Decision comparison
Milvus vs Vespa
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.
Architecture choice. These take different approaches to the same problem. Read the table as a fit question rather than a feature race.
These are different kinds of product — Vector Database and Search Engine.
Quick Comparison
| Decision factor | 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 | Milvus is free and open source, and free to self-host. Fully managed Milvus is a separate product, Zilliz Cloud, which publishes its own serverless and dedicated tiers and quotes enterprise deployments. | 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 |
Milvus
- Primary Focus:
- Purpose-built vector database for embedding similarity search and GenAI applications
- Search Capabilities:
- Vector similarity search with metadata filtering, hybrid search, and multi-vector support
- Ranking Approach:
- Global Index for fast approximate nearest neighbor search across billions of vectors
- Deployment Options:
- Milvus Lite (pip install), Standalone (single machine), Distributed (enterprise), Zilliz Cloud (managed)
- Pricing Model:
- Milvus is free and open source, and free to self-host. Fully managed Milvus is a separate product, Zilliz Cloud, which publishes its own serverless and dedicated tiers and quotes enterprise deployments.
- Best For:
- GenAI developers needing a dedicated vector database that scales from prototyping to billions of vectors
Vespa
- Primary Focus:
- Full AI search platform combining vector search, text search, and machine-learned ranking
- Search Capabilities:
- Vector search, true positional text indexes, structured data search, and hybrid combinations
- Ranking Approach:
- Distributed machine-learned model inference with ONNX and XGBoost support for multi-phase ranking
- Deployment Options:
- Self-hosted open source, Vespa Cloud (managed), Vespa Cloud Enclave (managed in customer VPC)
- Pricing Model:
- Community Edition free (self-hosted), Cloud pricing available on cloud.vespa.ai/pricing
- Best For:
- Teams building applications that need combined search, ranking, recommendation, and real-time inference at scale
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | Milvus | Vespa |
|---|---|---|
| Docker Hub pulls(Product adoption) | 78.9M | 15.1M |
| GitHub commits, 90d(Product adoption) | 718 | 1.5k |
| GitHub stars(Product adoption) | 46,000+ | 7,000+ |
| Search interest(Market interest) | 2 | Unavailable |
| Hacker News mentions, 90d(Community interest) | 0 | Not available |
| npm weekly downloads(Developer adoption) | 133.5k | Not available |
| PyPI weekly downloads(Developer adoption) | 883.0k | 187.2k |
| Stack Overflow questions(Community interest) | 209 | 262 |
| Hugging Face downloads(Product adoption) | Not available | 180 |
| Hugging Face likes(Product adoption) | Not available | 107 |
As of September 21, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
Milvus
September 21, 2026Package vulnerabilities
npm · @zilliz/milvus2-sdk-node@3.0.6 · PyPI · pymilvus@3.0.2
0 vulnerabilities
across 2 packages
Repository security score
Not available
Vespa
September 21, 2026Package vulnerabilities
PyPI · pyvespa@1.2.6
0 vulnerabilities
across 1 package
Repository security score
Not available
Interface Preview
Milvus
Feature Comparison
| 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 |
Search Capabilities
Vector Similarity Search
Text Search
Hybrid Search
Structured Data Search
Ranking & Relevance
Ranking Model Support
Multi-Phase Ranking
Custom Rank Profiles
Scalability & Performance
Horizontal Scaling
Write Performance
Query Latency
Deployment & Operations
Self-Hosted Deployment
Managed Cloud Service
Continuous Deployment
Use Case Support
RAG Applications
Recommendation Systems
Which approach fits
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.
When each approach fits
Choose Milvus if:
Choose Milvus if your primary requirement is vector similarity search for GenAI applications. Milvus delivers blazing fast retrieval through its Global Index and scales elastically to tens of billions of vectors with minimal performance loss. Its tiered deployment model lets you start with a pip install for prototyping and graduate to fully managed Zilliz Cloud for production. The extensive developer community, guided notebooks for RAG, image search, and multimodal search, and integration with popular AI tools make Milvus the most accessible entry point for teams building embedding-powered applications.
Choose Vespa if:
Choose Vespa if your application needs more than vector search alone. Vespa combines vector similarity, true positional text search, structured data operations, and distributed ML model inference in a single platform. Its multi-phase ranking with ONNX and XGBoost support, streaming search for personal data at 20x reduced cost, and continuous deployment with automated platform updates make it a strong foundation for complex search, recommendation, and personalization applications. Teams at Spotify, Yahoo, Elicit, and Farfetch rely on Vespa for production workloads where search quality depends on combining multiple ranking signals.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
Frequently Asked Questions
What is the main difference between Milvus and Vespa?
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.
Which platform is better for RAG applications?
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.
How do the pricing models compare between Milvus and Vespa?
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.
Can Milvus handle text search like Vespa?
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.
Which platform scales better for production workloads?
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.