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Pinecone vs Aerospike: Selecting the Right Database for GenAI Applications

Pinecone vs Aerospike: Selecting the Right Database for GenAI Applications

Oct 18, 20249 min read

As AI-driven applications evolve, the importance of vector search capabilities in supporting these advancements cannot be overstated. This blog post will discuss two prominent databases with vector search capabilities: Pinecone and Aerospike. Each provides robust capabilities for handling vector search, an essential feature for applications such as recommendation engines, image retrieval, and semantic search. Our goal is to provide developers and engineers with a clear comparison, aiding in the decision of which database best aligns with their specific requirements.

What is a Vector Database?

Before we compare Pinecone vs Aerospike, let's first explore the concept of vector databases.

A vector database is specifically designed to store and query high-dimensional vectors, which are numerical representations of unstructured data. These vectors encode complex information, such as the semantic meaning of text, the visual features of images, or product attributes. By enabling efficient similarity searches, vector databases play a pivotal role in AI applications, allowing for more advanced data analysis and retrieval.

Common use cases for vector databases include e-commerce product recommendations, content discovery platforms, anomaly detection in cybersecurity, medical image analysis, and natural language processing (NLP) tasks. They also play a crucial role in Retrieval Augmented Generation (RAG), a technique that enhances the performance of large language models (LLMs) by providing external knowledge to reduce issues like AI hallucinations.

There are many types of vector databases available in the market, including:

Purpose-built vector databases such as Milvus, Zilliz Cloud (fully managed Milvus)
Vector search libraries such as Faiss and Annoy.
Lightweight vector databases such as Chroma and Milvus Lite.
Traditional databases with vector search add-ons capable of performing small-scale vector searches.

Pinecone is a purpose-built vector database and Aerospike is a distributed, scalable NoSQL database with vector search capabilities as an add-on. This post compares their vector search capabilities.

Pinecone: The Basics

Pinecone is a SaaS built for vector search in machine learning applications. As a managed service, Pinecone handles the infrastructure so you can focus on building applications not databases. It’s a scalable platform for storing and querying large amounts of vector embeddings for tasks like semantic search and recommendation systems.

Key features of Pinecone include real-time updates, machine learning model compatibility and a proprietary indexing technique that makes vector search fast even with billions of vectors. Namespaces allow you to divide records within an index for faster queries and multitenancy. Pinecone also supports metadata filtering, so you can add context to each record and filter search results for speed and relevance.

Pinecone’s serverless offering makes database management easy and includes efficient data ingestion methods. One of the features is the ability to import data from object storage, which is very cost effective for large scale data ingestion. This uses an asynchronous long running operation to import and index data stored as Parquet files.

To improve search Pinecone hosts the multilanguage-e5-large model for vector generation and has a two stage retrieval process with reranking using the bge-reranker-v2-m3 model. Pinecone also supports hybrid search which combines dense and sparse vector embeddings to balance semantic understanding with keyword matching. With integration into popular machine learning frameworks, multiple language support and auto scaling Pinecone is a complete solution for vector search in AI applications with both performance and ease of use.

Aerospike: The Basics

Aerospike is a NoSQL database for high-performance real-time applications. It has added support for vector indexing and searching so it’s suitable for vector database use cases. The vector capability is called Aerospike Vector Search (AVS) and is in Preview. You can request early access from Aerospike.

AVS only supports Hierarchical Navigable Small World (HNSW) indexes for vector search. When updates or inserts are made in AVS, record data including the vector is written to the Aerospike Database (ASDB) and is immediately visible. For indexing, each record must have at least one vector in the specified vector field of an index. You can have multiple vectors and indexes for a single record so you can search on the same data in different ways. Aerospike recommends assigning upserted records to a specific set so you can monitor and operate on them.

AVS has a unique way of building the index, it’s concurrent across all AVS nodes. While vector record updates are written directly to ASDB, index records are processed asynchronously from an indexing queue. This is done in batches and distributed across all AVS nodes, so it uses all the CPU cores in the AVS cluster and is scalable. Ingestion performance is highly dependent on host memory and storage layer configuration.

For each item in the indexing queue, AVS processes the vector for indexing, builds the clusters for each vector and commits those to ASDB. An index record contains a copy of the vector itself and the clusters for that vector at a given layer of the HNSW graph. Indexing uses vector extensions (AVX) for single instruction, multiple data parallel processing.

AVS queries during ingestion to “pre-hydrate” the index cache because records in the clusters are interconnected. These queries are not counted as query requests but show up as reads against the storage layer. This way, the cache is populated with relevant data and can improve query performance. This shows how AVS handles vector data and builds indexes for similarity search so it can scale for high-dimensional vector searches.

Key Differences

When choosing a vector search tool, knowing the difference between Pinecone and Aerospike will help you make a decision. Both do vector search but are different. Let’s compare these across key features to help you pick the right one for you.

Search Methodology

Pinecone has a proprietary indexing method for vector search, so queries are fast even with billions of vectors. It supports real-time updates and has metadata filtering to refine results.

AVS (Aerospike’s Vector Search) uses Hierarchical Navigable Small World (HNSW) indexes for approximate nearest neighbor search. This in-memory index is good for high dimensional vector spaces and fast similarity search.

Data

Pinecone is designed for vector data, so it is perfect for machine learning and GenAI applications working with embeddings. You can store vector data along with metadata.

Aerospike is a NoSQL database that has added vector search to its existing data management features. So it can store both vector data and traditional structured and semi-structured data in one place.

Scalability and Performance

Pinecone is designed for horizontal scaling, it can handle large datasets. Its cloud native architecture allows for auto scaling based on workload.

Aerospike is high performance, distributed. Its vector search is built on top of this. It can handle large scale, high throughput workloads. Aerospike’s indexing is concurrent across all AVS nodes which makes it scalable.

Flexibility and Customization

Pinecone has customization through API and supports multiple vector dimensions. It has namespace for data organization and search parameters adjustment.

Aerospike has flexibility in data modeling and indexing. It can have multiple vectors and indexes per record, so you can search the same data in different ways.

Integration and Ecosystem

Pinecone integrates with popular machine learning frameworks and cloud services. It has SDKs for multiple languages, so you can use it in any development environment.

Aerospike has a wide range of connectors and integrations including Kafka, Spark and multiple languages. You can use vector search along with other database features.

Ease of Use

Pinecone as a managed service handles most of the operational complexity. It has a simple API and good documentation so it’s easy to learn for developers new to vector search.

Aerospike requires more setup and management, especially for on-premises. While it has good documentation, managing both traditional and vector data adds to the learning curve.

Cost

Pinecone has a pure pay as you go model based on the number of vectors stored, writes and reads. As a managed service, it reduces operational costs of infrastructure management.

Aerospike’s pricing is usually based on data and nodes. While it may require more upfront investment in infrastructure and management, it’s cost effective for large scale deployments with diverse data.

Security Features

Pinecone has encryption in transit and, at rest, role-based access control (RBAC) and supports SSO for enterprise customers.

Aerospike has strong security features including encryption, authentication and fine grained access control. It also supports enterprise security standards and compliance.

When to Use Each

Pinecone is best for projects that are mostly about vector search, especially in machine learning and AI. It’s great for use cases like semantic search, recommendation systems and image similarity search where you need to handle large volumes of vector embeddings. Pinecone’s managed service model is perfect for teams that want to build applications not manage infrastructure. Realtime updates and hybrid search (vector and keyword matching) makes it good for a wide range of AI use cases.

Aerospike is best for projects that need a more general purpose database with vector search. It’s good for use cases that involve both traditional data types and vector data like real-time fraud detection systems that combine transactional data with behavior vectors or content management systems that need both full-text search and semantic similarity. Aerospike’s high performance architecture makes it good for applications that need low latency at scale especially when you need to handle both vector and non-vector data in one platform.

Summary

Pinecone is great for a specialized managed vector search solution with high performance for large scale vector operations, easy scalability and integration with machine learning workflows. Aerospike is a more general purpose database that combines NoSQL with vector search for complex high performance applications that handle diverse data types. Your choice between these two should be based on your use case, data types, performance requirements and team expertise. Use Pinecone if your main focus is on vector search and you want a managed service, use Aerospike if you need a more flexible database that can handle both vector and non-vector data at scale.

Read this to get an overview of Pinecone and Aerospike but to evaluate these you need to evaluate based on your use case. One tool that can help with that is VectorDBBench, an open-source benchmarking tool for vector database comparison. In the end, thorough benchmarking with your own datasets and query patterns will be key to making a decision between these two powerful but different approaches to vector search in distributed database systems.

Using Open-source VectorDBBench to Evaluate and Compare Vector Databases on Your Own

VectorDBBench is an open-source benchmarking tool for users who need high-performance data storage and retrieval systems, especially vector databases. This tool allows users to test and compare different vector database systems like Milvus and Zilliz Cloud (the managed Milvus) using their own datasets and find the one that fits their use cases. With VectorDBBench, users can make decisions based on actual vector database performance rather than marketing claims or hearsay.

VectorDBBench is written in Python and licensed under the MIT open-source license, meaning anyone can freely use, modify, and distribute it. The tool is actively maintained by a community of developers committed to improving its features and performance.

Download VectorDBBench from its GitHub repository to reproduce our benchmark results or obtain performance results on your own datasets.
Take a quick look at the performance of mainstream vector databases on the VectorDBBench Leaderboard.
Read the following blogs to learn more about vector database evaluation.

Further Resources about VectorDB, GenAI, and ML

Chloe Williams
Chloe Williams is a technical writer at Zilliz.

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