Vector databases power the retrieval layer in RAG workflows by storing document and query embeddings as high‑dimensional vectors. They enable fast similarity searches based on vector distances.
We benchmarked six vector database providers, focusing on their pricing structures and performance:
Vector database comparison: Pricing & performance
In this benchmark, we used:
- 1 million vector dataset from Cohere, where each vector has 768 dimensions.
- Vector compression techniques, using binary quantization for Weaviate, Elasticsearch, Zilliz, and MongoDB Atlas, and product quantization for Pinecone, to reduce memory and disk usage.
- Evaluated latency according to our vector database benchmark methodology.
Estimated monthly costs are approximations based on certain assumptions and publicly available pricing at the time of writing. Actual costs will vary based on specific usage, configuration, data size, and current vendor pricing.
Vector database storage calculator
Use the calculator to estimate the number of vectors and storage required for a vector database based on input data size, embedding dimension, and chunk size:
Embedding Dimension:
- The number of numerical values (features) in each vector that represents a piece of text.
- Example: A dimension of 1536 means each vector has 1536 numbers, capturing the text’s meaning. Higher dimensions increase detail but require more storage.
Chunk Size:
- The number of tokens (words or punctuation) in each text segment is processed into a single vector.
- Example: A chunk size of 512 means each vector represents 512 tokens. Smaller chunks create more vectors, while larger chunks reduce the vector count but may lose detail.
The calculator uses the following assumptions and calculations:
- We use 4 bytes per token, a standard average for English text based on UTF-8 encoding and tokenizers like OpenAI’s tiktoken.
- Each vector’s size is calculated as the embedding dimension (e.g., 1536) multiplied by 4 bytes (since vectors use float32 values, which are 4 bytes each).
These calculations provide a general estimate to help plan vector database usage. For accurate results, preprocess your text using a specific tokenizer and consult the documentation for your vector database.
Elasticsearch
Vector search is integrated into the widely used Elasticsearch search and analytics engine. Leverages the mature ELK stack ecosystem, offering powerful filtering, aggregation, and combined keyword + vector (hybrid) search. Ideal if already using Elasticsearch.1
MongoDB Atlas
MongoDB Atlas’ vector search feature allows you to store and query vectors directly in MongoDB along with other application data. This simplifies the technology stack, especially for existing MongoDB users, making it easier to integrate AI and similar advanced applications.2
Qdrant cloud
Managed service for the open-source Qdrant database. Known for advanced filtering (pre-filtering), quantization, multi-tenancy, and resource-based pricing for performance tuning.3
Pinecone
A managed, cloud-native vector database focusing on ease of use, serverless scaling, and low-latency search. Offers a simple API and usage-based pricing.4
Weaviate cloud
Managed service for the open-source Weaviate database. Known for its GraphQL API, optional vectorization modules, and strong hybrid search capabilities. Storage-based pricing offers predictability.5
Zilliz cloud
Zilliz is the managed cloud service for the popular open-source Milvus vector database. It focuses purely on high-performance vector search and scalability, offering tunable consistency and various index types. It’s designed for demanding vector workloads.6
What is a vector database?
A vector database is designed to store data in vector format and perform real-time or near-real-time similarity queries. Text, images, or other data types are typically transformed into embedding vectors via deep learning models (e.g., language models). The database then uses specialized indexing structures (HNSW, IVF, etc.) to efficiently retrieve nearest neighbors based on these vector representations.
This approach enables tasks such as semantic search, for instance, matching a query with the most semantically similar documents or images.
Advantages of vector databases
Vector databases are essential, especially for AI applications like RAG:
- Efficient Similarity Search: Their core strength lies in finding vectors (representing data like text, images, or audio) that are “closest” or most similar in meaning or content, going beyond simple keyword matching.
- Handling High-Dimensional Data: Traditional databases struggle with the complexity and dimensionality of vector embeddings generated by modern AI models. Vector databases are architected specifically for this challenge.
- Scalability: They are designed to scale efficiently, handling billions of vectors while maintaining fast query performance, which is crucial as datasets grow.
- Semantic Understanding: By searching based on vector proximity, they enable applications to understand the semantic meaning or context of data, leading to more relevant results in search, recommendations, and RAG context retrieval.
- Powering AI Features: They are a fundamental building block for features like semantic search, image search, recommendation engines, anomaly detection, and, importantly, providing relevant context to large language models (LLMs) in RAG pipelines.
Choosing the right platform
Selecting the ideal vector database involves balancing performance, cost, and features against your specific RAG application requirements.
- Performance Needs (Latency & Throughput): How critical is sub-100ms latency? What is your expected query volume? Our benchmark results showed Zilliz leading in raw latency under test conditions, with Pinecone and Qdrant also being competitive. Test under your expected load.
- Budget and Cost Predictability: How does each pricing model fit your budget? Elasticsearch’s example cost was the lowest, but it depends heavily on usage. Weaviate is storage-based and predictable, but it may have a higher cost. Qdrant is resource-based, offering tuning but requiring careful tier selection. Factor in the 768-dimension assumption used in the cost calculation – different dimensions will change expenses, especially for Qdrant and Pinecone.
- Scalability Requirements: How large is your dataset expected to grow? How will the query load increase? Evaluate the scaling mechanisms and associated costs for each platform.
- Required Features: Do you require specific filtering logic, integrations, or data import/export capabilities? Compare the detailed feature lists.
- Developer Experience & Ecosystem: How easy are the SDKs and APIs to use? How good is the documentation and community support?
- Operational Overhead: Are you looking purely for a managed service, or is the option for self-hosting (available for Qdrant/Weaviate cores) potentially interesting?
Vector database benchmark methodology
To provide a fair comparison, we standardized our benchmark approach:
- Dataset: We used a 1 million vector dataset from Cohere, where each vector has 768 dimensions. This text-based embedding set is representative of common RAG use cases and suitable for similarity search benchmarks.
- Metric: We focused on average query latency (in milliseconds) for a nearest neighbor search. Lower latency indicates faster search performance.
FAQ
Why are vector databases so important for Retrieval-Augmented Generation (RAG) systems, and how do they handle the underlying data?
Vector databases play a crucial role in Retrieval Augmented Generation (RAG) because RAG systems need to efficiently find the most relevant context to feed into generative models. They are specifically designed to manage vector data – numerical representations (embeddings) derived from unstructured data like text documents via an embedding model. This allows for powerful vector similarity search.
Instead of just keyword matching, they perform semantic vector retrieval based on meaning, finding similar vectors even if the wording differs. This process is fundamental to the retrieval augmented generation rag workflow, improving response accuracy by providing better context from potentially large volumes of information, including existing data or ingested new data, effectively handling various data types used in natural language processing and other AI tasks.
What technical factors influence fast vector similarity search performance when comparing the top vector databases?
Achieving fast vector similarity search needed for scalable similarity searches across potentially big data volumes relies heavily on sophisticated indexing methods like HNSW or IVF. These methods use Approximate Nearest Neighbors (ANN) algorithms to quickly find close matches in high dimensional vector data without scanning the entire dataset.
Key factors impacting system performance and retrieval speed include the specific index configuration (which affects index size and memory consumption), the chosen distance metrics for measuring vector similarity, and the efficiency of handling real time processing if needed. Maximum performance often involves trade-offs between speed, accuracy, and resource usage, necessitating performance tests tailored to the specific workload.
What are the trade-offs between using an open source vector database versus a managed cloud service or integrated options?
Choosing the right vector database involves considering options like dedicated platforms (many with open source databases at their core, like Qdrant or Weaviate) versus integrated solutions. Open source vector database options can offer more control, potentially reduce vendor lock in, and allow for deep customization, including adding custom modules. However, they usually require more operational effort.
Managed services provide seamless integration, handle infrastructure, and often include robust data security measures, but might offer less granular control. Integrated solutions simplify the stack if you’re already using the parent platform. Evaluating key features like metadata filtering capabilities, active development pace, and ease of use for relevant machine learning tasks is crucial for making a cost effective decision.
How does metadata filtering work within a vector search engine, and why is it critical for refining the retrieval process?
Metadata filtering allows you to constrain the vector similarity search to only a subset of your vector data based on associated attributes stored alongside each data point (e.g., dates, categories, user IDs). Instead of just finding the closest vectors globally, you can ask for the closest vectors that also match specific metadata criteria. Some databases perform this filtering before the ANN search (pre-filtering), which can dramatically increase retrieval speed and relevance for queries on large volumes of data compared to filtering after retrieving neighbors (post-filtering). This capability is essential for building sophisticated applications where context beyond vector similarity is needed, directly impacting the effectiveness of the retrieval process in RAG systems.
What data-related considerations are important when selecting a vector database, particularly concerning machine learning models and data types?
Selecting the right vector database requires considering how it handles various data types and integrates with your machine learning pipeline. The embedding model you choose dictates the dimensionality and characteristics of your vector data. The database must efficiently store and index these vectors.
Consider its support for managing the original unstructured data alongside vectors, its scalability for large volumes generated by generative AI, and its features for managing new data ingestion. Ensuring good data security practices and understanding how the database interacts with your existing data infrastructure are also vital for a successful, cost effective, and performant implementation, supporting tasks from basic vector retrieval to complex machine learning tasks.
External Links
- 1. Elasticsearch: The Official Distributed Search & Analytics Engine | Elastic.
- 2. Atlas Database | MongoDB.
- 3. Qdrant Cloud: Scalable Managed Cloud Services - Qdrant.
- 4. The vector database to build knowledgeable AI | Pinecone.
- 5. The AI-native database developers love | Weaviate.
- 6. Zilliz Cloud, a managed vector database built on Milvus®.
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