CtrlK
Pinecone is a managed vector database built for artificial intelligence applications. It lets developers store, index, and query high-dimensional vector embeddings without managing infrastructure. Founded in 2019 by Edo Liberty, the company has positioned itself as one of the leading commercial solutions in the vector search space, competing with open-source alternatives like Weaviate, Chroma, Milvus, and Qdrant.
Edo Liberty founded Pinecone in 2019 after spending years working on large-scale machine learning systems at major technology companies. Before starting the company, Liberty served as Director of Research at Amazon Web Services and Head of Amazon AI Labs, where he built machine learning algorithms and services for AWS customers. Before Amazon, he held research positions at Yahoo Research. Liberty holds a B.Sc. in Physics and Computer Science from Tel Aviv University and a Ph.D. in Computer Science from Yale University, followed by a postdoctoral fellowship at Yale's Program in Applied Mathematics [1].
Liberty's experience at AWS and Yahoo showed him the power of combining AI models with vector search to improve applications like spam detectors and recommendation systems. He recognized that existing databases were poorly suited for similarity search over high-dimensional data, which led him to create a purpose-built solution [2].
In September 2025, Liberty transitioned from CEO to Chief Scientist, and Ash Ashutosh took over as CEO. Ashutosh is a three-time founder of storage and data infrastructure companies (Serano Systems, AppIQ, and Actifio) and previously served as CTO of HP's storage division, a partner at Greylock Partners, and Global Director of Solution Sales at Google [3].
Pinecone has raised approximately $138 million in total funding across three rounds [4].
| Round | Date | Amount | Lead Investor | Valuation |
|---|---|---|---|---|
| Seed | 2021 | $10M | Wing Venture Capital | Not disclosed |
| Series A | 2022 | $28M | Menlo Ventures | Not disclosed |
| Series B | April 2023 | $100M | Andreessen Horowitz | $750M |
The Series B round included participation from Iconiq Growth, Menlo Ventures, and Wing Venture Capital [4]. Reports from late 2025 indicated that Pinecone was exploring strategic options, including a potential sale, with a speculated valuation north of $2 billion. Oracle, IBM, MongoDB, and Snowflake were reportedly among the companies that expressed interest [3].
Pinecone's architecture has evolved through two main deployment models: pod-based and serverless.
The original Pinecone deployment model used pods, which are pre-configured units of hardware (including CPU, memory, and storage). Users selected a pod type and size based on their workload requirements, then scaled by adding replicas for throughput or increasing pod size for capacity. This model was straightforward but required users to make provisioning decisions upfront and could lead to over-provisioning or under-provisioning.
Pinecone offered three pod types, each optimized for different workload characteristics:
| Pod type | Optimized for | Key characteristic |
|---|---|---|
| p1 | Low-latency search | Fastest query performance, higher cost per vector |
| p2 | Cost-effective search | Balanced latency and storage density |
| s1 | Storage-optimized | Highest vector capacity per pod, slightly higher latency |
Each pod type was available in multiple sizes (x1, x2, x4, x8), with each size doubling the capacity of the previous one. Users could also add read replicas to increase query throughput without changing the pod type or size [7].
In January 2024, Pinecone launched its serverless architecture, which separates reads, writes, and storage into independent components. This design eliminates the need to provision compute, configure replicas, or manage node health. Users create an index, specify a cloud computing provider and region, and Pinecone handles everything else behind the scenes [5].
The serverless model claims 10x to 100x cost reduction compared to pod-based deployments for many workloads, since users pay only for the resources they actually consume rather than for idle capacity [5].
Internally, the serverless architecture consists of several key components:
This log-structured indexing approach, similar in concept to log-structured merge trees used in key-value stores like LevelDB and RocksDB, allows Pinecone to handle continuous writes without locking or blocking read operations.
Rather than traditional hash-based or range-based sharding, Pinecone serverless uses geometric partitioning. The vector space is divided into regions, each represented by a centroid. New vectors are assigned to the partition whose centroid is closest. This approach has two advantages: it enables fine-grained data isolation (queries only need to search relevant partitions rather than scanning all shards), and it adapts dynamically as the data distribution evolves over time [15].
Introduced in December 2025, Dedicated Read Nodes (DRNs) provide exclusive infrastructure for queries. Each provisioned node is reserved for a single customer's index, which eliminates noisy-neighbor effects and read rate limits. Pinecone reported that DRNs achieved 5,700 queries per second at P99 latency of 60ms on 1.4 billion vectors. DRNs use hourly per-node pricing, which can be more cost-effective than per-request pricing for sustained, high-throughput workloads [6].
Pinecone uses a variant of the HNSW (Hierarchical Navigable Small World) algorithm as the foundation for its vector search. HNSW constructs a multi-layered graph where each vector is a node. The top layer contains a sparse set of broadly connected nodes, and each subsequent layer adds more nodes with finer connections. During a query, the algorithm enters at the top layer and greedily navigates toward the query vector, descending through layers until it reaches the bottom, where the densest set of connections provides the final candidate set [7].
The key parameters that influence HNSW performance are:
| Parameter | Description | Effect of increasing |
|---|---|---|
| M | Maximum connections per node per layer | Higher recall, more memory usage, slower builds |
| ef_construction | Candidate list size during index building | Better graph quality, slower index construction |
| ef_search | Candidate list size during search | Higher recall, slower query latency |
In Pinecone's managed environment, these parameters are tuned automatically based on the index configuration and workload characteristics. Users do not need to set HNSW parameters directly, which is a deliberate simplification compared to open-source alternatives like Qdrant or Milvus where developers must tune these values themselves [7].
Pinecone supports real-time upserts, meaning vectors can be inserted or updated and become immediately queryable. This is useful for applications where the underlying data changes frequently, such as real-time recommendation engines or continuously updated knowledge bases.
Every vector in Pinecone can carry arbitrary key-value metadata. At query time, users can apply filters on this metadata alongside the vector similarity search. For example, a query might search for the most semantically similar product descriptions but restrict results to items in a particular category or price range [7].
Pinecone's metadata filtering query language is based on MongoDB's query and projection operators. The following operators are supported:
| Operator | Description | Supported types |
|---|---|---|
$eq | Equal to | Number, string, boolean |
$ne | Not equal to | Number, string, boolean |
$gt | Greater than | Number |
$gte | Greater than or equal to | Number |
$lt | Less than | Number |
$lte | Less than or equal to | Number |
$in | Value is in a specified array | String, number |
$nin | Value is not in a specified array | String, number |
$exists | Field exists on the vector | Number, string, boolean |
$and | Logical AND of conditions | N/A (combinator) |
$or | Logical OR of conditions | N/A (combinator) |
A query with metadata filtering looks like this:
results = index.query(
vector=query_embedding,
top_k=10,
include_metadata=True,
filter={
"genre": {"$eq": "fiction"},
"year": {"$gte": 2020}
}
)
Key limitations of metadata filtering include: each vector supports up to 40 KB of metadata; each $in or $nin operator accepts a maximum of 10,000 values; and only $and and $or are allowed at the top level of filter expressions [16].
In the serverless architecture, Pinecone uses disk-based bitmap indexes for metadata filtering. These indexes are adapted from techniques used in data warehouses and are designed to handle high-cardinality filtering scenarios like access control lists efficiently [15].
Namespaces partition data within a single index. Vectors in different namespaces are isolated from each other, meaning a query in one namespace will never return results from another. This is useful for multi-tenant applications where each customer's data needs to remain separate without creating entirely separate indexes [7].
In the serverless architecture, namespaces function as hard partitions. The index builder creates geometric partitions only within namespace boundaries, meaning queries are automatically scoped to the specified namespace. This design supports cost-effective multi-tenant scenarios; for example, Notion uses Pinecone's namespaces to maintain thousands of isolated user indexes within a single Pinecone index [15].
Pinecone supports hybrid search by combining dense and sparse vector representations in a single query. Dense vectors (from models like OpenAI's text-embedding-ada-002 or similar) capture semantic meaning, while sparse vectors (from algorithms like BM25 or SPLADE) capture keyword-level relevance. Each record can contain both a dense vector and a sparse vector, along with metadata. At query time, Pinecone blends the results from both representations to return more relevant matches [8].
Sparse vectors have a very large number of dimensions where only a small proportion of values are non-zero. Each dimension corresponds to a word from a dictionary, and the value represents the importance of that word in the document.
Pinecone provides client libraries for Python, Node.js, Java, and Go. The Python SDK is the most widely used. A typical workflow involves creating an index, upserting vectors, and querying:
from pinecone import Pinecone
# Initialize
pc = Pinecone(api_key="your-api-key")
# Create a serverless index
pc.create_index(
name="my-index",
dimension=1536,
metric="cosine",
spec=ServerlessSpec(
cloud="aws",
region="us-east-1"
)
)
# Connect to the index
index = pc.Index("my-index")
# Upsert vectors with metadata
index.upsert(
vectors=[
{
"id": "doc-1",
"values": [0.1, 0.2, ...], # 1536-dimensional vector
"metadata": {"source": "wiki", "category": "science"}
}
],
namespace="my-namespace"
)
# Query with metadata filter
results = index.query(
vector=[0.15, 0.22, ...],
top_k=5,
include_metadata=True,
filter={"category": {"$eq": "science"}},
namespace="my-namespace"
)
Pinecone also supports bulk operations for upserting, updating, and deleting vectors by metadata filter, which were introduced in 2025 to simplify data management at scale [17].
Pinecone has built a significant customer base across multiple industries. Notable deployments include:
| Company | Use case | Details |
|---|---|---|
| Notion | Knowledge Q&A | Powers Notion AI's Q&A feature across billions of documents using Pinecone serverless with namespace-per-user isolation [15] |
| Gong | Revenue intelligence | Stores billions of vectors from customer conversations for Gong's Smart Trackers feature, enabling real-time concept classification [18] |
| Shopify | E-commerce search | Uses Pinecone for semantic product search and recommendations |
| Vanguard | Customer support | Boosted accuracy by 12% with hybrid retrieval, reduced call times, and enhanced compliance [19] |
| Zapier | Workflow automation | Integrates vector search into automated workflows |
| Adobe | Creative tools | Semantic search across creative assets |
| Cisco | Security analytics | Threat detection using vector similarity |
| HubSpot | CRM intelligence | Powers semantic search in CRM platform |
Common application patterns across these customers include retrieval-augmented generation (RAG) for customer support chatbots, semantic search over product catalogs, recommendation engines, anomaly and fraud detection, and document deduplication [19].
Pinecone uses a consumption-based pricing model for its serverless indexes, charging based on three metrics: read units (RUs), write units (WUs), and storage [9].
| Plan | Monthly Minimum | RU Cost (per million) | Storage (per GB/month) | Support |
|---|---|---|---|---|
| Starter (Free) | $0 | $0 (limited) | $0 (limited) | Community |
| Standard | $50 | $16 | $0.33 | |
| Enterprise | $500 | $24 | $0.33 | Priority |
| Dedicated (BYOC) | Custom | Custom | Custom | Dedicated |
The Starter plan allows experimentation with limited capacity. Standard and Enterprise plans combine minimum monthly usage commitments with pay-as-you-go rates for actual usage beyond the minimums. The Dedicated plan supports Bring Your Own Cloud (BYOC) deployments for organizations with strict data residency requirements [9].
Pinecone is also available through the AWS Marketplace with pay-as-you-go billing [10].
For datasets under 50 million vectors, managed services like Pinecone tend to be cheaper than self-hosting due to the hidden cost of DevOps. At larger scales, self-hosted open-source options can become more cost-effective [14]. Some organizations have reported significant cost savings by migrating away from Pinecone to self-hosted solutions like pgvector; for example, Confident AI publicly documented their migration from Pinecone to pgvector, citing cost reduction as a primary factor [20].
The introduction of Dedicated Read Nodes in late 2025 added a new pricing dimension: hourly per-node billing that can be more economical than per-request pricing for applications with sustained high query throughput [6].
Pinecone has built integrations with most of the popular AI development frameworks and tools.
| Integration | Description |
|---|---|
| LangChain | PineconeVectorStore class for retrieval-augmented generation pipelines |
| LlamaIndex | Native vector store connector for indexing and querying |
| OpenAI | Compatible with OpenAI embedding models; used in OpenAI's cookbook examples |
| Pinecone Inference | Hosted embedding and reranking models on Pinecone's own infrastructure |
| Mastra | Reference vector store provider |
| Airbyte | Data ingestion connector for loading documents into Pinecone |
| Semantic Kernel | Microsoft's orchestration SDK for AI agents |
| Haystack | Document store integration for search pipelines |
The LangChain integration, through the langchain-pinecone Python package, allows developers to build RAG pipelines that embed user queries, search Pinecone for relevant context, and pass that context to a large language model for answer generation [11].
Pinecone Assistant is a higher-level service that lets developers build production-grade chat and agent-based applications without manually constructing RAG pipelines. Users upload documents to the Assistant, which handles chunking, embedding, indexing, and retrieval automatically. The Assistant then uses a language model to generate answers grounded in the uploaded documents. It is designed to reduce the engineering effort required to go from prototype to production for knowledge-retrieval applications [12].
While Pinecone excels at operational simplicity, it has several notable limitations:
The vector database market has grown rapidly since 2022, driven by the adoption of large language models and retrieval-augmented generation patterns. Pinecone competes with several categories of alternatives.
| Competitor | Type | Key differentiator | |-----------|------|--------------------|| | Weaviate | Open-source (Go) | Native hybrid search, GraphQL API, self-hostable | | Chroma | Open-source (Python) | Lightweight, easy prototyping, pip install | | Milvus / Zilliz | Open-source (Go/C++) | GPU acceleration, billions-scale, multiple index types | | Qdrant | Open-source (Rust) | High performance, advanced filtering, Rust-native | | pgvector | PostgreSQL extension | Adds vector search to existing Postgres databases | | FAISS | Library (C++/Python) | Facebook AI research library, not a full database |
Pinecone's main advantage over open-source alternatives is operational simplicity: there is no infrastructure to manage, no index tuning required, and the service scales automatically. Its main disadvantage is vendor lock-in and cost at scale, since users cannot self-host the software. Pinecone is also closed-source, which limits transparency into how the system works internally [13].
Pinecone has obtained SOC 2 Type II certification, ISO 27001 compliance, and GDPR alignment. The company has also completed an external HIPAA attestation, making the platform suitable for regulated industries including pharmaceuticals, banking, and public-sector applications [13].
By early 2026, Pinecone continues to operate as one of the most widely used managed vector databases. The leadership transition to Ash Ashutosh as CEO in September 2025 signaled a shift toward expanding the company's enterprise sales and go-to-market capabilities, while Edo Liberty's move to Chief Scientist focuses the company's technical direction on its AI research ambitions [3].
The company has expanded its product surface beyond pure vector storage. Pinecone Inference provides hosted embedding and reranking models, reducing the need for separate model-serving infrastructure. Pinecone Assistant abstracts away RAG pipeline construction entirely. Dedicated Read Nodes, launched in late 2025, address the needs of high-throughput production workloads that require predictable latency [6].
In early 2026, Pinecone announced a second generation of its serverless architecture, designed to automatically select optimal configurations for different application types, including recommendation engines and agentic systems, without compromising on speed or cost [21].
Reports of a potential acquisition in late 2025 suggest the company is weighing its options between remaining independent and joining a larger platform. Regardless of the outcome, Pinecone's serverless architecture and focus on developer experience have made it a reference point in the vector database category.