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Milvus logoPinecone logoZIlliz logo

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 46K 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?

Milvus logoZilliz Cloud logoPinecone logo

License

Open Source

Under the Apache 2.0 License

Open Source

Enterprise license fully compatible with Milvus

Closed Source

Operates under proprietary licensing

Infrastructure Responsibilities

Self-hosted

Infrastructure operations and maintenance considerations owned between customer

Fully-managed SaaS

Automated and fully-managed clusters with minimal provisioning, scaling, or operational burdens.

Fully-managed SaaS

Automated and fully-managed clusters with minimal provisioning, scaling, or operational burdens.

Scalability

Billion+ Scale

Scale-out to a billion vectors with little performance degradation

Billion+ Scale

Scale-out to 10 billion vectors with little performance degradation

Billion Scale with Performance Compromise

Capable of scaling up over a billion vectors, albeit with increased latency and reduced QPS

Performance

Highly performant

1.5X better performance than Pinecone on QPS

Further Enhanced Performance

3X better performance on average than Pinecone on QPS and latency

Moderate Performance

Sufficient for organizations without  high-performance requirements

Pricing

Not Applicable

User incurs hardware and hosting costs

Effectively Scaled, Usage-based Pricing

Average 3x+ higher QP$ than Pinecone, and cost-effective pricing that adjusts with increased usage

Usage-based Pricing, best for small use cases

Lower QP$ and can become significantly expensive, particularly in high-concurrency use cases as usage scales.

Milvus logo

License

Open Source

Under the Apache 2.0 License

Infrastructure Responsibilities

Self-hosted

Infrastructure operations and maintenance considerations owned between customer

Scalability

Billion+ Scale

Scale-out to a billion vectors with little performance degradation

Performance

Highly performant

1.5X better performance than Pinecone on QPS

Pricing

Not Applicable

User incurs hardware and hosting costs

Zilliz Cloud logo

License

Open Source

Enterprise license fully compatible with Milvus

Infrastructure Responsibilities

Fully-managed SaaS

Automated and fully-managed clusters with minimal provisioning, scaling, or operational burdens.

Scalability

Billion+ Scale

Scale-out to 10 billion vectors with little performance degradation

Performance

Further Enhanced Performance

3X better performance on average than Pinecone on QPS and latency

Pricing

Effectively Scaled, Usage-based Pricing

Average 3x+ higher QP$ than Pinecone, and cost-effective pricing that adjusts with increased usage

Pinecone logo

License

Closed Source

Operates under proprietary licensing

Infrastructure Responsibilities

Fully-managed SaaS

Automated and fully-managed clusters with minimal provisioning, scaling, or operational burdens.

Scalability

Billion Scale with Performance Compromise

Capable of scaling up over a billion vectors, albeit with increased latency and reduced QPS

Performance

Moderate Performance

Sufficient for organizations without  high-performance requirements

Pricing

Usage-based Pricing, best for small use cases

Lower 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 cloud

    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

    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 cloud

    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

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

    Zilliz Cloud logoPinecone logo

    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

    Zilliz Cloud logo

    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

    Pinecone logo

    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

    Zilliz Cloud logoPinecone logo

    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.

    Zilliz Cloud logo

    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.

    Pinecone logo

    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

    Zilliz Cloud logoPinecone logo

    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.

    Zilliz Cloud logo

    Resiliency Guarantee

    99.95% uptime SLA

    Monitoring

    Built-in Metrics

    Granular native usage metrics, incl. QPS resource, query latency, and more

    Pinecone logo

    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

    Zilliz Cloud logoPinecone logo

    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 & HIPAA-ready

    SOC 2 Type II, GDPR-ready & HIPAA Compliant

    Enterprise Support

    24/7/365 dedicated support

    24/7/365 dedicated support

    Zilliz Cloud logo

    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 & HIPAA-ready

    Enterprise Support

    24/7/365 dedicated support

    Pinecone logo

    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 & HIPAA Compliant

    Enterprise Support

    24/7/365 dedicated support

  • Deployment Flexibility

    Zilliz Cloud logoPinecone logo

    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

    Zilliz Cloud logo

    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

    Pinecone logo

    Cloud Service Provider

    Available on AWS, GCP, and Azure

    Self-hosted Option

    No

    Only fully managed service is available

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