Pinecone is a managed vector database that lets businesses create fast and well-organized systems for similarity searches and recommendations.
The vector database suits business applications requiring real-time feedback and high-dimensional data. The vector data that Pinecone works with is standard in data science and machine learning applications. The data allows for quick search and retrieval of data points or similar items.
Since Pinecone provides real-time performance, it works well for applications such as content personalizations, product recommendations, and various applications requiring quick responses.
The vector database is scalable and can process high query loads and bulky datasets, allowing it to retain efficiency despite your application’s growth.
In the age of the AI revolution, Pinecone provides the database needed to handle vector embeddings generated by Large AI language models.
Most businesses prefer Pinecone because it offers real-time similarity search, can handle various applications, is scalable, and is easy to update and maintain.
Other vector databases for machine learning applications include Qdrant, Weaviate, Faiss, Gravity, Milvus, and more. Though the best Pinecone alternative is Weaviate because it’s user-friendly, open-source, supports hybrid search, and offers flexible pricing.
We will find out more about these alternatives in the upcoming sections.
Why Consider Pinecone Alternatives?
While Pinecone can handle massive datasets at efficient performance, the cost of running the database may increase with an increase in database size.
I like Pinecone’s real-time similarity search functionality, especially for those applications that require real-time decision-making. However, high performance requires specialized software or hardware that could skyrocket Pinecone’s overall cost.
Developers use this tool for various applications due to its flexibility. However, given the degree of technical expertise needed to use the tool, businesses without the technical know-how may find it difficult to operate it.
While updating and maintaining Pinecone is easy, ongoing monitoring and support are essential for the system to perform efficiently.
Additionally, Pinecone is not an open-source vector database, meaning you can’t host your own stance on it.
Let’s discuss the best Pinecone alternatives you may want to consider.
Best Pinecone Alternatives
1. Elastic
Elastic (formerly Elasticsearch) is a versatile and robust open-source analytics and search engine with three key functionalities: security, observability, and search.
Elastic is one of the best Pinecone alternatives with powerful full-text search features. You can use it to categorize and search across massive text data. Applications that prioritize text similarity will find this feature helpful.
Elastic is open-source with an extensive community of active users and developers. In other words, you can access several plugins, resources, and extensions and tailor the search engine to your needs.
If you have an application that requires quick data update and retrieval, consider Elastic, as it provides real-time search and indexing.
Elastic serves clients in various industries, including financial services, automotive, healthcare, technology, retail and eCommerce, telecommunications, manufacturing, and the public sector.
With Elastic, you can also investigate, protect against, or respond to any cyber threat quickly and at scale. It has features such as threat hunting, continuous monitoring, automated threat protection, and incident response to achieve this.
The platform lets you combine infrastructure and app visibility to resolve issues proactively. Features like synthetic monitoring, log monitoring, accurate user monitoring, infrastructure monitoring, and universal monitoring make observability possible.
While Pinecone specializes in search and generative AI, Elastic offers app and infrastructure monitoring (observability) and security functionalities in addition to search.
Elastic has four pricing plans. Standard costs $95/month, Gold is $109/month, Platinum is $125/month, and Enterprise costs $175/month.
Want to try the service before committing? Use their 14-day free trial.
2. Weaviate
Weaviate is a popular vector database that lets you store vector embeddings for ML models and data objects.
The database supports vector search that allows you to use existing vectorization modules or introduce your own vectors. Either way, Weaviate lets you index countless data objects users can search for.
You can perform a hybrid search, too. You can combine different search techniques, like vector and keyword-based, to achieve efficient search experiences.
The third level of search is a generative search, which lets you refine the search results by passing them through large language models (LLM), such as GPT-3, to build a next-generation search experience.
Other than allowing you to import your own vectors, Weaviate lets you select from one of its modules that supports vectorization.
Besides, there’s an option to select from popular neural search frameworks integrated with Weaviate. These include OpenAI, Transformers, Jina AI, and more. Here’s a detailed page of Weaviate’s modules.
Weaviate is an open-source database open to everyone to use from any location. They have plenty of resources for new users to browse through. The resources include an academy, workshop, blog, playbook, and contributor guide.
They also have an active community with many users and developers on their community forum. But that’s not all. Weaviate has a large community on Slack, GitHub, and Twitter.
Their solutions are two-fold: you can use the service as a self-deployed solution (open source) or run it on the cloud using Weaviate Cloud Services (WCS). Pinecone doesn’t offer this dual service.
You can use the platform for free and only start paying for the vector dimensions queried and stored. However, if you choose a monthly subscription, you can start paying from $25/month. Check the rest of their pricing plans here.
3. Qdrant
Qdrant is a vector database that doubles as a vector similarity search engine. It uses an API functionality to provide a search for nearby high-dimensional vectors.
With this database, you can turn neural network encoders or embeddings into entire applications for searching, matching, or recommending.
Qdrant has a user-friendly API that uses OpenAPI v3 to produce a client library in nearly any programming language. With extra functionality, you can also use the ready-made client for Python and other languages.
If you are looking for the best alternative for Pinecone that’s fast and accurate, Qdrant will do. It lets you search incredibly quickly and use filters to get accurate search results.
It supports an extra payload attributed to vectors. That means it doesn’t just store payload but also allows filter results dependent on payload values.
Qdrant supports various query and data types, including string matching, geo locations, and numerical ranges.
You can use payload filtering conditions to create a customized business logic that works and uses similarity matching.
It doesn’t end there. Qdrant is cloud-based and scales horizontally. Regardless of the quantity of data you want to process, you can ensure it has adequate computer power to complete the job efficiently.
Qdrant is designed to work efficiently; it’s built from Rust, a programming language. It organizes your payload data correctly and processes dynamic queries. Besides, it provides hardware-aware versions for enterprise performance. Pinecone doesn’t have such functionalities.
Qdrant has three pricing plans. The Community plan is free, the Managed Cloud starts from $25, and the Enterprise plan price is available on request.
4. Milvus
Milvus is an open-source vector database designed to develop and maintain AI applications. This database stores, indexes, and manages large embedding vectors generated by deep neural networks and other machine learning models.
Milvus is easy to use. It takes less than 60 seconds to build a large-scale similarity search functionality. It also provides straightforward software development kits (SDKs) for various languages.
The vector database’s hardware is powerful with up-to-the-minute index algorithms, allowing it to retrieve data 10x faster.
Like Pinecone, Milvus is highly scalable. Its distributed and efficient throughput makes it suitable for serving enterprise-level vector data.
It’s also cloud-native and feature-rich; it supports different data types, uses vectors to locate similar data, and supports UDF (user-defined function), allowing you to customize your own rules. Plus, you can adjust its consistency and travel through time to see past data.
Do you need assistance getting started or troubleshooting technical issues while using Milvus? Hop to its open-source community with documentation, resources, and support from other users.
Other resources include hosted events, videos, and technical meetings.
What gives Milvus an edge over Pinecone is that it’s 100% free to use, being an open-source project. You are advised to use Apache License 2.0 when using this vector database for production and distribution.
However, If you don’t wish to create and maintain your own distribution instance, you can opt for a managed cloud version of Milvus offered by Zilliz, the organization in charge of Milvus.
5. LanceDB
LanceDB is a vector database designed for developers. It’s open-source, developer-friendly, and needs zero management overhead.
LanceDB is super lightweight and helps you scale from development through production.
It supports powerful and flexible search. You can query your data by combining full text, vector, and SQL. You can also use this vector database to query and store anything, from documents to embeddings and metadata.
LanceDB allows scaling without affecting performance or increasing your budget. You can increase storage from gigabytes to terabytes and more without worrying about speed. On the other hand, Pinecone may require additional costs for extra database size.
It gets better.
LanceDB is serverless. So, instead of managing separate servers, this database’s open-source software is cloud-based and functions while embedded into your app.
Besides, LanceDB’s APIs run smoothly with the growing Javascript and Python ecosystem. This feature lets you use DataFrames to manipulate data, Pydantic to build models, and LanceDB to query and store data.
LanceDB has an active community on Discord and Twitter. You can also find many guides on its blog, documentation, and GitHub.
The best part? LanceDB is 100% free of charge. It’s easy to use, too!
6. Marqo
Marqo is an open-source end-to-end vector database and search engine that supports vector generation, retrieval, and storage. It handles all these functionalities via a single API.
Marqo supports state-of-the-art embeddings using modern machine-learning models from OpenAI, HuggingFace, PyTorch, and more.
It allows you to bring your own model or use a pre-configured one. Also, the embeddings have an integrated ONNX support and conversion for higher throughput and quicker inference. And users love the vector engine’s support for both GPU and CPU.
Marqo’s performance is top-notch, too. Embeddings are stored in the built-in memory HNSW indexes to boost search speeds.
The horizontal index sharding lets you scale to over 100 million indexes. Besides, Marqo supports searching and uploading async and non-blocking data.
It combines weighted search terms, allowing you to create complex queries. Plus, you can use your images and text to build search, data exploration, and entity resolution applications.
Filtering search results in Marqo is easy, too; use the query DSL. Check out this guide on how to use Marqo.
Here’s another impressive feature you won’t find in Pinecone: Marqo allows you to store semi-structured and unstructured metadata close to each other in documents with the help of various data types, such as keywords and bools.
The vector database is cloud-managed with low-latency deployment. It’s also free for anyone to use.
If you’re looking for a vector database optimized for various production tasks without paying for operations, Marqo is a suitable managed and serverless cloud-based database.
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7. Epsilla
Epsilla is a vector database that delivers vector search’s high performance, scalability, and high performance.
The open-source vector database fills the gap between memory retention and information retrieval often seen in large language models.
Epsilla is known to deliver production-scale and high-performance similarity search for embeddings.
It has a complete database management system featuring a familiar database, field concepts, and a table. Vectors are part of the field types.
Epsilla is built with C++, and its vector indexing uses an up-to-date academic parallel graph traversal method. This technique achieves a ten times quicker vector search than the standard Hierarchical Navigable Small World (HNSW), with at least 99.9% precision levels.
While Pinecone is also built with C++ combined with Python, it doesn’t use the academic parallel graph traversal method in vector indexing.
The website says it’s a cheaper service, but the actual cost isn’t indicated. However, you can complete a Google form to get a customized cost of Epsilla Cloud for your business needs.
Also Read: Best Open-source Data Analytics Tools
8. pgvector
pgvector is an extension for PostgreSQL that can perform vector similarity searches. You can use this open-source extension to store embeddings, too.
Vector similarity is the degree of similarity between items that look alike. Suppose you have various related items; you can find similar items using vector similarity.
To achieve this, use a mathematical model to convert the items into a vector of numbers. The same model can apply to images, text, and other data types. After storing all the vectors in a database, use pgvector’s vector similarity to locate similar items.
Embeddings are essential if you build with large language models, such as GPT-4 or GPT-3. pgvector lets you build and store embeddings. You can retrieve the embeddings later for augmented generation.
pgvector has unique capabilities to identify the approximate and exact nearest neighbors. It has been designed to work efficiently with other PostgreSQL features like querying and indexing.
Being a free and open-source application makes pgvector more accessible than Pinecone.
The most significant benefit of pgvector over a specialized vector database like Pinecone is that it can keep all your data in the same database. This makes activities like creating new databases, switching to separate solutions, and building new operational protocols unnecessary.
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9. NucliaDB
NucliaDB is a powerful database to store and search unstructured data. This out-of-the-box database uses vector, graph, and full-text indices.
It’s built-in Python and Rust programming languages to offer multi-tenant support and index massive datasets.
This robust database stores vectors, text, labels, annotations, and files. On the other hand, Pinecone specializes in vector similarity search, with an emphasis on high-dimensional vector data. A text search using a word or a list of words will return resources containing them.
You can then export the data in a format that works with Natural Language Processing (NLP) pipelines, such as Pytorch, HuggingFace datasets, and more.
NucliaDB lets you store extracted information, original data, and data from the Nuclia Understanding API. It provides indexing for paragraphs, semantic sentences, and fields for seamless organization and retrieval of data.
It integrates with cloud services, such as Nuclia Learning API and Nuclia Understanding API to enhance machine learning-based training and data extraction from the cloud.
The database consists of role-based security features that support authentication via upstream proxies. This security system guarantees data protection and regulated access.
NucliaDB also accommodates different field types, such as file, text, conversation, link, and layout, enhancing diverse storage capabilities.
Both NucliaDB and Pinecone support blob storage compatible with GCS or S3 drivers.
10. Faiss
Faiss is a Facebook similarity search and clustering library for dense vectors. Its algorithms can search vectors in sets of varying sizes, including those that can’t fit in RAM.
Faiss also has supporting code to evaluate and fine-tune parameters. It’s written entirely in C++ and offers comprehensive numpy/Python wrappers.
It provides various similarity search methods and assumes that vectors represent instances, and integers identify them.
Dot products or L2 (Euclidean) distances can help compare these vectors. Vectors similar to a query vector have the highest dot product or lowest Euclidean distance. You can use Faiss for cosine similarity, as well.
Faiss uses some methods that don’t require storing original vectors but instead rely on the vectors’ compressed representations. Examples of these methods include compact quantization codes and binary vectors.
Although such methods may lack accuracy in search results, they can process billions of vectors on a single server’s primary memory. Other methods like NSG and HNSW add indexing structures on raw vectors for efficient searching.
Faiss has a versatile GPU implementation; it can process input from GPU and CPU memories.
Being open-source gives Faiss an advantage over Pinecone. You can seek assistance from Faiss’s community and detailed documentation.
Also, Faiss is more versatile than Pinecone, as it can work with a broad array of data types, such as numerical vectors. This versatility makes it suit more applications than Pinecone.
11. Chroma
Chroma is one of the best open-source Pinecone alternatives to store and retrieve vector embeddings. Chroma’s primary use is saving embeddings with their metadata so that large language models can use them later.
But that’s not all. Chroma supports semantic search over full-text search. Also, it supports various storage options, such as ClickHouse for scalability and DuckDB for standalone storage.
It offers software development kits (SDKs) for Javascript and Python, emphasizing speed, simplicity, and analytics.
To use Chroma, start by setting up a collection, like you’d do tables in a standard relational database. Chroma’s default operation involves using the all-MiniLM-L6-v2 model to convert text into embeddings. However, you can still customize the collection using a different embedding model.
Once the collection is ready, start adding text documents with unique IDs and metadata. As you add text to your collection, it turns into embeddings automatically.
Incredible, right?
Chroma is free with many features, such as filtering, queries, density estimation, and others.
Check out its source code on GitHub. Their documentation has valuable guides on migration, getting started, deployment, troubleshooting, etc. You can also interact with other Chroma enthusiasts on their Discord server.
12. txtai
txtai is an embeddings database focusing on semantic search, language model workflows, and LLM orchestration.
As an embeddings database, txtai is a combination of vector indexes, relational databases, and graph networks. This union enhances vector search via topic modeling, SQL, retrieval augmented generation, etc.
That said, txtai can operate independently, serving as a robust knowledge source for various LLM (large language model) prompts.
txtai supports vector search with SQL, topic modeling, multimodal indexing, graphic analysis, and object storage.
You can use it to build embeddings for audio, video, text, documents, and images.
Its pipelines use language models running on LLM prompts, labeling, translation, summarization, transcription, question-answering, and more.
You can build applications using YAML or Python, and access your API via bindings in Java, Go, JavaScript, and Rust.
You can run the database locally or use container orchestration to scale it for cloud deployment.
Additionally, you can combine pipelines and aggregate business logic to create customized workflows. txtai works with both simple and complex workflows.
Like Pinecone, txtai is a vector database that lets you write data to various vector index formats like HNSWLib and Faiss. However, it goes beyond that because it can use another vector database for its file format.
Besides, its graph component supports data transformation via workflow and topic modeling.
Conclusion
Of all the Pinecone alternatives above, Weaviate stands out for me. Weaviate is open-source, allowing you to customize it for your needs and access a community for help.
It allows you to bring in vectors or choose from its modules. Weaviate also supports a hybrid search, allowing users to combine search methods for a better outcome.
Not only is it easy to use but also offers ample help resources. You can also choose to use its open-source self-deployed option or the cloud-based one.
The pricing of Weaviate is flexible as well. You can use it for free, pay for queries, or subscribe.
