Scaling 200M+ Dense Embeddings with 2ms Search Latency and Zero Resharding Downtime
DeepSearch Labs powers semantic retrieval for over 120 e-commerce enterprises. As vector index sizes exploded past 400 GB in memory, PulseFlow AI provided automated dynamic index sharding and memory-mapped page warming.
The Challenge: In-Memory Index Bloat
HNSW graphs and IVF vector indexes demand vast amounts of RAM. Whenever node utilization crossed 85%, traditional Kubernetes pod evictions caused cascade restarts, taking index segments offline and causing severe API tail latency spikes.
The Solution: Non-Blocking Predictive Resharding
PulseFlow AI monitors memory pressure gradients across nodes, seamlessly carving out read-only snapshots and migrating vector partitions before memory exhaustion occurs:
- Zero-Lock Hot Partitions: Re-indexes happen on sidecar worker pods without locking queries on primary nodes.
- Tiered SSD Caching: Frequently retrieved centroid embeddings remain pinned in L1 VRAM, while colder vectors stream from ultra-fast NVMe storage.
"Before PulseFlow, resharding our vector indices required a scheduled 2 AM maintenance window and manual cluster balancing. Now it happens continuously with zero customer degradation."
Quick Facts
Scaling RAG or Vector Search?
Achieve lightning-fast vector similarity with automated shard balancing.