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Pinecone

  • What it is:Pinecone is a fully managed, serverless vector database for building accurate, performant AI applications at scale in production.
  • Best for:AI/ML engineering teams building production RAG apps, Startups with variable AI workloads, Teams using LangChain/LlamaIndex frameworks
  • Pricing:Free tier available, paid plans from $50/month minimum
  • Rating:85/100Very Good
  • Expert's conclusion:Pinecone is the best choice for production-ready AI applications needing reliable, scalable vector search with minimal infrastructure management.
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Reviewed byMaxim Manylov·Web3 Engineer & Serial Founder

What Is Pinecone and What Does It Do?

Pinecone is a managed vector database service that offers engineers an easy way to develop AI models that can quickly retrieve data at scale. It was created by Edo Liberty so that vector search would be available to companies other than the large tech firms that have been able to utilize this type of search technology because they have the engineering resources to do it.

Active
📍New York, NY
📅Founded 2019
🏢Private
TARGET SEGMENTS
DevelopersStartupsEnterpriseFortune 500

What Are Pinecone's Key Business Metrics?

👥
Shopify, Hubspot, Zapier, Gong, Vanguard
Notable Customers
👥
From thousands to 50+ billion vectors
Customer Scale Range
📊
Under 50 milliseconds
Query Latency
📊
Fast Company's Most Innovative Companies 2025 (only vector database honored)
Recognition
Regulated By
SOC 2 Type II(USA)GDPR Compliant(EU)ISO 27001(Global)HIPAA Certified(USA)

How Credible and Trustworthy Is Pinecone?

85/100
Excellent

Pinecone has demonstrated strong credibility as the leader in vector databases due to its enterprise customers, numerous compliance and security certifications, and being recognized as the only vector database to be included in Fast Company's Most Innovative Companies List.

BREAKDOWN
Product Maturity85/100
Company Stability80/100
Security & Compliance95/100
User Reviews80/100
Transparency85/100
Support Quality85/100
TRUST SIGNALS
Used by Fortune 500 companies including Vanguard, Shopify, HubspotSOC 2 Type II, GDPR, ISO 27001, HIPAA certifiedOnly vector database named to Fast Company's Most Innovative Companies 2025Sub-50ms query latency with uptime SLAsPrivate networking and hierarchical encryption options available

What is the history of Pinecone and its key milestones?

2019

Company Founded

Edo Liberty, formerly a research director at AWS and Yahoo!, founded Pinecone to democratize vector search technology that was previously only accessible to large tech companies with extensive engineering resources.

2023

Leadership Addition

Bob Wiederhold joined as President and COO, bringing experience from leading NoSQL database startup Couchbase through its $1.2B IPO.

2025

Innovation Recognition

Pinecone was named to Fast Company's list of World's Most Innovative Companies 2025, recognized as the only vector database in the Enterprise category.

Who Are the Key Executives Behind Pinecone?

Edo Liberty— Founder & CEO
Former research director at AWS and Yahoo!, where he worked on custom vector search systems at scale. Founded Pinecone to make vector database technology accessible to all engineering teams.
Bob Wiederhold— President & COO
Former CEO of NoSQL database startup Couchbase, which achieved a $1.2B IPO in July 2021. Brings extensive experience in database infrastructure and scaling.

What Are the Key Features of Pinecone?

⚡
High-Speed Vector Search
Executes approximate nearest neighbor (ANN) searches across billions of items in milliseconds using optimized algorithms and dimensionality reduction techniques.
✨
Hybrid Search
Combines sparse and dense embeddings to deliver more robust and accurate search results while optimizing costs and performance based on use case requirements.
✨
Real-Time Indexing
Dynamically indexes upserted and updated vectors in real-time to ensure fresh reads and consistency across applications.
📊
Metadata Filtering
Retrieve only vectors matching specific metadata filters for more precise and relevant search results.
✨
Fully Managed Infrastructure
Serverless vector database that automatically handles scaling, replication, load balancing, and performance optimization without manual infrastructure management.
💬
Multi-Model Support
Choose from hosted embedding models or bring your own vectors; integrates with third-party models including OpenAI.
🔒
Enterprise Security
Encryption at rest and in transit, hierarchical encryption keys, private networking options, SOC 2, GDPR, ISO 27001, and HIPAA compliance.
🔗
LLM Integration
Purpose-built for large language models with in-context learning capabilities to reduce hallucinations by providing relevant contextual data with each query.

What Technology Stack and Infrastructure Does Pinecone Use?

Infrastructure

Fully managed cloud infrastructure with multi-region deployment, private networking options, and load balancing across distributed pods

Technologies

Vector indexing algorithmsDimensionality reductionHigh-dimensional data processing

Integrations

OpenAI and other LLM providersThird-party embedding modelsAPI-based applications

AI/ML Capabilities

Specialized vector search infrastructure designed specifically for machine learning and LLM applications, supporting both hosted embedding models and custom vector ingestion with low-latency retrieval.

Based on official product documentation and company materials

What Are the Best Use Cases for Pinecone?

LLM Application Developers
Use Pinecone to create AI applications where hallucination is reduced through the storage of contextual data in Pinecone and then retrieving only the relevant contextual data for each query using in-context learning.
Enterprise Search Teams
Use Pinecone to power the semantic search of very large document stores (e.g., customer support databases) in sub-millisecond query times; Vanguard saw an improvement in the accuracy of their customer support by 12% when they switched to a hybrid retrieval model.
E-commerce and Marketplace Companies
Create recommendation engines and product discovery features by storing product embeddings in Pinecone and performing fast similarity searches.
Content and Media Companies
Use Pinecone to perform semantic search over images, text, and/or audio content; enable similarity based recommendations and content discovery without being limited by keyword matching.
Data Scientists and ML Teams
Streamline the process of managing vector data for your machine learning application (e.g., classification, anomaly detection, clustering) so you do not have to build out your own custom infrastructure.
Startups with Limited ML Infrastructure
Use Pinecone to access enterprise grade vector search functionality without having to manage your own custom infrastructure; Pinecone is a fully managed serverless service that takes care of the complexities of scaling and optimizing.
NOT FORReal-Time Transaction Processing Systems
This option is not recommended as while the sub-50ms latency offered by Pinecone is excellent for nearly all AI/ML applications, there are certain types of applications (such as high frequency financial trading) that require even lower latencies than what any cloud service can provide.
NOT FORApplications Requiring Sub-Millisecond Response Times
Not best – Pinecone’s 50 millisecond latency goal will cover most of AI application needs, however, it may not cover ultra low latency needs.
NOT FORFully On-Premise Deployments Only
Not suitable – Pinecone is a managed cloud service that does not have self-hosted options although customers can deploy a private managed region to their own cloud environment.

How Much Does Pinecone Cost and What Plans Are Available?

Pricing information with service tiers, costs, and details
☐Service$CostℹDetails🔗Source
Starter$0Up to 2 GB storage, 2M write units/mo, 1M read units/mo, up to 5 indexes, 100 namespaces per index, access to most embedding models—
Standard$50/month minimum$0.33/GB/month storage, $4 per million write units, $16 per million read units, SAML SSO, backup/restore, all regions—
Enterprise$500/month minimumHigher capacity limits, private networking, customer-managed encryption keys, HIPAA compliance, volume discounts available—
Dedicated (BYOC)Custom quoteBring-your-own-cloud deployment, private regions, premium support—
Inference (Standard/Enterprise)$0.08 per million tokensEmbedding and reranking models, input and output tokens—
Assistant (Standard/Enterprise)$0.05 per assistant hour + $5 per 1M context tokensPlus storage costs, plan-based limits on Starter—
Starter$0
Up to 2 GB storage, 2M write units/mo, 1M read units/mo, up to 5 indexes, 100 namespaces per index, access to most embedding models
Standard$50/month minimum
$0.33/GB/month storage, $4 per million write units, $16 per million read units, SAML SSO, backup/restore, all regions
Enterprise$500/month minimum
Higher capacity limits, private networking, customer-managed encryption keys, HIPAA compliance, volume discounts available
Dedicated (BYOC)Custom quote
Bring-your-own-cloud deployment, private regions, premium support
Inference (Standard/Enterprise)$0.08 per million tokens
Embedding and reranking models, input and output tokens
Assistant (Standard/Enterprise)$0.05 per assistant hour + $5 per 1M context tokens
Plus storage costs, plan-based limits on Starter

How Does Pinecone Compare to Competitors?

FeaturePineconeWeaviateQdrantMilvus
Core FunctionalityServerless vector DBVector DB + modulesVector DBOpen-source vector DBVector DB
Starting Price$0 (Starter)$0 (Free)$0 (Free tier)$0 (Community)
Free TierYes (2GB)YesYesYes (self-hosted)
Enterprise FeaturesSSO, HIPAA, BYOCSSO, RBACSSO, RBACEnterprise edition
API AvailabilityREST + SDKsREST + GraphQLREST + gRPCREST + gRPC
Integration CountLangChain, LlamaIndexLangChain, HaystackLangChainLangChain
Support OptionsCommunity + paidCommunity + enterpriseCommunity + paidCommunity + enterprise
Security CertificationsSOC 2, HIPAASOC 2SOC 2Enterprise certs
Pricing ModelUsage-based serverlessPod-based + serverlessPod-basedSelf-hosted or managed
Deployment OptionsServerless + BYOCCloud + self-hostedCloud + self-hostedSelf-hosted + Zilliz Cloud
Core Functionality
PineconeServerless vector DB
WeaviateVector DB + modules
QdrantVector DB
MilvusOpen-source vector DB
Starting Price
Pinecone$0 (Starter)
Weaviate$0 (Free)
Qdrant$0 (Free tier)
Milvus$0 (Community)
Free Tier
PineconeYes (2GB)
WeaviateYes
QdrantYes
MilvusYes (self-hosted)
Enterprise Features
PineconeSSO, HIPAA, BYOC
WeaviateSSO, RBAC
QdrantSSO, RBAC
MilvusEnterprise edition
API Availability
PineconeREST + SDKs
WeaviateREST + GraphQL
QdrantREST + gRPC
MilvusREST + gRPC
Integration Count
PineconeLangChain, LlamaIndex
WeaviateLangChain, Haystack
QdrantLangChain
MilvusLangChain
Support Options
PineconeCommunity + paid
WeaviateCommunity + enterprise
QdrantCommunity + paid
MilvusCommunity + enterprise
Security Certifications
PineconeSOC 2, HIPAA
WeaviateSOC 2
QdrantSOC 2
MilvusEnterprise certs
Pricing Model
PineconeUsage-based serverless
WeaviatePod-based + serverless
QdrantPod-based
MilvusSelf-hosted or managed
Deployment Options
PineconeServerless + BYOC
WeaviateCloud + self-hosted
QdrantCloud + self-hosted
MilvusSelf-hosted + Zilliz Cloud

How Does Pinecone Compare to Competitors?

vs Weaviate

Pinecone has the best fully managed serverless architecture and developer experience; Weaviate has the most hybrid search modules and GraphQL however, it requires more configuration than Pinecone. Pinecone has a better pricing model based on usage for varying workloads.

Use Pinecone for production AI applications that need to scale with no maintenance (zero-ops) and Weaviate for complex knowledge graphs.

vs Qdrant

Qdrant is best at deploying on premise and is the lowest cost option for static workload. Pinecone is best at serverless with better inference integration and more momentum in its ecosystem. Qdrant is best when you are working with a fixed budget for your hardware.

Use Pinecone for cloud native AI and Qdrant for self-hosted solutions where you need total control.

vs Milvus/Zilliz

Milvus has the highest scale for billions of vectors but also the most complex operation. Pinecone is easier to use and has managed inference. The pricing for Zilliz Cloud is competitive with Pinecone in terms of retrieval accuracy benchmarking.

Use Pinecone for maximum developer productivity and Milvus for the largest scale requirements.

vs Chroma

Chroma is used by many for local development and is an open source solution. Pinecone was created as a production scalable solution which has claimed to have 50 times cost savings. Chroma does not support enterprise features or production level SLA’s.

Use Pinecone for production deployments and Chroma for prototyping.

What are the strengths and limitations of Pinecone?

Pros

  • Serverless architecture completely automated – no need to plan capacity
  • Developer friendly API – easy to use SDK’s and web console for fast prototyping
  • Integrated inference – hosted embedding and reranking models make vendor management less complicated
  • Highest performance – Optimized for real time vector search with filtering
  • Largest ecosystem – Native integrations with LangChain, LlamaIndex, Haystack
  • Pricing based on serverless usage – Only charged for actual reads/writes/storage usage
  • The product is ready for production; the service has an uptime Service Level Agreement (SLA), it integrates with your existing monitoring systems, and we will help you set up backups and restore them if needed.

Cons

  • The service has a minimum commitment of $50 / month for the Standard plan and $500 / month for the Enterprise plan regardless of how much you use it.
  • Vendor Lock-in - The serverless platform uses a proprietary format that is very hard to switch away from.
  • Minimal Pod Control - Because you are using serverless, you do not have as much ability to tune performance in detail.
  • The price can grow rapidly -- read/write units add up fast when you move into production.
  • Multi-Tenancy Isolation - A single project model does not provide enough flexibility for Software as a Service (SaaS) vendors who need to isolate their customers.
  • Immature Ecosystem -- Pinecone is a young company and currently has far fewer advanced modules than many open source alternatives.
  • Inaccurate Unit Pricing -- The pricing for Pinecone is opaque so you need to use a cost calculator to get an accurate picture of what your costs will be.

Who Is Pinecone Best For?

Best For

  • AI/ML engineering teams building production RAG apps — Serverless removes the burden of managing infrastructure during both experimentation and deployment.
  • Startups with variable AI workloads — The usage-based pricing matches well with the variable nature of query volumes and growth.
  • Teams using LangChain/LlamaIndex frameworks — Native integration accelerates the velocity of your development.
  • Companies needing quick AI prototype to production — Rapidly creating indexes and managing inference reduces your time-to-value.
  • Enterprise AI teams (post-Starter growth) — Pinecone supports Single Sign-On (SSO), Health Insurance Portability and Accountability Act (HIPAA), and Bring Your Own Certificate (BYOC) compliance for regulated industries.

Not Suitable For

  • Cost-sensitive hobbyists or tiny workloads — The $50 / month minimum means that the free tier is too restrictive, therefore, consider either Chroma or running your own FAISS locally.
  • Teams requiring full infrastructure control — While serverless allows you to eliminate the need to worry about pod tuning, you may still want to consider Qdrant or Weaviate for hosting yourself.
  • Multi-tenant SaaS providers — Because the single project isolation model does not allow for customer namespacing, you may want to consider running a solution like Elasticsearch.
  • Static massive-scale deployments — For large data sets and high query volumes, serverless is likely less cost effective than dedicated clusters, therefore, consider using Milvus.

Are There Usage Limits or Geographic Restrictions for Pinecone?

Starter Storage
2 GB maximum
Starter Write Units
2 million per month
Starter Read Units
1 million per month
Starter Indexes
Up to 5 indexes
Namespaces per Index
100 maximum
Standard Minimum
$50/month commitment
Enterprise Minimum
$500/month commitment
Region Availability
AWS multi-region (full list in docs)
Assistant Limits (Starter)
3 assistants max, 1 GB file storage, 1.5M LLM tokens
Index Types
Dense and Sparse vectors only

Is Pinecone Secure and Compliant?

SOC 2 Type IICompleted audit covering security, availability, processing integrity, confidentiality, privacy
HIPAA ComplianceEnterprise plan supports healthcare workloads with BAA available
Customer-Managed Encryption KeysEnterprise feature - bring your own KMS keys for data at rest
SAML/SSOStandard and Enterprise - integrates with identity providers like Okta, Azure AD
Data EncryptionTLS 1.3 in transit, AES-256 at rest across all plans
Private NetworkingEnterprise VPC peering eliminates public internet exposure
GDPR ComplianceData residency options and deletion capabilities available
Audit LoggingConsole metrics and API monitoring, Prometheus/Datadog integrations
Bring Your Own Cloud (BYOC)Dedicated - eliminates third-party data residency concerns

What Customer Support Options Does Pinecone Offer?

Channels
Comprehensive guides and API documentation at docs.pinecone.ioAvailable for all tiersDeveloper community support
Specialized
Enterprise customers receive dedicated support integrated into their plan
Business Tier
Enterprise plan includes priority support with custom SLA options

What APIs and Integrations Does Pinecone Support?

API Type
REST API with gRPC support for high-performance vector operations
SDKs
Python, JavaScript/Node.js, Java, Go, and other language SDKs available
Authentication
API Key based authentication
Documentation
Comprehensive API documentation at docs.pinecone.io with code examples
Integration
Integrates with major AI/ML frameworks and embedding models (Cohere, OpenAI embeddings, etc.)
Use Cases
Vector search, semantic search, similarity matching, RAG (Retrieval-Augmented Generation) applications, AI-powered search and recommendations

What Are Common Questions About Pinecone?

Pinecone offers four different pricing plans: Starter (has no monthly cost and includes 2 GB of storage), Standard ($50 / month minimum), Enterprise ($500 / month minimum), and a Dedicated plan that has a custom pricing model. All three paid plans charge based on the number of read/write units you consume beyond your monthly minimum.

Pinecone charges $0.33 / GB / month for storage on all Standard pricing plans and higher for Enterprise pricing plans. You also pay for the number of read units you consume (which is priced at $16 / million for Standard pricing and $24 / million for Enterprise pricing) and the number of write units you consume (which is priced at $4 / million for Standard pricing and $6 / million for Enterprise pricing).

Pinecone's Standard and Enterprise plans charge $0.08 per million tokens for each input and output query on hosted embedding and reranking models. The Starter plan has 5 million free tokens every month.

Yes, Pinecone has a free Starter plan that provides 2 GB of storage, 2 million write units, and 1 million read units per month and allows users to have access to all of the embedding and reranking models (most).

HIPAA compliance, Customer-managed encryption keys and Private Networking are all part of the Enterprise plan. All tiers have SAML SSO for paid plans.

Pinecone is billed as you go in terms of your actual number of queries and amount of data used. By contrast, OpenSearch bills you for OCU rates hourly based on how many you use regardless of what you actually use which makes Pinecone cheaper for variable workloads.

Yes, Pinecone is listed on the AWS Marketplace and comes with pricing for the Standard plan ($50/month) and can be paired with other AWS services such as Bedrock.

Enterprise plan is priced at $500/month and comes with Dedicated Support, Higher Capacity Limits, Private Networking, Customer-managed Encryption Keys and HIPAA Compliance. Standard is ideal for Production Applications that require standard capabilities.

Is Pinecone Worth It?

Pinecone is the leading managed vector database purpose-built for production AI applications, offering strong performance with flexible pay-as-you-go pricing. Its serverless architecture, integrated embedding models, and comprehensive feature set make it well-suited for teams building vector search and RAG applications at scale. The tiered pricing model with reasonable minimums ($50 for Standard, $500 for Enterprise) balances accessibility with enterprise capabilities.

Recommended For

  • AI/ML teams building semantic search and RAG applications
  • Companies seeking managed vector database without infrastructure overhead
  • Organizations building recommendation systems and similarity search features
  • Enterprise applications requiring HIPAA compliance and private networking
  • Teams wanting integrated embedding and reranking models

!
Use With Caution

  • Organizations with unpredictable vector search volumes — monitor usage patterns to manage costs
  • Teams requiring on-premise deployment — Dedicated (BYOC) option available but custom priced
  • Low-budget projects — $50 minimum may be steep for small-scale experiments

Not Recommended For

  • Cost-sensitive startups in early experimentation phase — free tier has limited capacity
  • Teams requiring completely self-managed solutions — Pinecone is fully managed (serverless)
  • Applications requiring exotic customization — limited control over infrastructure
Expert's Conclusion

Pinecone is the best choice for production-ready AI applications needing reliable, scalable vector search with minimal infrastructure management.

Best For
AI/ML teams building semantic search and RAG applicationsCompanies seeking managed vector database without infrastructure overheadOrganizations building recommendation systems and similarity search features

What do expert reviews and research say about Pinecone?

Key Findings

Pinecone is a fully managed, serverless vector database built specifically for AI applications. Pinecone has four different levels of service and charges customers on a flexible, "pay as you go" basis above the minimum per month charge (free for Starter; $50 for Standard; $500 for Enterprise; and custom for Dedicated). Additionally, Pinecone comes equipped with integrated embedding and reranking models, supports multiple index types, and operates in all AWS regions. Customers have reported very low set-up costs when using Pinecone, and appreciate the query-based billing model used by Pinecone relative to its competitors.

Data Quality

Excellent - comprehensive pricing information from official Pinecone website, AWS Marketplace listing, and Microsoft Marketplace. Documentation thoroughly covers features and pricing structure. Customer feedback available from multiple marketplace sources.

Risk Factors

!
Query volume-based pricing — extremely variable based upon how much an application is being used
!
Primarily AWS-based infrastructure with limited geographic availability
!
Dedicated Tier — custom pricing will need to be negotiated
!
Storage costs ($0.33/GB/month) accumulate quickly for large-scale indexes
Last updated: January 2026

What Additional Information Is Available for Pinecone?

Embedded AI Models

Pinecone integrates popular embedding models from Cohere, OpenAI, and other providers directly in the platform. Customers can choose embedding dimensions and models without external infrastructure setup, reducing operational complexity.

Assistant Feature

All plans include Pinecone Assistant with plan-based limits. Starter includes up to 3 assistants with 1.5M LLM tokens and 1GB file storage. Standard and Enterprise bill by assistant hours ($0.05/hour start) and context tokens ($5 per 1M tokens).

Marketplace Availability

Pinecone is available through AWS Marketplace and Microsoft Marketplace, enabling customers to consolidate billing and leverage existing cloud commitments.

Infrastructure & Scaling

Fully managed serverless architecture scales automatically without manual infrastructure management. Offers 50x lower cost compared to traditional solutions at any scale with live index updates and hybrid search capabilities.

Monitoring & Operations

Includes console metrics, Prometheus and Datadog monitoring support (Standard tier and above), and backup/restore options for data protection.

What Are the Best Alternatives to Pinecone?

  • •
    Weaviate: Weaviate is an open source vector database that allows for deployment in both cloud and self hosted options. While this gives more flexibility and control and can offer lower costs when using cloud services, there is a greater level of operational complexity. This would be best suited to teams that want the open source flexibility and/or want to save money through self hosting.
  • •
    Milvus: Milvus is also an open source vector database that provides highly scalable and customizable options. However, it does require a steeper learning curve and is better suited to teams that have technical experience in setting up and managing their own infrastructure. It would be best for larger deployments where a high degree of customization is required.
  • •
    Chroma: Chroma is a lightweight, open source vector database that is ideal for developing/testing AI related applications. It is relatively easy to set up however it is not the most suitable choice for large-scale use cases in a production environment. It would be best for prototyping and smaller scale deployments.
  • •
    Qdrant: QDrant is another open source vector database that provides a managed cloud option. QDrant has strong performance and provides filtering capabilities. In terms of maturity and features, it falls somewhere between Chroma and Pinecone. It would be best suited to teams that want to use an open source product with the added benefit of having a professionally supported option.
  • •
    Vespa: A powerful, open source big data search and AI engine that is best suited for large scale deployments that require high level filtering and ranking. Due to its complexity, this is typically a good option for large organizations that have existing expertise in deploying Vespa. (vespa.ai)
  • •
    AWS OpenSearch Serverless: An Amazon Web Services (AWS) managed search service that supports vector search. The advantage of using Amazon OpenSearch Service is that it can be integrated directly into the AWS environment; however, you will pay based on hourly usage of operational capacity units (OCU). Because Amazon OpenSearch Service is priced based on usage of OCU per hour, it may be less cost effective than Pinecone’s query-based pricing model for variable workload scenarios. If your team has an investment in OpenSearch, Amazon OpenSearch Service may be the best choice for your organization. (aws.amazon.com)

What Are Pinecone's Vector Db Performance?

Milliseconds
Query Latency
99%+
Recall Rate
Fast ANN searches
Approximate Nearest Neighbor

What Is Pinecone's Vector Db Scalability?

Max Vectors
Billions of vectors
Horizontal Scaling
Distributed architecture with automatic scaling across multiple pods
Sharding Support
Data partitioned across multiple pods with separation of storage from compute
Replication
Multi-cloud deployment with dynamic partitioning strategies

What Vector Db Index Types Does Pinecone Support?

Inverted File Index (IVF)Product Quantization (PQ)Sparse-only indexesBM25Learned-sparse models

Advanced indexing algorithms with compression techniques for efficient similarity search

What Vector Db Features Does Pinecone Offer?

Real-time Indexing

Vectors are upserted and updated dynamically as they are added to the index.

Hybrid Search

It combines both sparse and dense embeddings to create the most robust search capability.

Metadata Filtering

Only retrieve the vectors that match the metadata filter criteria.

Multi-tenancy

Namespace support allows for logical separation of data.

Re-ranking

Advanced re-ranking features utilizing models such as pinecone-rerank-v0.

Automated Embedding Generation

There are multiple hosted embedding models available.

Enterprise Security

Uses AES-256 encryption and allows customers to manage their own keys. Also compliant with HIPAA and GDPR regulations.

What Is Pinecone's Vector Db Deployment?

Cloud Managed
Fully managed cloud-native service with automatic infrastructure scaling
Self Hosted
Not offered - Pinecone is cloud-only
Kubernetes
Multi-cloud deployment capability with cloud-native architecture
Serverless
Serverless indexes with bulk imports via Parquet files and async operations

What Vector Db Distance Metrics Does Pinecone Support?

Cosine similarityEuclidean distance

What Vector Db Integrations Does Pinecone Offer?

Python SDK

Supports latest async and other improvements.

Node.js SDK

Improvements in performance and functionality.

Java SDK

Integration support for enterprise environments.

.NET SDK

Complete platform support.

REST API

API version 2025-04 with enhanced capabilities.

Airbyte

Over 600 connectors for automatic data ingestion and generation of embeddings.

Retrieval-Augmented Generation (RAG)

Supports native RAG workflow capabilities

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