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Pinecone Reviews

Vendor: Pinecone
4.2 out of 5
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Helped 899,204 peers since 2012

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Pinecone mindshare

As of June 2026, the mindshare of Pinecone in the Vector Databases category stands at 6.5%, down from 7.4% compared to the previous year, according to calculations based on PeerSpot user engagement data.
Vector Databases Mindshare Distribution
ProductMindshare (%)
Pinecone6.5%
PostgreSQL8.6%
Milvus6.8%
Other78.1%
Vector Databases

PeerResearch reports based on Pinecone reviews

TypeTitleDate
CategoryVector DatabasesJun 13, 2026Download
ProductReviews, tips, and advice from real usersJun 13, 2026Download
ComparisonPinecone vs Microsoft Azure Cosmos DBJun 13, 2026Download
ComparisonPinecone vs RedisJun 13, 2026Download
ComparisonPinecone vs QdrantJun 13, 2026Download
Suggested products
TitleRatingMindshareRecommending
PostgreSQL4.28.6%96%127 interviewsAdd to research
Redis4.46.5%100%26 interviewsAdd to research
 
 
Key learnings from peers
Last updated May 31, 2026

Valuable Features

Room for Improvement

ROI

Pricing

Popular Use Cases

Service and Support

Deployment

Scalability

Stability

Review data by company size

By reviewers
Company SizeCount
Small Business6
Midsize Enterprise2
Large Enterprise6
By reviewers
By visitors reading reviews
Company SizeCount
Small Business111
Midsize Enterprise63
Large Enterprise186
By visitors reading reviews

Top industries

By visitors reading reviews
Computer Software Company
10%
University
9%
Manufacturing Company
8%
Financial Services Firm
8%
Comms Service Provider
8%
Construction Company
5%
Media Company
5%
Healthcare Company
4%
Educational Organization
4%
Performing Arts
4%
Retailer
3%
Outsourcing Company
3%
Recreational Facilities/Services Company
3%
Government
2%
Legal Firm
2%
Transportation Company
2%
Non Profit
2%
Logistics Company
1%
Wholesaler/Distributor
1%
Security Firm
1%
Wellness & Fitness Company
1%
Hospitality Company
1%
Insurance Company
1%
Pharma/Biotech Company
1%
Energy/Utilities Company
1%
Real Estate/Law Firm
1%
Venture Capital & Private Equity Firm
1%
Marketing Services Firm
1%
Leisure / Travel Company
1%
Consumer Goods Company
1%
Aerospace/Defense Firm
1%

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Pinecone customers

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Pinecone Reviews Summary
Author infoRatingReview Summary
AI Engineer at a educational organization with 51-200 employees4.0I found Pinecone excellent for RAG and financial document search, offering reliable, scalable performance and significant ROI through improved retrieval relevance and speed. Its free tier is limited, and advanced documentation could be better.
Chief Technology Advisor at Kovaad technologies Pvt Ltd5.0I use Pinecone for chat memory, valuing its easy integration, fast retrieval, and free tier. It personalizes chats and is stable. However, a major frustration is its inability to search by metadata, despite otherwise strong performance and support.
Associate Director at a pharma/biotech company with 10,001+ employees3.5Pinecone excels for my scalable, low-latency AI and RAG applications, boosting productivity and ROI with its fully managed, auto-scaling vector search. However, I find its cost high and it lacks custom GPU support compared to alternatives.
Data Science Architect at publicis Sapient4.0I use Pinecone for my RAG system, leveraging its strong UI and auto-scaling for financial data. It slashed task time from 60 to 2 minutes, saving costs. I wish it supported larger vector sizes and offered end-to-end monitoring like LangSmith.
Technical Product Manager at Hireright3.5I use Pinecone for RAG chatbots, valuing its managed, high-performance, and scalable infrastructure, greatly reducing documentation time. Downsides include its cloud-only nature, rising costs, and lack of structured query conversion, though it's stable and secure.
Research Assistant at a university with 10,001+ employees4.0We use Pinecone for our RAG pipeline, valuing its seamless AWS integration and cost-effectiveness compared to OpenSearch. Its documentation is excellent, and it performs well in beta. We wish it offered RAG as a service.
AI/ML Engineer3.5I use Pinecone for AI chatbots, valuing its low latency and scalability for large datasets. While it delivered excellent ROI by boosting bot efficiency, its high cost is a significant concern, leading me to seek alternatives.
Head of Engineering 4.0I used Pinecone for RAG due to its easy setup and namespaces. Despite good stability and scalability, high pricing and limited serverless regions (data residency) led me to switch, though it's great for prototyping.