HolonomiX
Your enterprise stores the same data up to nine times. It only needs to store it once.
Walk through how modern infrastructure actually works, see what changes, and review the feature-by-feature audit that proves it.
This is how a typical enterprise data stack looks today. 13 separate services, each storing its own copy of the data in its own format. Every box is a vendor, a bill, and a team maintaining it. The same information passes through all of them because no single format could serve every need.
Ingest$18K / month
Replaced
🚪
API Gateway
The front door. Receives every request, webhook, and external call coming into your system.
$3,000 / mo
Replaced
📨
Event Stream
A conveyor belt for data. Every event gets queued up and sent to whichever systems need it.
$8,000 / mo
Replaced
🔄
ETL Pipeline
Takes raw data and reshapes it into formats each downstream system can understand.
$7,000 / mo
↓
Stream Retention$12K / month
Replaced
💾
Kafka Retention
Holds a copy of every event for 7 to 30 days "just in case." At 500 GB/day, that is 7 TB of data sitting idle.
$12,000 / mo
↓
Serving Layer$51K / month
Replaced
⚡
Cache (Redis)
Keeps a fast copy of popular data in memory so applications do not have to wait for the database.
$15,000 / mo
Replaced
🧭
Vector Database
Stores the same data in yet another format, optimized for "find me something similar" queries.
$12,000 / mo
Replaced
📊
Feature Store
Restructures the same data again so machine learning models can read it. Another copy, another bill.
$9,000 / mo
Replaced
🔍
Search Index
The same data, indexed one more time so people can do keyword searches against it.
$15,000 / mo
HolonomiX
One representation. Every access pattern.
Data is stored once in a structural format that natively serves every access pattern: caching, similarity search, feature serving, full-text search, event streaming, and ingestion. Eight separate services become one.
API GatewayEvent StreamETL PipelineKafka RetentionRedis CacheVector DBFeature StoreSearch Index
↓
Compute$45K / month
Improved
💻
GPU Cluster
The engine. Loads data into GPU memory for processing. Currently fills 75 GB of VRAM with uncompressed data.
$40,000 / mo
Improved
📤
KV Cache Offload
When GPU memory fills up, data overflows to slower system memory. A bottleneck caused by data size.
$5,000 / mo
↓
Results & Training$13K / month
Improved
📈
Observability
Logs, metrics, and monitoring. The volume of data written here scales with the size of everything upstream.
$5,000 / mo
Improved
🧠
Training Pipeline
Reads data from storage, produces new model weights, then syncs updated data back to every service above.
$8,000 / mo
↓
Archive$10K / month
Improved
🌊
Data Lake
Long-term storage. 200 TB of uncompressed data sitting in the cloud, billed by the terabyte.
$7,000 / mo
Improved
🛡️
Backup & DR
Disaster recovery. A full copy of the 200 TB data lake, replicated to another data center.
$3,000 / mo
Services
13
↓
5
Copies of data
9
↓
1
Monthly cost
$149K
↓
~$20K
Total storage
200 TB
↓
~1.3 TB
Ready to evaluate?
Request access for a technical evaluation, pilot scoping, or diligence review.