Vector Database Replication
Made Simple

Traditional data replication tools treat vectors as generic binary data. They can move bytes, but they can't preserve the semantic relationships that make embeddings work. Pipelyt understands vector data—we validate that cosine similarity stays consistent, handle dimension mismatches, and ensure your search results remain identical across replicas. Plus, we support true bi-directional sync between any vector databases, not just one-way data loading.

We support all major vector databases including Pinecone, Chroma, Weaviate, Qdrant, Milvus, Zilliz Cloud, pgvector, and more to come. Our architecture is designed to add new connectors rapidly, if you need a specific database, we can usually add support within weeks.

This is where our vector-specific intelligence shines. We automatically detect dimension mismatches (e.g., 384D → 1536D) and apply appropriate normalization techniques like padding, truncation, or linear transformation. We also handle value range conversion (0-1 → -1 to 1) and validate that distance metrics remain consistent post-transformation.

Sub-second latency for most operations. Simple vector updates typically replicate within 100-500ms. Large batch operations or full index rebuilds may take longer depending on size, but we provide real-time progress monitoring and can parallelize transfers for maximum speed.

Absolutely. One of our key advantages is one-to-many replication. You can sync a single source to multiple vector databases simultaneously—perfect for multi-cloud DR, A/B testing, or maintaining dev/staging environments.

We implement exactly-once delivery guarantees and continuous semantic validation. Every replicated vector is verified to ensure search results match the source. We also provide conflict resolution for multi-master scenarios and real-time monitoring to detect any drift.

Both! We excel at real-time continuous replication (CDC) for keeping systems in sync, but we also support one-time migrations when you're switching vendors. You can even start with a migration and then switch to ongoing replication.

We automatically map metadata schemas between different vector databases. Custom fields, namespaces, collections, and indexes are preserved wherever possible. When exact mapping isn't possible, we provide configurable transformation rules.

Building production grade vector replication requires expertise in distributed systems, vector mathematics, database internals, and ongoing maintenance. Our customers typically save 6-18 months of engineering time and can focus on their core AI features instead of data plumbing.