AI / ML Platform Teams
One platform for features, embeddings, and inference cache.
Replace Feast, Pinecone, and Redis with a single GPU-node-resident representation. Serve ML features and vector queries at memory bandwidth with zero copies.
The problem
ML platforms run on fragmented data.
A typical ML platform copies the same embeddings into a feature store for training, a vector database for retrieval, a cache for inference, and an analytics store for monitoring. Each copy drifts, each service fails independently, and the aggregate cost is 5–10x what the GPU compute itself costs.
Feature-serving latency
Feast or Tecton adds 10–50ms to every inference call. At scale, that's the bottleneck.
Embedding drift
Vectors in Pinecone diverge from features in Feast. Reindexing takes hours.
Cost explosion
Separate contracts for vector DB, feature store, inference cache, and search. Aggregate cost is 5–10x what the GPU compute itself costs.
Ops burden
Three services, three APIs, three failure modes, three on-call rotations.
The solution
Features, vectors, and cache from one API.
20.4 ms
p50 feature-serving latency
On 100M entries. Benchmarked on a single GPU node.
100%
Vector recall
Exact recall. No approximation, no re-ranking needed.
23.3x
Compression ratio (fp32)
132 bytes per entry. 500M entries per GPU.
3
Services eliminated
Feature store, vector database, and inference cache. Replaced by one.
Use cases
What AI/ML teams use HX-SDP for.
Real-time feature serving
Serve ML features at sub-millisecond latency. 3,493 entities/s ingest, instant query at 10M entries.
Embedding retrieval (RAG)
100M vectors on a single GPU node at 100% exact recall on the QTT-Native fp32 path. Drop Pinecone.
KV cache offload
LLM KV caches stored in structural form. Sub-millisecond retrieval from VRAM. Drop Redis.
Training data management
One representation for training and serving. No ETL between feature store and training pipeline.
Model monitoring
Query drift and distribution metrics directly from the structural store. No analytics copy.
Multi-model serving
Share one data store across multiple models. Each gets its own view without a separate copy.
Access
One platform. Three precision tiers.
Choose the tier by the work: fp32 for most production ML, fp16 for high-volume embeddings, fp64 when the answer has to be exact. Scoping, tier fit, and cost are sized to the workload, not the seat.
Contact sales
Workload-scoped engagements
Tell us your embedding scale, feature count, and latency requirements. We'll recommend a tier, estimate savings against your current stack, and outline the evaluation path. Request access →
Talk to our ML infrastructure team.
Describe your embedding scale, feature count, and latency requirements. We'll recommend a tier and estimate savings.