Vespa Vector Database
Vespa combines the flexibility of a vector database with the power of a full search and ranking engine. It delivers the fastest retrieval at scale, high-precision results, and real-time freshness across billions of documents in production.
Vespa turns complexity into clarity. It provides a single, unified engine that scales seamlessly, retrieves with intelligence, and ranks with precision. By addressing performance and scale challenges at the architectural level, Vespa delivers what traditional vector databases simply can’t.
Vespa delivers sub-millisecond latency at any scale by running vector search, filtering, scoring, and model inference where the data lives: on content nodes.
By eliminating network hops, you get fewer failure points, higher throughput, and dramatically lower latency. The result is a system that stays fast under pressure, supports higher QPS with fewer machines, and keeps your AI applications responsive as you grow.
New documents and embeddings become searchable immediately, even under heavy write traffic. Vespa Avoids the immutable-segment constraints that slow down other systems.
This gives you real-time freshness without downtime, predictable indexing performance, and the ability to support RAG pipelines, personalization, and recommendation systems that rely on constantly evolving data.
With intelligent partitioning, replication, and autoscaling, Vespa scales to billions of vectors without degrading performance or requiring system sprawl.
This architecture ensures linear scaling, efficient resource usage, and consistent sub-millisecond responses even as data volumes grow. You get the freedom to scale without re-architecting, rebalancing, or deploying multiple systems to fill functionality gaps.
Vespa brings all retrieval methods into a single, cohesive engine. You can combine dense embeddings, keyword signals, and metadata filters in one query, eliminating the need for multiple systems or external orchestration.
Vespa supports multiple embeddings per document, enabling use cases such as ColBERT or ColPali-style retrieval, image–text matching, and cross-modal search all within the same schema.
Vespa lets you deploy ranking models directly inside the serving layer using ONNX, XGBoost, or custom functions. You can perform first-phase recall with embeddings, then re-rank using machine learning models for maximum accuracy and explainability.
Beyond similarity search, Vespa supports structured filters, geospatial queries, and aggregations directly on vector fields. This allows you to combine semantic and business logic seamlessly.
Vespa stores and computes on tensors, not just vectors. This enables multi-dimensional representations that capture relationships between modalities such as text, images, and numeric data, supporting both dense and sparse features for hybrid search.
Unlike immutable-segment vector systems, Vespa supports continuous data ingestion and updates without costly index rebuilds. Applications stay fresh and responsive even under high write throughput.
Vector databases and legacy search engines each solve part of the problem of retrieval at scale, but neither can handle large-scale, hybrid, real-time retrieval on their own. Vespa brings it all together, vectors + tensors + inference + ranking, in one scalable, production-ready engine.
| Capability | Vespa | Lucene-Based Search (Elasticsearch, OpenSearch) | Standalone Vector DB (Qdrant, Weaviate, Pinecone) |
|---|---|---|---|
| Architecture | Unified engine for vector, text, and structured search. Built for low latency and large scale. | Text-first architecture with bolt-on vector support. Separate ANN indexes per segment. | Vector-only systems that require additional tools for text and filtering |
| Data Model | Tensor-native with dense, sparse, and multimodal data in one schema. | Flat vector fields added to documents; limited representation. | Vector-only schemas with basic metadata support. |
| Hybrid Retrieval (Vector + Text + Filters) | Vector, text, and filters in a single query with native hybrid ranking. | Hybrid search done through rescoring or post-filtering; slower and more complex. | Limited to simple metadata filtering; cannot combine with keyword scoring or symbolic logic effectively. |
| Ranking & ML Integration | Multi-phase ranking with ONNX/XGBoost models running inside the cluster. | Basic rescoring; heavy models require external services. | Minimal ranking; external rerankers needed. |
| Real-Time Updates | True real-time indexing with no rebuilds or refresh cycles. | Segment merges and rebuilds cause latency spikes. | Limited updates; often requires full re-embedding or re-indexing. |
| Multimodal Retrieval | Native support for text, image, audio, and other embeddings. | No native multimodal support. | Limited to embeddings; no cross-modal ranking or reasoning. |
| Explainability & Feature Control | Built-in ranking explainability and feature inspection. | Limited visibility into vector scoring. | Opaque similarity scoring with minimal transparency. |
| Operational Efficiency | One system replaces vector DB, keyword index, and reranking layer leading to lower ops and cost. | Multiple pipelines and indices increase operational complexity and overhead. | Requires orchestration with external systems for full retrieval pipeline. |
The report provides a detailed comparison of 17 leading open source and commercial solutions, examining their strengths across hybrid search, semantic retrieval, RAG, and large-scale AI workloads. It also highlights how vendors are integrating vectors, tensors, and other numerical representations to power next-generation AI applications.
A vector database is used in Retrieval-Augmented Generation (RAG) to store and retrieve embeddings, which are numerical representations of text that capture semantic meaning. This allows the system to find conceptually similar information rather than relying only on exact keyword matches, helping large language models ground their responses in relevant context.
While vector databases handle similarity search effectively, production-grade RAG systems also require hybrid retrieval that combines text and vector search, structured filtering, model-driven ranking, and real-time updates. Vespa unifies all of these capabilities in a single engine, enabling fast, transparent, and scalable retrieval for enterprise RAG applications.
Traditional search engines rely on keyword matching and text-based ranking, while vector databases use embeddings to understand meaning and context, returning results that are semantically similar even when the exact words differ. Vespa does both in a single engine, combining precise keyword and structured filtering with semantic vector retrieval so results are not only contextually relevant but also accurate and explainable.
Vespa goes beyond simple vector search by supporting tensors, multi-phase ranking, and hybrid retrieval. It can blend semantic, textual, and structured signals at scale, run machine learning models directly in the query path, and provide explainable, real-time results even for massive datasets.
Tensors are multi-dimensional data structures that generalize vectors and matrices, allowing AI systems to represent complex relationships and context across text, images, and structured data. In Vespa, tensors enable unified retrieval and ranking by combining vector, text, and structured signals within a single, flexible framework.
You need a vector database with tensor support when your AI applications require more than simple similarity search. Traditional vector databases handle one-dimensional embeddings, which work well for finding semantically similar items, but many real-world use cases such as RAG, personalization, or multimodal search depend on combining multiple signals like text relevance, metadata, and semantic similarity.
Tensors make this possible by representing data across several dimensions, allowing richer relationships and custom ranking formulas. A vector database with tensor support, such as Vespa, enables hybrid, explainable, and scalable AI retrieval that standard vector databases cannot provide.
Learn how Vespa delivers accurate, high-performance retrieval for GenAI agents at web scale.
Accelerate your path to production with a best-practice template that prioritizes retrieval quality, inference speed, and operational scale.
