Directory AI & ML PlatformsVector Databases & RAGVespa

Vespa

Open Source

About Vespa

Vespa is a platform for building search and AI applications that combines retrieval, ranking, machine learning inference and real-time updates. It stores and queries vectors, tensors, text and structured data. The Vespa engine is open source under the Apache 2.0 license. Vespa.ai is headquartered in Trondheim, Norway.

Products

Vespa: The open-source engine, which teams can build and run on their own infrastructure.
Vespa Cloud: A managed service for running Vespa applications, operated by the Vespa team.

Used for

Vector, text and structured search.
Retrieval for generative AI applications (RAG).
Recommendation, personalization and ad targeting.
E-commerce search and navigation over structured data, text and images.
Search over personal or private data, using a streaming search mode.

From Vespa's own website, read 2026-10-28. How we build listings.

How to evaluate Vector Databases & RAG

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Vespa. It comes from our Vector Database & AI Search buyer guide.

25%

Retrieval Quality & Index Options

ANN algorithms offered (HNSW, IVF/IVF-PQ, DiskANN, GPU/CAGRA), achievable recall@k at your latency budget, quantization for memory savings, and whether the index supports real-time inserts or needs rebuilds

20%

Hybrid Search & Filtering at Scale

Native vector+keyword (BM25 / sparse) fusion, reranking, and — critically — in-graph metadata pre-filtering that holds recall under selective filters rather than degrading to brute force or empty result sets

20%

Scale, Performance & Freshness

Vector ceiling per node and horizontally, p95/p99 latency and QPS under your real filters, multi-tenancy and namespaces, multi-region, and how fast new or updated embeddings become queryable (insert-to-searchable lag)

15%

Operating Model & Lock-In

Managed SaaS vs. self-hosted vs. bring-your-own-cloud, license terms (Apache 2.0 / BSD vs. proprietary), data-export and migration path, and how much sync/ETL you take on if vectors live apart from the source data

10%

Security, Governance & Residency

RBAC and API-key scoping, encryption in transit and at rest, SOC 2 / ISO 27001 / GDPR posture, data residency and VPC/air-gapped options, and inherited governance (e.g. Unity Catalog, platform IAM) where applicable

10%

Developer Experience & Ecosystem

Client SDKs and API ergonomics, LangChain / LlamaIndex / framework integrations, built-in or hosted embedding and reranking, docs quality, and operational maturity (backups, observability, upgrades)

Other Vector Databases & RAG Vendors

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