Milvus vs. Pinecone vs. Zilliz Cloud
Semantic similarity searches using vectors are becoming increasingly popular for software developers seeking to build high-performing vector searches for AI or retrieval-augmented generation (RAG) applications in combination with large language models (LLMs). It’s essential to pick a vector database that can handle vector embeddings well.
Milvus is a widely used open-source vector database for scalability and performance in enterprise-level applications and is a popular option among developers. This page provides a comprehensive vector database comparison between Pinecone, Milvus, and Zilliz Cloud, a fully managed Milvus service offering enhanced features and convenience.
Milvus vs. Pinecone vs. Zilliz Cloud
What is Milvus?
Milvus is an open-source vector database engineered for high-performance and scalable vector search in GenAI applications. It is built on a distributed architecture and excels in vector similarity searches and complex query handling. Since its initial release in 2019, Milvus has garnered over 33K GitHub stars and has been adopted by large enterprises for various AI, RAG, and machine learning use cases.
What is the Pinecone vector database? Is Pinecone open source?
Pinecone is a managed vector database service for similarity search applications. The Pinecone vector database is not an open-source vector database but a closed, fully managed solution that offers a proprietary implementation optimized for easy-to-get-started experiences. Founded in 2020, Pinecone is privately owned and provides a range of enterprise features through its free and subscription plans.
What is Zilliz Cloud?
Developed by the original creators of Milvus, Zilliz Cloud is a cloud-native vector database service that brings advanced capabilities to the forefront. Zilliz has re-engineered Milvus to offer a fully managed solution with cutting-edge scalability, performance, and a rich set of developer tools. It includes comprehensive enterprise features designed to alleviate operational complexities, streamline development cycles, and provide seamless integration with existing systems. Supported on all major cloud platforms (AWS, GCP, Azure) and available in multiple regions (14 global regions), Zilliz Cloud ensures efficient, high-performance vector search. It also offers a free plan to get started and a transparent pricing page for further details.
At-a-glance: Milvus vs. Pinecone vs. Zilliz Cloud
Milvus, Zilliz Cloud, and Pinecone each offer unique approaches to vector database management and similarity search. While Milvus is an open-source solution engineered for high scalability and performance, Zilliz Cloud is a fully managed service built on Milvus, offering additional enterprise features and operational convenience. Pinecone distinguishes itself as a cloud-native, managed service with a proprietary implementation optimized for ease of use and quick start-up. These fundamental differences significantly influence their use cases, performance metrics, scalability, how they approach vector search, and their suitability for various enterprise needs. What are the critical differences between Milvus, Zilliz Cloud, and Pinecone?
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License | Open SourceUnder the Apache 2.0 License | Open SourceEnterprise license fully compatible with Milvus | Closed SourceOperates under proprietary licensing |
Infrastructure Responsibilities | Self-hostedInfrastructure operations and maintenance considerations owned between customer | Fully-managed SaaSAutomated and fully-managed clusters with minimal provisioning, scaling, or operational burdens. | Fully-managed SaaSAutomated and fully-managed clusters with minimal provisioning, scaling, or operational burdens. |
Scalability | Billion+ ScaleScale-out to a billion vectors with little performance degradation | Billion+ ScaleScale-out to 10 billion vectors with little performance degradation | Billion Scale with Performance CompromiseCapable of scaling up over a billion vectors, albeit with increased latency and reduced QPS |
Performance | Highly performant1.5X better performance than Pinecone on QPS | Further Enhanced Performance3X better performance on average than Pinecone on QPS and latency | Moderate PerformanceSufficient for organizations without high-performance requirements |
Pricing | Not ApplicableUser incurs hardware and hosting costs | Effectively Scaled, Usage-based PricingAverage 3x+ higher QP$ than Pinecone, and cost-effective pricing that adjusts with increased usage | Usage-based Pricing, best for small use casesLower QP$ and can become significantly expensive, particularly in high-concurrency use cases as usage scales. |
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License | Open SourceUnder the Apache 2.0 License |
Infrastructure Responsibilities | Self-hostedInfrastructure operations and maintenance considerations owned between customer |
Scalability | Billion+ ScaleScale-out to a billion vectors with little performance degradation |
Performance | Highly performant1.5X better performance than Pinecone on QPS |
Pricing | Not ApplicableUser incurs hardware and hosting costs |
| |
|---|---|
License | Open SourceEnterprise license fully compatible with Milvus |
Infrastructure Responsibilities | Fully-managed SaaSAutomated and fully-managed clusters with minimal provisioning, scaling, or operational burdens. |
Scalability | Billion+ ScaleScale-out to 10 billion vectors with little performance degradation |
Performance | Further Enhanced Performance3X better performance on average than Pinecone on QPS and latency |
Pricing | Effectively Scaled, Usage-based PricingAverage 3x+ higher QP$ than Pinecone, and cost-effective pricing that adjusts with increased usage |
| |
|---|---|
License | Closed SourceOperates under proprietary licensing |
Infrastructure Responsibilities | Fully-managed SaaSAutomated and fully-managed clusters with minimal provisioning, scaling, or operational burdens. |
Scalability | Billion Scale with Performance CompromiseCapable of scaling up over a billion vectors, albeit with increased latency and reduced QPS |
Performance | Moderate PerformanceSufficient for organizations without high-performance requirements |
Pricing | Usage-based Pricing, best for small use casesLower QP$ and can become significantly expensive, particularly in high-concurrency use cases as usage scales. |
Vector Database Performance Comparison Charts Milvus vs. Pinecone vs. Zilliz Cloud
Large-size datasets tested (≥5M vectors)
Dataset1
10,000,000 vectors with 768 dimensions
Dataset2
5,000,000 vectors with 1,536 dimensions
Products tested (with similar capabilities)
Milvus (16c64g-HNSW)
Milvus with 16 CPUs and 64G of RAM using HNSW index
Milvus (4c16g-disk)
Milvus with four CPUs and 16G of RAM using DISK_ANN index
Zilliz Cloud (8cu-perf)
Zilliz Cloud with eight performance-optimized compute units
Zilliz Cloud (2cu-cap)
Zilliz Cloud with two capacity-optimized compute units
Pinecone (p2.x1-8node)
Pinecone with one p2 (performance-optimized) pods and eight nodes
Pinecone (s1x1-2node)
Pinecone with one s1 (storage-optimized) pods and two nodes
- Pinecone pods and Zilliz compute units are pre-configured units of hardware for running vector storage, process, and search services.
- For more information about Zilliz Cloud’s compute units, see Zilliz blog introducing Zilliz Cloud CU type and size.
Results: QPS
Results: Latency
Results: QP$
Note: QP$ doesn’t apply to Milvus because it is an open source vector database.
Medium-size datasets tested (< 5M vectors)
Comprehensive benchmarking scores by VectorDBBench
Deep-dive: Zilliz Cloud vs. Pinecone
Developers, data scientists, and architects require a robust, cloud-native vector database service that emphasizes performance and operational efficiency. This entails delivering a fully managed vector store and search service with high scalability and performance, low operational burden, and enterprise-grade security features—all designed to handle complex vector searches and machine-learning tasks.
Vector Search & Management Capabilities
Index
AUTOINDEX
Automatically determine the most suitable configurations for searches and indexes
Proprietary Index
Static indexing algorithm to Pod bindings
Hybrid Search
Multi-vector + Hybrid Search
Enable more precise query results by allowing hybrid sparse & dense search, multimodal search, and vector search with scalar filtering
Sparse + Dense Vector Search
Offer nuanced similarity searches across sparse and dense embeddings but don’t support multimodal search
Index
AUTOINDEX
Automatically determine the most suitable configurations for searches and indexes
Hybrid Search
Multi-vector + Hybrid Search
Enable more precise query results by allowing hybrid sparse & dense search, multimodal search, and vector search with scalar filtering
Index
Proprietary Index
Static indexing algorithm to Pod bindings
Hybrid Search
Sparse + Dense Vector Search
Offer nuanced similarity searches across sparse and dense embeddings but don’t support multimodal search
Cloud Native Features and Performance
Separate Compute and Storage resources
Yes
Enable greater scalability and cost-efficiency for various workloads by separating compute and storage resources consumed, which is important for production applications
No
Resources cannot be independently adjusted to just the results that meet specific workload demands
Data Partitioning
Dynamic Segment Placement
Automatically redistribute data among various nodes or segments based on real-time usage patterns, index, query load, or other metrics.
Static Data Sharding
Divide data into shards based on predefined rules or keys, and these shards are distributed across different servers or clusters.
Separate Compute and Storage resources
Yes
Enable greater scalability and cost-efficiency for various workloads by separating compute and storage resources consumed, which is important for production applications
Data Partitioning
Dynamic Segment Placement
Automatically redistribute data among various nodes or segments based on real-time usage patterns, index, query load, or other metrics.
Separate Compute and Storage resources
No
Resources cannot be independently adjusted to just the results that meet specific workload demands
Data Partitioning
Static Data Sharding
Divide data into shards based on predefined rules or keys, and these shards are distributed across different servers or clusters.
Enterprise Production Readiness
Resiliency Guarantee
99.95% uptime SLA
99.9% uptime SLA
Monitoring
Built-in Metrics
Granular native usage metrics, incl. QPS resource, query latency, and more
Integration with third-party monitoring tools available
Integration with third-party monitoring systems like Prometheus and Datadog.
Resiliency Guarantee
99.95% uptime SLA
Monitoring
Built-in Metrics
Granular native usage metrics, incl. QPS resource, query latency, and more
Resiliency Guarantee
99.9% uptime SLA
Monitoring
Integration with third-party monitoring tools available
Integration with third-party monitoring systems like Prometheus and Datadog.
Security & Trust
Authorization
RBAC
2 organizational roles, 2 project roles, and 4 built-in cluster roles available for granular permission controls
RBAC
2 organizational roles available for permission controls
Private Connection
Support Private Link
Enhance data security and network performance
Support Private Link for Dedicated Enterprise Cluster ONLY
Come with a high minimum commitment and special setup
Data Encryption
Encryption both in-transit and at-rest
Encryption both in-transit and at-rest
Compliance & Privacy
SoC 2 Type II, ISO27001, GDPR-ready & HIPPA-ready
SOC 2 Type II, GDPR-ready & HIPPA Compliant
Enterprise Support
24/7/365 dedicated support
24/7/365 dedicated support
Authorization
RBAC
2 organizational roles, 2 project roles, and 4 built-in cluster roles available for granular permission controls
Private Connection
Support Private Link
Enhance data security and network performance
Data Encryption
Encryption both in-transit and at-rest
Compliance & Privacy
SoC 2 Type II, ISO27001, GDPR-ready & HIPPA-ready
Enterprise Support
24/7/365 dedicated support
Authorization
RBAC
2 organizational roles available for permission controls
Private Connection
Support Private Link for Dedicated Enterprise Cluster ONLY
Come with a high minimum commitment and special setup
Data Encryption
Encryption both in-transit and at-rest
Compliance & Privacy
SOC 2 Type II, GDPR-ready & HIPPA Compliant
Enterprise Support
24/7/365 dedicated support
Deployment Flexibility
Cloud Service Provider
Available on AWS, GCP, and Azure
Available on AWS, GCP, and Azure
Self-hosted Option
Yes
Option to bring company data to your own cloud (BYOC) and manage the data stored in the customer’s VPC
No
Only fully managed service is available
Cloud Service Provider
Available on AWS, GCP, and Azure
Self-hosted Option
Yes
Option to bring company data to your own cloud (BYOC) and manage the data stored in the customer’s VPC
Cloud Service Provider
Available on AWS, GCP, and Azure
Self-hosted Option
No
Only fully managed service is available