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Vector Data Migration

2 posts · Apr 2025 – Feb 2026

What this trail covers

Qdrant continues to be a core component for AI applications, with a focus on reliability, performance, and cost-efficiency. This post details the launch of the beta Qdrant Vector Data Migration Tool, designed to simplify moving data between different instances, whether migrating between Qdrant deployments or switching from other vector database providers. The tool streams all vectors from a source collection to a target Qdrant instance in live batches, supporting migrations from open source to Qdrant Hybrid Cloud. Dust's experience highlights the challenges of scaling with per-collection architectures and the benefits of consolidating into multi-tenant collections with Qdrant, leveraging features like scalar quantization for significant performance and cost improvements. They also emphasize the importance of pairing vector search with structured query capabilities and plan for future enhancements like multi-region sharding and support for new embedding models.

All Qdrant trails
  1. How Bazaarvoice scaled AI-powered product insights with Qdrant - QdrantBazaarvoice details their successful migration of billions of vectors from PostgreSQL with pgvector to Qdrant. Key technical challenges and solutions included: scaling to billions of embeddings with cost-efficiency, implementing tenant isolation via payload-based partitioning for scoped searches, and leveraging Qdrant's quantization for significant storage and RAM reduction (~100x). The migration was performed under real-world constraints with streaming data ingestion and disk-backed collections, still achieving sub-100ms query latency and ~98% recall. This migration enabled new product development by removing operational overhead of manual partitioning and enabling flexible, cross-product queries at query time.
  2. How Fieldy AI Achieved Reliable AI Memory with Qdrant - QdrantThis post details Fieldy AI's migration from Weaviate to Qdrant to address reliability issues (5xx errors) and achieve fault tolerance for their AI memory platform. Key technical contributions include: reusing existing vector schema, batching embeddings locally before ingestion, disabling indexing during bulk import for throughput optimization, using Qdrant's gRPC interface, and implementing hybrid search with BM25 and Reciprocal Rank Fusion (RRF) for relevance scoring. The migration resulted in 100% reliable recall, reduced latency, eliminated network errors, and a two-thirds reduction in infrastructure costs, enabling scalability to tens of millions of embeddings with minimal operational intervention.
  3. How &AI scaled global legal retrieval with Qdrant - QdrantThis post details how &AI scaled global legal retrieval with Qdrant, handling hundreds of millions of documents and over 1 billion vectors. It highlights their use of Qdrant Cloud for managed infrastructure, scalar quantization for retrieval speed and accuracy, and multi-tenancy for data privacy. The article emphasizes a retrieval-first approach to legal AI, grounding results in real documents to minimize hallucination risk. It also touches on the challenges of processing noisy PDFs and standardizing data across jurisdictions.
  4. How Alhena AI unified its AI stack and improved ecommerce conversions with Qdrant - QdrantThis post details Alhena AI's successful migration from a fragmented vector search infrastructure (FAISS, Pinecone, Weaviate) to Qdrant Cloud. Key technical contributions include: replacing multiple vector search backends with a single unified solution, achieving up to 90% reduction in retrieval latency, enabling hybrid search with dense and sparse embeddings, implementing metadata filtering and real-time boosting for improved relevance and business alignment, leveraging Qdrant's multitenancy to manage hundreds of thousands of customers with a few collections, and simplifying global deployment through Qdrant Cloud's managed regional clusters. The post highlights the business impact of these technical improvements, such as faster customer onboarding, improved conversion rates, and predictable margins.
  5. Vector Data Migration Tool - QdrantIntroduces the beta Qdrant Vector Data Migration Tool, a streaming batch migration utility for moving data between Qdrant instances (including open source to Cloud, and between regions) and from other vector databases. The tool operates via a single command, can be run as a container, and does not require direct connectivity between source and target. It allows for changing collection configurations on the target and is presented as an alternative to snapshot migration.
  7. How Dust Scaled to 5,000+ Data Sources with Qdrant - QdrantThis post details how Dust scaled their AI infrastructure to over 5,000 data sources by migrating from a per-collection architecture to a consolidated, multi-tenant approach using Qdrant. Key technical contributions include: implementing scalar quantization to reduce memory footprint and improve latency (from 5-10 seconds to under 1 second), managing millions of vector upsert/delete operations, building a custom `DustQdrantClient` for abstraction, and deploying Qdrant in a multi-region GCP setup with secure authentication. The post highlights the performance gains (collections reduced from 30GB+ RAM to a quarter), cost savings, and improved user experience achieved through Qdrant's architecture, sharding, and payload filtering. It also touches on the strategic decision to pair Qdrant with a text-to-SQL system for hybrid querying.