Pinecone vs Qdrant

by Gralio Mar 13, 2025

Pinecone and Qdrant are both vector databases tailored for AI applications. Pinecone provides a managed cloud service with convenient integrations, while Qdrant's open-source nature offers flexibility and control at a potentially lower cost. The best choice depends on your company's technical resources and budget constraints.

At Gralio.ai we help to simplify your decision-making process by offering detailed, side-by-side software comparisons like this one, to help you confidently choose the tool that aligns with your business goals.

This comparison was created by analysing 48 reviews and 60 websites, saving 46 minutes of reading.

Pinecone

Website LinkedIn

Qdrant

Website LinkedIn Twitter

About

Pinecone is a cloud-based database designed specifically for AI applications. Unlike traditional databases, it focuses on storing and quickly searching through "vectors," which are mathematical representations of data like text, images, or audio. This allows companies to build AI features that can, for example, instantly find the most relevant information from millions of documents, power accurate recommendation systems, or analyze complex patterns. Pinecone manages all the technical aspects, so developers can focus on building their applications without worrying about infrastructure.

Qdrant is an open-source vector database designed for AI and machine learning applications. It allows your applications to store, search, and analyze large amounts of complex data, like images and text, based on their meaning and relationships to each other. This makes it ideal for building advanced search engines, recommendation systems, and AI-powered features. Qdrant prioritizes speed, scalability, and efficient use of resources.

Summary

Main difference

Pinecone is a fully managed cloud service ideal for businesses seeking a hassle-free vector database solution for AI applications. Qdrant, being open-source, offers greater flexibility and control, making it better suited for companies with in-house expertise who prioritize cost-effectiveness and customization.

Relative strengths of Pinecone (compared to Qdrant)

Fully Managed Cloud Service: Pinecone handles infrastructure management, allowing developers to focus solely on building applications.
Extensive Integrations: Pinecone integrates seamlessly with popular AI/ML tools and platforms.
Robust Feature Set: Pinecone offers advanced features like metadata filtering and upserting, enhancing search capabilities and data management

Relative weaknesses of Pinecone (compared to Qdrant)

Higher Cost: Pinecone's managed service comes at a premium compared to Qdrant's open-source offering.
Less Control: Limited customization options compared to the flexibility of self-hosting Qdrant.
Vendor Lock-in: Migrating from Pinecone to another solution can be complex due to its proprietary nature

Who is using Pinecone and Qdrant?

Laurens Nys

Founder & GTM Engineer

No data

Who should use Pinecone VS. Qdrant

Pinecone is a cloud-based vector database for AI applications. It allows developers to store and search through vector embeddings of data like text and images, enabling features such as semantic search and recommendations. Users praise its speed, reliability, and ease of use, particularly highlighting the simple integration and excellent support. Some users mention limitations in ecosystem integration and metadata management.

Qdrant is an open-source vector database for AI and ML applications. It excels at storing, searching, and analyzing complex data like images and text, enabling advanced search engines, recommendation systems, and AI-powered features. Qdrant prioritizes speed, scalability, and efficient resource use, making it ideal for businesses building AI-driven applications.

Ideal for medium to large businesses (100+ employees) seeking AI-powered applications.
Best for software, IT, and telecommunications companies building AI applications.

Best for medium to large businesses (101+ employees) seeking AI/ML solutions.
Particularly well-suited for E-commerce, IT/Software, and Marketing/Advertising.

Pinecone and Qdrant features

Supported

Partially supported

Not supported

Type in the name of the feature or in your own words tell us what you need

High-Performance Vector Search

Supported

Pinecone supports efficient similarity search for massive-scale AI applications using high-dimensional vectors.

Supported

Qdrant offers efficient similarity search for high-dimensional vectors, suitable for large-scale AI applications.
Cloud-Native Scalability & High-Availability

Partially supported

Pinecone supports vertical and horizontal scaling for effortless adaptation to growing demands.

Supported

Qdrant Cloud supports horizontal and vertical scaling with zero-downtime upgrades.
Easy API Integration & Deployment

Partially supported

Pinecone offers easy API integration and supports various deployment methods, though Docker support is not explicitly confirmed.

Supported

Qdrant offers a RESTful API and SDKs in multiple languages for easy integration. Deployment options include a Cloud API, but Docker support is not explicitly mentioned.
Efficient Data Storage & Compression

Supported

Pinecone uses Product Quantization to compress vectors, reducing memory usage.

Supported

Qdrant supports efficient storage and compression with quantization and delta encoding, reducing storage costs.
Vector similarity search

Supported

Pinecone supports vector similarity search with its API and k-NN algorithm.

Supported

Qdrant is a vector database designed for similarity search using various methods like ANN.
Index management

Supported

Pinecone offers tools for creating, managing, and optimizing indexes, including features like upserting, querying, and metadata filtering.

Supported

Qdrant provides tools for index creation, management, and optimization, including advanced techniques and real-time updates.

Qualities

Value and Pricing Transparency

+0.71

Strongly positive sentiment

No data
Customer Service

+1

Strongly positive sentiment

No data
Ease of Use

+1

Strongly positive sentiment

No data
Reliability and Performance

+1

Strongly positive sentiment

No data
Ease of Implementation

+0.75

Strongly positive sentiment

No data
Scalability

+0.67

Rather positive sentiment

No data

Pinecone and Qdrant Pricing

Pinecone's pricing is available through various plans: Starter (free), Standard (from $25/month), and Enterprise (from $500/month). Usage-based pricing applies to serverless indexes, inference, and Pinecone Assistant features. Additional support plans are available as add-ons.

Plans

User sentiment

Strongly positive

+0.71

See full Pricing page

No data

See full Pricing page

Pinecone and Qdrant review insights

48 reviews analysed from

Users love

Excellent for AI/ML applications and similarity search.
Hassle-free and cheap embedding creation.
Reliable and fast with competitive pricing.
High performance with upsert and search in milliseconds.
Simple integration via API and deployment.
Incredible support.
Useful metadata features.
Serverless indexes simplify maintenance and scaling.
Free limited indexes for development.
Affordable serverless pricing for startups.
Supports large embeddings, sparse & dense embeddings, and fast queries.
Great user interface and learning resources.
Easy workflow integration.
Fast and stable with good uptime.
Seamless, high-performance vector search.
Real-time updates and scalability.
Effortless workflow integration and top-notch support.
Sparse-Dense offering improves retrieval quality.
Excellent documentation and tutorials.
User-friendly, especially for vector database newcomers.
Good documentation and Python SDK.
Production-ready with low latency.
Good integration with AI/LLM ecosystem.
Responsive and easy implementation.
Helpful documentation and integrations with OpenAI and Langchain.
SOC 2 and HIPAA compliant.

No data

Users dislike

Limited ecosystem integration and complex use cases.
Pinecone Assistant is not production-ready.
Documentation feels lightweight.
Limited clarity on AKNN algorithm.
Limited metadata documentation and single-stage queries.
Smart bot support is less effective than alternatives like ChatGPT.
Occasional downtime.
Pricing can be steep for smaller projects.
10,000 namespace limit on the serverless instance.
Difficult metadata management.
Web interface needs improvement.
Limited data center location options and lack of MFA.
Inaccurate search results for semantic queries.
No Azure support.
No easy way to export all vectors.
Tricky pricing structure.
Limited features and integrations compared to established databases.
Learning curve to fully leverage capabilities.
Customization and vector exportability limitations.

No data

Pinecone and Qdrant Ratings

G2

4.6/5

(36)
Glassdoor

5.0/5

(5)

G2

5.0/5

(12)
Glassdoor

1.0/5

(1)

Company health

Employee growth

24% increase in the last year

65% increase in the last year

Web traffic

10% decrease in the last quarter

14% increase in the last quarter

Financing

July 2023 - $138M

October 2023 - $38M

How does Qdrant's open-source nature affect long-term maintenance costs?

Qdrant's open-source nature can significantly reduce long-term maintenance costs. By leveraging community support and having direct access to the source code, businesses can potentially avoid vendor lock-in, reduce licensing fees, and customize the software to meet their specific needs, thereby lowering ongoing maintenance and support expenses.

Which product offers easier integration with existing SQL databases?

MyScale offers easier integration with existing SQL databases because it allows users to interact with the database using familiar SQL commands. Neither Pinecone nor Qdrant offer direct SQL integration. They are specialized for vector search and utilize APIs and SDKs for interaction.

What are the advantages of Pinecone?

Pinecone offers a fully managed, cloud-based solution, simplifying deployment and infrastructure management for users. It's praised for its speed and performance, particularly with real-time updates and scaling, along with excellent support and documentation. Users highlight its ease of use, especially for vector database newcomers, and seamless integration with AI/LLM ecosystems like OpenAI and Langchain. It also supports a wide range of embedding types, including large, sparse, and dense vectors.

What are the disadvantages of Pinecone?

Pinecone users have noted a few disadvantages. Some find the ecosystem integration limited and complex use cases challenging. The Pinecone Assistant's lack of production readiness, lightweight documentation, and limited clarity on the AKNN algorithm have also been mentioned. Metadata management and the web interface could benefit from improvements, and some users desire more data center locations and multi-factor authentication. Pricing can be expensive for smaller projects, and there are limitations on namespaces in the serverless instance. Finally, exporting vectors and the pricing structure have been identified as areas needing improvement.

Alternatives to Pinecone and Qdrant

Weaviate

Weaviate is a specialized database designed for AI applications. It allows businesses to build features like semantic search by making sense of relationships between data, not just keywords. It's open-source, meaning it's free to use and adaptable. Weaviate works well with existing AI tools and offers flexible setup options for different needs.

Microsoft SQL Server

Microsoft SQL Server is a database management system for businesses of all sizes. It helps you analyze various types of data and can be used in multiple environments, including on your servers and in the cloud. SQL Server offers high performance and strong security features. It allows developers to build applications using different programming languages and provides mobile business intelligence tools.

MyScale

MyScale is a vector database designed for storing and searching data using artificial intelligence. It allows businesses to build AI-powered applications using the familiar SQL programming language, making it easier to integrate with existing systems. This approach reduces the need for specialized knowledge, allowing companies to develop and launch AI applications more efficiently and cost-effectively.

Orb

Orb is a cloud-based billing software designed for modern businesses with complex pricing models like subscriptions or usage-based billing. It helps companies automate their billing processes, from invoicing to revenue recognition. Orb stands out with its flexibility, allowing you to easily change pricing strategies and experiment with different models without heavy technical lifting. It also provides a centralized system for finance and engineering teams to track usage data and gain insights into revenue.

Milvus

Milvus is a free, open-source database specifically designed for managing and searching large collections of AI data. If your company uses AI that analyzes images, video, or text, Milvus can help you quickly find similar items within those collections. It is commonly used in applications like image recognition, recommendation systems, and fraud detection. Milvus is known for its speed, ability to handle massive datasets, and ease of use.

Elastic Enterprise Search

Elastic Enterprise Search lets you create modern search experiences for your company's website, online store, internal documents, and customer support resources. Built on the popular Elasticsearch platform, it offers AI-powered features like relevant search suggestions and detailed analytics to understand user behavior. It's designed to handle large amounts of data and can be customized to fit your business needs, whether you need a simple website search or a complex e-commerce system.

Page co-authored by

Michal Kaczor

CEO at Gralio

Michal has worked at startups for many years and writes about topics relating to software selection and IT management. As a former consultant for Bain, a business advisory company, he also knows how to understand needs of any business and find solutions to its problems.

Tymon Terlikiewicz

CTO at Gralio

Tymon is a seasoned CTO who loves finding the perfect tools for any task. He recently headed up the tech department at Batmaid, a well-known Swiss company, where he managed about 60 software purchases, including CX, HR, Payroll, Marketing automation and various developer tools.

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