Vespa alternatives

3 tools to consider instead of Vespa, shown against it.

Vespa Milvus Weaviate Qdrant
Vendor Vespa.ai (originally Yahoo) LF AI & Data Foundation (originally Zilliz) Weaviate B.V. Qdrant Solutions GmbH
Pricing model Free tier + paid plans Open source + paid options Free tier + paid plans Free tier + paid plans
Free tier Yes Yes Yes Yes
Deployment Cloud, Self-hosted Self-hosted Cloud, Self-hosted Cloud, Self-hosted
Open source Yes (Apache-2.0) Yes (Apache-2.0) Yes (BSD-3-Clause) Yes (Apache-2.0)
Best for Teams building search or recommendation products that need custom relevance ranking, not just similarity scores. Teams needing self-hosted vector search at very large scale with full control over infrastructure. Teams wanting hybrid semantic + keyword search with the flexibility to self-host or go managed. Teams needing efficient, filterable vector search with the option to self-host at no license cost.
Pricing

Self-hosted is free; Vespa Cloud offers a free trial tier with usage-based paid plans, though the pricing page did not publish specific dollar tiers.

Pricing has not been verified yet — see the vendor's site.

Free and open source under the Linux Foundation; a fully managed cloud version is sold separately by Zilliz as Zilliz Cloud.

Pricing has not been verified yet — see the vendor's site.

Self-hosted is free; Weaviate Cloud has a free tier, a pay-as-you-go Flex plan, and prepaid Premium plans for shared or dedicated deployments.

Free $0/month
Flex From $45/month
Premium (Shared/Dedicated) From $400/month

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Self-hosted is free; Qdrant Cloud has a free-forever tier, usage-based Standard pricing, and minimum-spend Premium and Hybrid/Private Cloud options.

Free Free forever
Standard Usage-based (rates via calculator)
Premium Minimum spend required (rates via calculator)

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features
  • Combined vector, keyword and structured search in one query
  • Multi-phase machine-learned ranking
  • Horizontally scalable distributed architecture
  • Real-time writes with large-scale concurrent reads
  • Tensor computation for custom ranking models
  • Managed Vespa Cloud option
  • Billion-scale approximate nearest-neighbor search
  • Multiple ANN index types (HNSW, IVF, DiskANN)
  • Hybrid dense + sparse vector search
  • Storage-compute separation in cluster mode
  • Milvus Lite embedded mode for local development
  • Scalar filtering combined with vector search
  • Native hybrid search (vector + BM25 keyword)
  • Built-in embedding generation modules
  • GraphQL and REST query APIs
  • Self-hosted (Docker/Kubernetes) or managed cloud deployment
  • Multi-tenancy for SaaS applications
  • Generative search (RAG) module integration
  • Approximate nearest-neighbor search with payload filtering
  • Rust-based engine for high performance and low memory use
  • Vector quantization for reduced memory footprint
  • gRPC and REST APIs
  • Self-hosted or managed cloud, including hybrid/private cloud
  • Sharding and replication for horizontal scale

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