DataFrame to debugging large-scale production pipelines.

Real-time intelligence

Ingests logs, stage DAGs, executor metrics, task timelines, shuffle read/write stats, and JVM/GC data in real time.

Autonomous root-cause analysis

Identifies root causes for OOMs, fetch failures, skew, broadcast timeouts, and pipeline errors without manual debugging.

Continuous optimizations

Surfaces actionable optimizations including dynamic repartitioning, optimal executor sizing, memory tuning, and AQE configurations.

// Pricing

Radically fair
pricing.

You pay for the data you process, not the compute hours you don't use. No DBU markups. No idle waste. No bill shock.

cost = GB_processed × rate

THAT'S THE ENTIRE BILLING MODEL

Compute billing — 4× faster = 75% less revenue.

Per-GB billing — speed is free to give.

DBU compute hours × markup rate $84,291.00

Idle cluster time (nights & weekends) $7,200.00

Underutilized capacity util: ████████████░░░░░ 67% bill: ████████████████████ 100% $5,647.00

Mandatory support tier $4,200.00

Subtotal$101,338.00

AMOUNT DUE$101,338.00

OVERPRICED

DBU compute hours × markup rate $84,291.00

Idle cluster time (nights & weekends) $7,200.00

Underutilized capacity util: ████████████░░░░░ 67% bill: ████████████████████ 100% $5,647.00

Mandatory support tier $4,200.00

Subtotal$101,338.00

AMOUNT DUE$101,338.00

OVERPRICED

40,000 GB × $0.85 per GB processed $34,000.00

Idle cluster time $0.00

Underutilized capacity $0.00

Support included

Subtotal$34,000.00

AMOUNT DUE$34,000.00

YOU SAVE $67K

// Deploy anywhere

Spark everywhere
you already run it.

One engine. Any platform. Drop Quanton into your existing Spark stack in minutes — no migration, no rewrites, no lock-in.

$ helm repo add onehouse https://charts.onehouse.ai

$ helm upgrade --install quanton-operator onehouse/quanton-operator

✔ Quanton engine active — existing SparkApplication manifests unchanged

// Community

You're not alone
in this.

Built by the pioneers of the Lakehouse. We show up in Slack, merge PRs fast, and take Spark seriously.

💬

Talk to an engineer

Ask the team who built Quanton. Real engineers, real answers, no sales pitch.

Kenji 10:32 AM

Having trouble getting Quanton running on our EMR cluster — any tips?

Quanton Team Onehouse 10:33 AM

Hey Kenji! Drop your cluster config here — we'll have you running in 10 min

Join us on Slack →

⚡

Try it yourself

Skip the chit-chat. Spin up a cluster on your laptop and see the speed difference in under 10 minutes.

$ helm upgrade --install quanton-operator

✔ Done. Run your first Spark job.

⏱ ~4 minutes from zero to running

View on GitHub →

Ready to make Spark fast?

Deploy the Quanton Operator in under 10 minutes.

Create an account → Talk to us

// Customer impact

Click a card or use the arrows to explore.

✈️

Large US Airlines

EMR → Quanton on EKS

"We cut our Spark infrastructure bill in half and freed up millions of core-hours. The migration was drop-in — no job rewrites."

— VP of Data Engineering

60%

Cost savings vs EMR

3.8M

Core-hours saved/year

💳

Fintech Customer

EMR → Quanton on EKS

"Eight hundred thousand dollars in annual savings and our jobs run nearly twice as fast. It's not even close."

— Head of Platform Engineering

$800K

Annual savings vs EMR

45%

Faster Spark jobs

📡

Global Telecom

Databricks → Quanton on EKS

"10× the performance off Databricks, and we now hit 15-second ingest latencies we could never achieve before."

— Principal Data Architect

10×

Perf vs Databricks

15s

Ingest latency

🤖

AI Note Taking SaaS

EMR → Quanton on EKS

"Processing a petabyte a day at half the cost — Quanton made our real-time AI pipeline actually viable at scale."

— CTO

1PB

Processed per day

50%

Cost savings vs EMR

🏥

Healthcare Analytics Co.

DIY K8s → Quanton on EKS

"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua."

— Director of Data Platform

3.2×

Lorem ipsum metric

38%

Dolor sit amet

Spark at full speed.Now with AI.