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Replace Your Analytics Cluster With One GPU Server

Cluster performance. Single-node simplicity.

10x Faster
8x Cheaper
Standard PostgreSQL
Get Early Access See the Numbers
Data-flow diagram comparing traditional CPU-bottlenecked path vs cupug GPU-direct I/O

The $50B Cluster Problem

10B+ Row Tables Now Common

Traditional systems can't handle enterprise analytical data without clustering.

$2M+ Annual Warehouse Costs

Distributed clusters require massive infrastructure spend and staff resources.

30%+ YoY Cost Growth

Cloud providers set the price hikes, not you.

The Hidden Cost: Operational Complexity

Distributed clusters don't just cost more upfront, they spiral out of control. Every node added multiplies failure modes. High availability requires 3x replication, dedicated SRE teams, and 24/7 on-call rotations. Network partitions, split-brain scenarios, and cross-node coordination become daily firefights. What starts as a "scalable solution" becomes a full-time job for expensive specialists, all to keep the cluster from falling over.

Ditch the Clusters Costs with cupug

PostgreSQL Extension

GPU-accelerated analytics that stays in the Postgres ecosystem. No migration, no retraining. Standard extension with no code forks.

GPU Direct Technology

GPU-direct storage access eliminates CPU bottlenecks. First to productize for Postgres.

Single-Node Scale

Performance that rivals multi-node clusters without the operational complexity.

DRAM IOPs for NVMe Costs

GPU-Direct storage fabrics can saturate NVMe systems, beating DRAM IOPs.

Painless Migration

Logical Replication

Stream your existing PostgreSQL data to cupug using native logical replication. Zero downtime, no application changes required.

Seamless Cutover

Connect your existing tools and applications to cupug after verification. Same PostgreSQL interface, dramatically better performance.

Decommission with Confidence

Once fully migrated, shut down your old cluster. No more distributed complexity, no more spiraling costs.

The Choice

Upgrade your cluster, multiply nodes, and watch costs spiral. Or migrate to cupug: fewer nodes, lower costs, better performance.

Migration choice illustration

TCO Comparison

Metric 8-Node CPU Cluster cupug (1 Node 2x B200) Advantage
CUDA Cores 1,024 33,792 33x
Memory Bandwidth 1,600 GB/s 16 TB/s (HBM3e) 10x
Node Interconnect 100-200 Gbps 1.8 TB/s (NVLink 5) 10x
Storage IOPs 1-2M 10-20M (10x NVMe) 10x
CPU CLUSTER ANNUAL TCO $400K-$550K

Compute + Storage + Operations

COST REDUCTION 8x

Better performance, fraction of cost

CUPUG ANNUAL TCO $55K-$65K

Single server + 2x B200 GPUs

How It Works

Traditional (CPU-Centric)

  • CPU orchestrates all storage I/O
  • Bulk reads and writes only
  • GPU idles waiting on CPU for data
  • Network shuffle between nodes dominates query time
CPU bounce buffer data path

cupug (GPU-Centric)

  • GPU direct storage I/O
  • Fine-grained, sparse reads. Fetch only bytes needed
  • Massive thread parallelism hides storage latency
  • No network shuffle. All data local on NVMe
GPU-direct storage access
Result: Fine-grained, on-demand data access with massive parallelism on random-access workloads

Storage Types

Row Storage

Row storage diagram

GPU-accelerated: OLTP, Joins, Row Operations

  • Use Standard heap tables from GPU
  • Full ACID transactions
  • Row-level joins and lookups
  • OLTP and mixed workloads

Column Storage

Columnar storage diagram

GPU-accelerated: Analytics, OLAP, Bulk Compute

  • GPU-accelerated columnar scans
  • GPU-direct NVMe reads
  • OLAP and data warehouse queries
  • Columnar compression (10–20x ratio)

Matrix Storage

Matrix storage diagram

GPU-accelerated: Matrix and Graph Workloads

  • Dense matrix operations via cuBLAS
  • Sparse matrix operations via cuSPARSE
  • Graph traversal via cuGraph
  • cuVS vector similarity search

Key Use Cases

  • Ad-hoc queries on 10B+ row tables
  • Massive-scale Vector Search and RAG
  • ML feature store with historical depth
  • Hybrid OLTP/OLAP workloads on a single server
  • Data-dependent queries without I/O amplification
  • Tick-level financial data and risk modeling
  • CDR and network telemetry analytics
  • Clickstream and recommendation pipelines
  • Genomic and clinical trial queries
  • IoT sensor telemetry and predictive maintenance

Target Customers

Financial Services

Tick data, risk modeling, real-time compliance

Robinhood, Revolut, BMO

Telecommunications

CDR analytics, network telemetry

Vonage, NTT, Twilio

E-commerce / AdTech

Clickstream, recommendations, ML features

Instagram, Reddit, Zalando

Life Sciences

Genomic queries, clinical trials

Tripal, LabxDB, MGI

IoT / Industrial

Sensor telemetry, predictive maintenance

Everactive, Titan America, Agoda

Logistics / Supply Chain

Route optimization, inventory forecasting, tracking

Amazon, Gojek, Flexport

Pricing Tiers

Cloud Hosted

Core
1-GPU Instance
Fully hosted
  • Full SQL analytics acceleration
  • Heap & column block storage
  • Email support
Enterprise
4-GPU Instance
Fully hosted
  • Multi-drive NVMe arrays
  • NVMe-oF fabric support
  • Commercial SLA & support
Managed
12-GPU Instance
Fully managed service
  • Dedicated infrastructure
  • Automated backups & monitoring
  • Priority SLA & onboarding

On Premises

Workstation
1-GPU Desktop
Your hardware, our software
  • Full SQL analytics acceleration
  • Heap & column block storage
  • Email support
Rack
4-GPU Server
Certified rack-mount hardware
  • Multi-drive NVMe arrays
  • NVMe-oF fabric support
  • Commercial SLA & support
Enterprise Rack
12-GPU Server
Certified rack-mount hardware
  • Dedicated on-prem infrastructure
  • Self-managed or supported
  • Priority SLA & onboarding

Get Early Access

Join the waitlist for the cupug beta.