Vald

Free#26 of 27 in Vector DatabasesSearch DatabasesAi Databases

Vald: Open-source distributed ANN search for teams running vector infrastructure on Kubernetes. Ranked #26 of 27 in Vector Databases by our editors (6.8/10); pricing: Open source; best for kubernetes teams needing distributed ANN search.

6.8/10★★★☆☆Editor score

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At a glance

• Editor score
  6.8 / 10
• Pricing
  Open source
• Best for
  Kubernetes teams needing distributed ANN search
• Paid from
  Free
• Deployment
  Self-hosted
• Facts checked
  22 Sep 2026

• Where it wins

  • Automatic indexing, backups, replication, and horizontal scaling
  • Exact linear search alongside approximate nearest-neighbor search
  • gRPC and REST APIs with Go, Java, Python, and Node.js SDKs

• Where it doesn't

  • Requires a self-hosted Kubernetes deployment
  • No hybrid search or sparse-vector support
  • Metadata filtering uses configurable filter components rather than native query filters

Our verdict on Vald

Vald is an open-source, cloud-native vector search engine for approximate nearest-neighbor search over dense vectors. It runs as a self-hosted Kubernetes cluster and is aimed at teams that need distributed indexing, replication, and horizontal scaling across agent pods. Applications can insert, update, search, and remove vectors through gRPC or REST APIs, while official SDKs support Go, Java, Python, and Node.js.

Its strongest fit is a Kubernetes-centered platform ecosystem. Vald supports Helm, uses agent implementations based on NGT or Faiss, and can back up indexes to Amazon S3, Google Cloud Storage, object storage, or persistent volumes. Automatic indexing and distributed index replication reduce the need to manage a single index service, while exact linear search provides an alternative to ANN search for workloads that require it. These capabilities make Vald more suitable for infrastructure teams comfortable operating clustered services than for developers seeking a managed database with minimal deployment work.

Vald keeps a focused scope around distributed dense-vector retrieval. It includes ingress and egress filtering through filter components, but metadata filtering is implemented through that component model rather than documented as a native query filter. Hybrid search and sparse vectors are not part of its stated capabilities, which narrows its fit for applications combining keyword retrieval with dense embeddings or relying on sparse representations. Choose Vald when open-source control, Kubernetes deployment, and distributed ANN search are priorities; choose another vector database when managed hosting, native hybrid search, or sparse-vector workflows are central requirements.

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Vald pricing

Vald fact sheet

Alternatives to Vald

• ElasticsearchA broad hybrid-search platform for teams adding vectors to mature analytics stacks.9.3
• Vertex AI Vector SearchA managed Google Cloud option for teams building dense, sparse, and hybrid search.9.2
• VikingDBA cloud vector database with dense, sparse, hybrid, filtered, and multimodal retrieval.9.1

See all Vald alternatives →

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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

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Last updated 7 Oct 2026 · How we research and update