Milvus and Vespa represent two distinct approaches to vector search infrastructure. Milvus is a dedicated vector database that does one thing exceptionally well: storing and searching high-dimensional embeddings at massive scale. Its Global Index, cloud-native architecture, and tiered deployment options from pip-installable Milvus Lite to enterprise-grade distributed clusters make it the fastest path from prototype to production for vector search workloads. Vespa is a comprehensive AI search platform that integrates vector search, full-text search, structured data operations, and distributed machine-learned ranking into a single serving system. Organizations like Spotify, Yahoo, and Perplexity run production workloads on Vespa because it eliminates the need to stitch together separate search, ranking, and inference components. The right choice depends on whether you need a specialized vector database or a unified platform for building complete AI-powered search and recommendation applications.
| Feature | Milvus | Vespa |
|---|---|---|
| Primary Focus | Purpose-built vector database for embedding similarity search and GenAI applications | Full AI search platform combining vector search, text search, and machine-learned ranking |
| Search Capabilities | Vector similarity search with metadata filtering, hybrid search, and multi-vector support | Vector search, true positional text indexes, structured data search, and hybrid combinations |
| Ranking Approach | Global Index for fast approximate nearest neighbor search across billions of vectors | Distributed machine-learned model inference with ONNX and XGBoost support for multi-phase ranking |
| Deployment Options | Milvus Lite (pip install), Standalone (single machine), Distributed (enterprise), Zilliz Cloud (managed) | Self-hosted open source, Vespa Cloud (managed), Vespa Cloud Enclave (managed in customer VPC) |
| Pricing Model | Contact for pricing | Community Edition free (self-hosted), Cloud pricing available on cloud.vespa.ai/pricing |
| Best For | GenAI developers needing a dedicated vector database that scales from prototyping to billions of vectors | Teams building applications that need combined search, ranking, recommendation, and real-time inference at scale |
| Metric | Milvus | Vespa |
|---|---|---|
| GitHub stars | — | 7.0k |
| PyPI weekly downloads | 1.3M | 2.4M |
| Docker Hub pulls | 76.8M | 14.5M |
| Search interest | 3 | 0 |
As of 2026-06-22 — updated weekly.
| Feature | Milvus | Vespa |
|---|---|---|
| Search Capabilities | ||
| Vector Similarity Search | Core strength with Global Index for blazing fast approximate nearest neighbor search at scale | Full vector and tensor search with any number of vector fields, indexed or unindexed |
| Text Search | Not a primary capability; focused on vector operations | True positional text indexes with BM25, proximity matching, and configurable linguistics |
| Hybrid Search | Supports hybrid search combining vector similarity with metadata filtering | Boolean combinations of vector, text, and structured operators with data-aware query planning |
| Structured Data Search | Metadata filtering on structured fields alongside vector search | Full structured data support with arrays, maps, structs, exact match, ranges, fuzzy, and regex |
| Ranking & Relevance | ||
| Ranking Model Support | Vector distance-based ranking with configurable similarity metrics | Distributed ML model inference with ONNX and XGBoost support in first and second ranking phases |
| Multi-Phase Ranking | Single-phase vector similarity ranking | Three ranking phases: local first-phase, local second-phase, and distributed third-phase |
| Custom Rank Profiles | Configurable distance metrics for similarity search | Multiple rank profiles per application with inheritance, function calling, and per-query selection |
| Scalability & Performance | ||
| Horizontal Scaling | Distributed architecture supporting tens of billions of vectors with minimal performance loss | Infinite automated scalability with automatic data distribution and background rebalancing |
| Write Performance | Cloud-native stateless design for elastic scaling of write operations | Sustained write handling with stable query performance during continuous data updates |
| Query Latency | Blazing fast retrieval with Global Index regardless of dataset scale | Sub-100ms latency at thousands of queries per second across billions of data items |
| Deployment & Operations | ||
| Self-Hosted Deployment | Milvus Lite (pip install), Standalone (single machine), and Distributed (enterprise clusters) | Open-source self-hosted with Apache-2.0 license; manual upgrades and security management |
| Managed Cloud Service | Zilliz Cloud with serverless and dedicated cluster options including SaaS and BYOC | Vespa Cloud with fully managed operations, automatic upgrades, and Enclave mode for customer VPCs |
| Continuous Deployment | Standard deployment workflows via Zilliz Cloud | Built-in safe continuous deployment with automated platform updates four times per week |
| Use Case Support | ||
| RAG Applications | Primary use case with guided notebooks and quickstart tutorials for RAG development | Deep RAG support with hybrid search, relevance models, and multi-vector representations |
| Recommendation Systems | Supports recommendation via embedding similarity search | Purpose-built recommendation and personalization with ML model evaluation at any scale |
Vector Similarity Search
Text Search
Hybrid Search
Structured Data Search
Ranking Model Support
Multi-Phase Ranking
Custom Rank Profiles
Horizontal Scaling
Write Performance
Query Latency
Self-Hosted Deployment
Managed Cloud Service
Continuous Deployment
RAG Applications
Recommendation Systems
Milvus and Vespa represent two distinct approaches to vector search infrastructure. Milvus is a dedicated vector database that does one thing exceptionally well: storing and searching high-dimensional embeddings at massive scale. Its Global Index, cloud-native architecture, and tiered deployment options from pip-installable Milvus Lite to enterprise-grade distributed clusters make it the fastest path from prototype to production for vector search workloads. Vespa is a comprehensive AI search platform that integrates vector search, full-text search, structured data operations, and distributed machine-learned ranking into a single serving system. Organizations like Spotify, Yahoo, and Perplexity run production workloads on Vespa because it eliminates the need to stitch together separate search, ranking, and inference components. The right choice depends on whether you need a specialized vector database or a unified platform for building complete AI-powered search and recommendation applications.
This verdict is based on general use cases. Your specific requirements, existing tech stack, and team expertise should guide your final decision.
Milvus is a purpose-built vector database designed specifically for embedding similarity search and GenAI applications. It focuses on storing and querying high-dimensional vectors at massive scale. Vespa is a broader AI search platform that combines vector search with text search, machine-learned ranking, and real-time inference in a single system. Milvus excels when your primary need is fast, scalable vector similarity search. Vespa is the stronger choice when you need to combine multiple search modalities with complex ML-powered ranking in one application.
Both platforms support RAG workloads, but they approach it differently. Milvus provides a straightforward vector storage and retrieval layer that integrates with popular AI development tools, with guided notebooks for building RAG applications quickly. Vespa offers a more comprehensive RAG stack with hybrid search combining vector similarity and text relevance, multi-vector representations for chunked documents, and distributed ML model inference for re-ranking results. For simple RAG pipelines, Milvus gets you started faster. For production RAG systems requiring sophisticated relevance tuning, Vespa provides more built-in capabilities.
Both platforms are open source for self-hosting. Milvus uses an enterprise pricing model for its managed service (Zilliz Cloud), requiring you to contact sales for pricing details. Zilliz Cloud offers both serverless and dedicated cluster options with SaaS and BYOC deployment. Vespa's Community Edition is free for self-hosting under the Apache-2.0 license. Vespa Cloud's managed service pricing is available on cloud.vespa.ai/pricing, with options for standard managed deployment and an Enclave mode that runs inside your own AWS or GCP account.
Milvus is primarily designed for vector operations rather than traditional text search. It supports metadata filtering and hybrid search that combines vector similarity with structured field filtering, but it does not include a native text search engine. Vespa includes true positional text indexes with BM25, term proximity matching, configurable linguistics with stemming and token normalization across many languages, and CJK segmentation. If your application requires both vector and full-text search, Vespa delivers both natively in a single platform.
Both platforms are designed for large-scale production use. Milvus supports tens of billions of vectors with minimal performance loss through its distributed architecture and cloud-native stateless design. Vespa handles billions of constantly changing data items with sub-100ms latency at thousands of queries per second. Vespa's architecture scales in two dimensions: horizontally for more data and with node groups for more traffic, with automatic data distribution. The choice depends on workload type rather than raw scale. Milvus optimizes for vector search throughput, while Vespa optimizes for complex queries combining multiple search modalities with ML-powered ranking.