Quanton
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// Upto 4x Faster · Apache Spark™
Spark at full speed,<br>now with AI.
One engine that runs everywhere. Drop Quanton into your existing Spark stack in minutes — no migration, no rewrites. AI superpowers directly on Spark UI.
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K8s<br>AWS<br>GCP<br>Azure
quanton — ask me anything<br>!helm upgrade --install quanton-operator \<br>oci://registry-1.docker.io/onehouseai/quanton-operator \<br>--namespace quanton-operator \<br>--create-namespace \<br>-f onehouse-values.yaml<br>✔ Release "quanton-operator" deployed<br>✔ SparkApplication CRD registered<br>✔ Quanton engine active
— Ask anything (e.g. "how fast is it?")
quanton — eks setup<br>!helm upgrade --install quanton-operator \<br>oci://registry-1.docker.io/onehouseai/quanton-operator \<br>--namespace quanton-operator --create-namespace \<br>-f onehouse-values.yaml<br># Grant S3 access via IRSA<br>!kubectl annotate serviceaccount spark-operator-spark \<br>eks.amazonaws.com/role-arn=arn:aws:iam:::role/SparkS3Role<br>✔ Quanton active on EKS — S3 access via IRSA
— Helm-installed, S3 wired through IRSA. No code changes.
quanton — gke setup<br>!helm upgrade --install quanton-operator \<br>oci://registry-1.docker.io/onehouseai/quanton-operator \<br>--namespace quanton-operator --create-namespace \<br>-f onehouse-values.yaml<br># Grant GCS access via Workload Identity<br>!kubectl annotate serviceaccount spark-operator-spark \<br>iam.gke.io/gcp-service-account=spark-gcs@.iam.gserviceaccount.com<br>✔ Quanton active on GKE — GCS access via Workload Identity
— Helm-installed, GCS wired through Workload Identity.
quanton — aks setup<br>!helm upgrade --install quanton-operator \<br>oci://registry-1.docker.io/onehouseai/quanton-operator \<br>--namespace quanton-operator --create-namespace \<br>-f onehouse-values.yaml<br># Mount ADLS Gen2 credentials<br>!kubectl create secret generic adls-sp \<br>--from-literal=client-id= \<br>--from-literal=client-secret=<br>✔ Quanton active on AKS — ADLS Gen2 access configured
— Helm-installed, ADLS Gen2 wired via service-principal secret.
// Works with
// Features
Unmatched Speed<br>Faster with SIMD-vectorized execution. Smarter with storage-aware planning, incremental rewrites, and background indexing across your lakehouse.
Runs Anywhere<br>Run in the cloud or on-prem using our Kubernetes operator built on the kubeflow SparkApplication CRD. Or directly run it in your existing Spark platform.
🧠<br>AI Spark Engineer, embedded<br>Lives inside the Spark UI with live access to configs, runtime, heap, and GC. Backed by a knowledge server trained on a decade of Spark and lakehouse expertise.
// Benchmarks
open-source-spark │ quanton<br>TPC-DS 10TB · EKS · 32 vCPU
Rerun benchmark? (Y/N): Y ↺ rerun
0.0×<br>Faster Execution<br>vs. open-source Spark on TPC-DS, TPCx-BB, TPC-DI, LakeLoader
0%<br>Compute Cost Reduction<br>achieved by Fortune 500 companies in production
Petabytes<br>Data Processed Per Day in Production<br>on battle-tested Spark infrastructure
// AI co-pilot<br>AI assistance<br>for every Spark job.<br>Quanton AI watches every job, diagnosing issues in real time, and guiding you from your first DataFrame to debugging large-scale production pipelines.
Sees every signal, live<br>Streams logs, stage DAGs, executor metrics, task timelines, shuffle stats, and JVM/GC into a single live picture of your job.
Diagnoses, then prescribes<br>Pinpoints OOMs, skew, fetch failures, and broadcast timeouts — then recommends repartitioning, executor sizing, memory tuning, and AQE fixes that move the needle.
Skills built for Spark engineers<br>Dozens of tools for query plan analysis, SQL execution, JVM and PySpark debugging, and Open Table Formats storage tuning — the work you actually do.
Your keys. Your data.<br>Bring your Claude or OpenAI API key — stored in your browser. Prompts and AI data never touch our servers.
// Pricing<br>Radically fair<br>pricing.<br>Pay by GB processed, not by compute hours burnt — every speedup we ship cuts your infra bill while keeping your Quanton bill flat. Quanton runs in your own Kubernetes, so spot and reserved-instance savings stay yours, not ours — up to 70% on top.<br>cost = GB_processed × rate<br>THAT'S THE ENTIRE BILLING MODEL
Faster jobs = less money for vendor VENDOR REVENUE High Low Slow Fast JOB PERFORMANCE Compute billing — 4× faster = 75% less revenue.<br>Per-GB billing — speed is free to give.
* Estimates for illustrative purposes only. Actual costs vary by workload, usage, and vendor pricing.<br>THAT ONE RED BRICK CO.<br>INV-2024-0847 May 2026<br>40 TB processed per month<br>compute billing
Compute hours × DBU markup rate $96,291.00<br>Mandatory support tier $5,250.00
Subtotal$101,541.00
ANNUAL AMOUNT DUE$101,541.00<br>OVERPRICED
THAT ONE RED BRICK CO.<br>INV-2024-0847 May 2026<br>40 TB processed per month<br>compute billing
Compute hours × DBU markup rate $96,291.00<br>Mandatory support tier $5,250.00
Subtotal$101,541.00
ANNUAL AMOUNT DUE$101,541.00<br>OVERPRICED
QUANTON · ONEHOUSE<br>INV-2024-0847 May 2026<br>40 TB processed per month<br>per-GB billing
40,000 GB × markup rate $34,000.00<br>Support...