Pinecone vs Qdrant vs Weaviate: Best vector database for AI projects

Home › Blog › Vector database selection guide: Pinecone vs. Weaviate vs. Qdrant for enterprises

The vector database market is incredibly saturated. Over twenty dedicated vector management systems compete for attention, while traditional databases rapidly add vector support.

With $200+ million in VC funding raised since 2023, it’s challenging to compare these platforms objectively and understand which delivers real value beyond the hype.

This guide offers a comprehensive vector database selection framework. We will examine the market landscape, define selection criteria, and make a side-by-side comparison of three popular market players—Weaviate, Qdrant, and Pinecone to explore the long-term future for vector databases that goes beyond the hype.

The rise of vector databases

The surge of generative AI and RAG applications created perfect timing for vector databases. Enterprise organizations needed tools to connect unstructured internal data, corporate documentation, knowledge bases, and customer records to LLMs.

The process transforms raw data into numerical representations called embeddings, stored as high-dimensional vectors that vector databases can efficiently manage and query.

Enterprise adoption exploded between 2022-2024. Meta, Yahoo, Google, eBay, Walmart, and IKEA all developed vector search toolsets. Academic interest doubled too; arXiv papers on vector search increased 100% from 2019 to 2022.

Papers on Arxiv that mention "similarity search" or "semantic search" — The rate at which papers on vector search nearly doubled in the last five years

Vector search evolved from a niche infrastructure to a competitive battleground. New entrants like Qdrant emerged while traditional databases rushed to add vector capabilities.

In 2025, the vector database market roughly has the following landscape.

Vector database market landscape — Aside from vector-only databases, vector libraries, search engines, and even traditional DBs with vector capabilities are part of the landscape

Dedicated vector databases: The market leaders

Vector databases enable machine learning teams to search and retrieve data based on similarity between stored items rather than exact matches. Unlike traditional databases that rely on predefined criteria, vector databases group embeddings by semantic and contextual connections.

This means finding similar content even when it’s not semantically identical or doesn’t fit precise user-defined criteria.

Vector databases don't rely on precise query matches — Vector databases see connections and similarities between embeddings that are not semantically related

Dedicated vector databases can be further clustered into two buckets: open-source platforms like Qdrant and Chroma and commercially licensed solutions like Weaviate and Pinecone.

Open source vs commercial vector databases — Most solutions in the vector DB space are open-source, with only a handful of commercial products

Vector libraries vs. dedicated databases

Vector libraries like Facebook’s FAISS (35.9k GitHub stars), Google’s ScaNN (35.9k stars), and Spotify’s ANNOY (14k stars) offer high adoption rates for specific use cases.

While vector databases provide comprehensive vector management, libraries focus primarily on similarity searches using the approximate nearest neighbor (ANN) algorithm.

What is ANN?

The All-Nearest-Neighbors algorithm systematically examines a collection of points and, for each point, identifies the single point that lies closest to it. It then produces a complete listing that pairs every point with its nearest companion and creates a clear picture of the “closest relationships” within the entire set.

They differentiate from the rest of the market by the ease of setup and integration but have limitations that prevent their widespread adoption for enterprise-grade use cases.

Immutable indexed. After an engineering team imports indexes to a library, they can no longer be edited or deleted. Making a change to an index requires building a new one from the ground up.

No real-time queries. Query libraries design indexes only after all data is imported. This complicates vector management, especially for use cases with millions or billions of stored objects.

Consensus: Libraries suit smaller projects; vector databases handle high-volume enterprise AI solutions.

How engineering teams choose between  vector databases and vector libraries — Vendor databases are one-stop-shops with robust search and management capabilities, whereas vector libraries focus primarily on search

Vector-capable traditional databases

Seeing the success of dedicated vector databases (Pinecone closed a $100 M Series B round in 2023, and Qdrant raised $28 million in Series A last year), traditional database vendors hopped on the bandwagon and added vectors to supported data types.

Vector-capable databases are now emerging both in the SQL and NoSQL infrastructure layers.

NoSQL databases: MongoDB, Cassandra, Redis, Neo4j, Rockset
SQL databases: TimeScale, SingleStore, Kinetica, ClickHouse.

While vendors promote “one-size-fits-all” approaches, vector search capabilities remain limited in ranking, relevance tuning, and text matching. Most lack industry-standard BM25 algorithm support.

Jo Kristian Bergum, Chief Scientist at Vespa.AI, pointed out in a blog post ‘The rise and fall of the vector database infrastructure category”:

“Just as nobody who cares about search quality would use a regular database to power their e-commerce search, adding vector capabilities doesn’t suddenly transform an SQL database into a complete retrieval engine.”

That’s why enterprise engineering teams continue to choose specialized vector databases over traditional solutions augmented with vector capabilities.

Text-based databases

Vector search and traditional text search are rapidly converging. Vector databases like Weaviate now position themselves as “AI-native databases” with full search capabilities.

On the other hand, ElasticSearch, a long-term leader in the search engine market, incorporated vector search capabilities built on top of Apache Lucene in 2023.

Now, the platform is marketed as a “search engine with a fully integrated vector database.”

Enterprise companies are seeking both traditional and vector search to power their RAG applications, prompting vendors to integrate both capabilities into their technology.

What type of vector search tools is the best fit for enterprise-grade applications?

Before comparing individual vendors, engineers need to choose the right category of tools for their vector search use case.

Here is a short recap of when engineering teams prefer using tools in each of the categories listed above.

Cheatsheet for choosing the right tool for vector storage and management — All tools in the vector database landscape have unique benefits and use cases, but dedicated DBs are the safest option for enterprises

Key consideration: Vector integrations in traditional databases and search engines remain relatively new. While these will likely mature over the next 3-5 years, dedicated vector databases are currently the safest choice for enterprise applications.

Vector-first database market leaders

There are dozens of vector databases on the market. To simplify the selection process, we are zooming in on three rapidly growing vector-only DBs: Pinecone, Qdrant, and Weaviate.

Pinecone

Pinecone is a high-performance, fully managed vector database that scales to tens of billions of embeddings at a sub-10 ms latency.

It is compliant with SOC 2, HIPAA, ISO 27001, and GDPR and is a popular choice among enterprise companies. Pinecone partnered with both Amazon Web Services and Microsoft Azure and is used by rapidly scaling companies like Notion.

Qdrant

Qdrant offers high-performance, open-source vector search with single-digit millisecond latency. Available as managed cloud, hybrid, or private deployments with enterprise features: SSO, RBAC, TLS, Prometheus/Grafana integration, SOC 2 compliance.

Notable users: Tripadvisor, HubSpot, Deutsche Telekom, Bayer, and Dailymotion use Qdrant.

Weaviate

Weaviate combines cloud-native architecture with an optimized HNSW engine for sub-50ms ANN query response. Supports multi-tenancy and enterprise compliance like Pinecone and Qdrant.

Notable users: Morningstar, a global finance and banking enterprise, used Weaviate to power internal document search, and Neople, one of the leading game publishers in South Korea, built an agentic customer service platform on top of the database.

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Criteria for choosing a vector database

Each of these databases has been battle-tested on the enterprise scale, so the choice between them is not straightforward.

To break the tie, let’s compare Pinecone, Qdrant, and Weaviate side by side across benchmarks that are critical for successful deployment.

Key features comparison

Let’s examine how three leading vector DBs rank in cost, hosting, and ease of use, which assesses the presence of documentation and the API ecosystem.

Hosting models:

Pinecone: Fully managed
Qdrant & Weaviate: Open-source, self-hosted options

Pricing:

Pinecone: Free plan → $50/month Starter → $500/month Enterprise (full compliance, private networking, encryption keys)
Qdrant: Free 1GB cluster → $0.014/hour hybrid cloud → Custom private cloud pricing
Weaviate: $25/month Serverless → $2.64/AI unit Enterprise → Custom private cloud

How to compare vector database costs?

Managed cloud vendors charge based on data storage and query volume, making them ideal for RAG pilots but expensive at an enterprise scale. Self-hosted solutions require upfront infrastructure investment but offer better long-term economics for high-volume use cases.

Performance (QPS):

Weaviate: 791 queries/second
Qdrant: 326 QPS
Pinecone: 150 QPS (p2 pods)

Search capabilities

Essential vector search features to evaluate across Pinecone, Weaviate, and Qdrant:

Core search features:

Filters to narrow vector-search results according to metadata conditions (e.g., brand=”Nike” AND price < 100). Vector DBs use different filtering strategies. Pre-filtered search applies filters before running a search. Most engineers would not recommend this approach as it can cause the HSNW graph to collapse into disconnected components. Post-filtered search ranks the results after a search has been performed. This filtering strategy is often too rigid, and if there’s no exact output-filter match, the search may return no result at all. Custom filtering, used by Weaviate and Qdrant, enables engineers to fine-tune their filter settings and avoid missing relevant results or compromising the HSNW graph.
Hybrid search blends vector similarity scores with lexical or rule-based scores in a single ranking. This feature gives product, legal, discovery, and support teams a list of results that satisfy both “fuzzy” and exact-match relevance requirements.
Facets return count buckets (color, category, etc.) for drill-down navigation.
Geo search restricts or orders hits by distance from a latitude/longitude point or region. Enterprise teams can use this feature to surface the “nearest store/asset/technician” within the same vector query, meeting location-based SLA and compliance needs (e.g., GDPR data residency or radius-restricted content).

Advanced capabilities:

Multi-vector stores or queries several embeddings per item/query to capture different semantic aspects. If a vector database supports multi-vector search, engineering teams can store multilingual documents, multimodal data, or complex queries (title + body + tags) without duplicating rows.
Sparse vectors support high-dimensional vectors with mostly zero weights (e.g., SPLADE) for better recall on jargon-heavy content like technical documentation or medical records.
BM25: Classic TF-IDF relevance scoring for keyword matches
Full-text search tokenizes and indexes raw text, allowing engineering teams to run phrase, wildcard, or boolean queries.

Search capabilities:  Pinecone vs Qdrant vs Weaviate — Pinecone supports basic vector search capabilities while Qdrant and Weaviate come with robust search toolsets

Content type support

Multi-modal vector search enables understanding relationships between text and non-text data, allowing models to assign captions, labels, or attributes to images and video frames.

Weaviate and Qdrant both support multi-modal search, whereas Pinecone only offers text-based search.

Choosing an optimal vector DB for enterprise use cases

Colin Harman, CTO at Nesh, emphasizes that venture capital rounds or tutorial popularity shouldn’t drive vector database selection. With no clear “market leader,” the right choice is entirely case-by-case.

Best for compliance-heavy industries: Pinecone

Enterprise teams in highly regulated sectors (pharma, banking, government) benefit from Pinecone’s fully managed, compliant infrastructure. It’s the only platform with battle-tested HIPAA compliance among the three options.

Weaviate’s HIPAA capabilities were recently launched, and Qdrant does not have HIPAA/ISO certificates at the time of writing.

Choosing a fully managed database gives enterprise teams the ability to delegate operational controls and audits to the vendor.

Geo coverage: Pinecone offers the broadest reach for GDPR and regional compliance needs due to Integrations with three major cloud vendors (AWS, Azure, and Google Cloud), while Weaviate’s HIPAA scope remains AWS-only, and Qdrant lacks vendor-backed compliance guarantees.

Best for multi-tenant deployment: Qdrant

Global organizations with multiple regional business units need shared capabilities while maintaining department-specific data isolation. Qdrant offers the most comprehensive multi-tenant toolkit of the three platforms.

Qdrant is the optimal choice for use cases that require multi-tenancy — User-defined sharding and binary quantization make Qdrant the frontrunner in multi-tenancy support

Best for integrated RAG workflows: Weaviate

Weaviate’s built-in “generative” module enables direct LLM queries and generated answers from the database itself, while Qdrant and Pinecone require separate codebases for generation.

Enterprise RAG advantages:

Unified platform: Combines vectorizer, retriever, and generator in one managed component for faster time-to-market
Performance boost: Eliminates round trips between LLM, application, and database—reducing response time by 30-60ms per query and cloud costs
Vendor flexibility: Supports multiple providers (OpenAI, Cohere, Databricks) to prevent lock-in

Transform terabytes of unstructured enterprise data into AI-powered knowledge systems

Custom embedding pipelines, real-time synchronization, and production-grade MLOps workflows

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Vector databases are here to stay despite larger context windows

Machine learning engineers have questioned whether expanding LLM context windows will eliminate the need for vector databases, assuming users could share all data directly with GPT, Claude, or Gemini.

However, Victoria Slocum, ML engineer at Weaviate, explains why larger context windows won’t replace vector databases: the concepts serve fundamentally different purposes. Context windows function like human short-term memory—immediately accessible but limited in scope. Vector databases operate more like long-term memory, where retrieval takes time but offers reliable, persistent storage for vast amounts of information.

Scalable RAG infrastructures use both large context windows and embeddings for high LLM recall and output quality — Even with larger context windows, embeddings will stay essential to long term LLM-recall

Even with billion-token context windows, processing massive datasets would remain computationally inefficient and make it harder for models to focus on relevant prompt sections. This computational reality means RAG architectures combining both technologies will continue as the optimal approach for enterprise AI applications.

Within the limits of modern AI infrastructure, RAG is not going anywhere, and vector databases remain essential components of scalable AI systems. For organizations building enterprise AI solutions, vector databases provide the foundation for efficient data retrieval while large context windows handle immediate processing needs.

Rather than competing technologies, they work together to create more effective and economical AI solutions. This makes choosing a vector database that matches your team’s use case, skillset, and budget a critical priority for CTOs and CIOs planning their AI infrastructure investments.