Vector search has become increasingly important with the rise of large language models and neural embeddings. The ability to find semantically similar content based on vector representations of text is a key capability needed to build applications like chatbots, search engines, and recommender systems.

Two popular open-source solutions for vector search are ChromaDB and Elasticsearch. Both can be used to store and query vector embeddings, but have different architectures, capabilities, and use cases.

In this article, we'll do a technical dive into ChromaDB and Elasticsearch to understand their approaches to vector search and how they compare.

Overview of ChromaDB

ChromaDB is an open-source vector database purpose-built for storing and querying neural embeddings. It is designed specifically for use cases like semantic search, clustering, and nearest neighbors search.

Some key aspects of ChromaDB:

  • Written in Rust for performance and efficiency.
  • Supports different persistence backends like SQLite, Apache Arrow Flight, and local filesystem. This allows tuning for different usage patterns.
  • Multi-threaded query processing for fast response times.
  • SIMD-optimized similarity search using algorithms like HNSW for low latency.
  • Python, Java, and Rust APIs for easy integration.
  • Very lightweight and simple API. Primary objects are Collections and Documents. For example:
  • Auto-vectorization of text content using models like sentence-transformers.
  • Active development and community.

ChromaDB is best suited for applications where fast vector similarity search is critical, data fits on a single machine, and simplicity is preferred over a feature-rich API.

Overview of Elasticsearch

Elasticsearch is a general purpose search and analytics engine. It powers many search experiences but can also be used for vector search workloads.

Some key aspects of Elasticsearch:

  • Implemented in Java and accessible through REST APIs.
  • Distributed architecture that scales horizontally across nodes.
  • Advanced search features like full-text search, aggregations, geo-search.
  • Can be extended through custom analyzers, tokenizers, and plugins.
  • Node-to-node communication uses binary protocol over HTTP.
  • Documents stored as JSON blobs that can nest complex data.
  • Open source under Apache license but also has commercial versions.

Elasticsearch excels at text search workloads and analytics over complex data. For vector search, it requires more configuration but can scale better due to its distributed nature.

Vector Storage

At their core, both ChromaDB and Elasticsearch are able to ingest and store vector data. However, their approaches differ.

ChromaDB only deals with vectors and numeric data. It expects vectors to be passed directly when adding documents. Text is auto-converted to vectors using sentence transformer models. Vectors are optimized for similarity search and stored in columnar format.

In Elasticsearch, vectors have to be modeled as dense_vector or sparse_vector data types within JSON documents. This provides flexibility to store heterogeneous data but also adds overhead. Vectors are stored in lucene indexes like other text fields.

This means ingesting vector data is simpler in ChromaDB but Elasticsearch provides more flexibility in data modeling.

Similarity Search

Searching vectors by similarity is optimized heavily in ChromaDB. It uses purpose-built indexing structures like HNSW graph.

There are no scoring concerns - retrieval is purely by vector distance.

Elasticsearch uses sparse/dense vector field types so vectors can be queried as-is. But the default scoring and ranking models in Elasticsearch are meant for text search. To rank purely by vector similarity requires customization of similarity algorithms and disabling text-centric features.

So out-of-the-box, ChromaDB provides simpler and faster similarity search of vectors. In Elasticsearch, tuning is needed to optimize vector search instead of text search workloads.

Scale and Performance

ChromaDB runs on a single node and scales vertically. It can leverage multiple CPU cores and large RAM sizes. Data is stored on local disk or attached storage.

Elasticsearch scales horizontally across multiple nodes and handles sharding and replication automatically. But there is coordination overhead between nodes that can impact latency.

For pure vector search on a single machine, ChromaDB provides better performance. But Elasticsearch can scale to handle much larger datasets and throughput by adding more nodes.

Result Pagination

ChromaDB retrieves the top-N most similar vectors to a query, ranked by distance. Fetching additional results requires a new query.

Elasticsearch search hits include a scroll ID that allows "paging" through results. So initial query can retrieve top-N hits, but subsequent API calls can paginate through the rest of the sorted results.

This gives Elasticsearch more flexibility if applications need to process a large number of similar results.


ChromaDB is purpose-built for simple vector similarity search, optimized for performance on a single machine. It has simple data ingestion and query APIs that make it easy to get started with vector search.

Elasticsearch handles vector data as a special case of its more generalized search capabilities. It requires more configuration for optimal vector search performance. But its distributed nature, rich API, and surrounding ecosystem enable building large-scale search apps.

The best option depends on the specific application needs around vector search and the scale of data and users. But both ChromaDB and Elasticsearch are open-source options that can enable vector search in different ways.

1. What is vector search and why is it useful?

Vector search allows finding semantically similar items based on vector representations of the data. This is useful for applications like search engines, recommender systems, chatbots etc. where you want to match user queries or items against a dataset to find the most relevant results.

Vectors capture semantics and positioning of words/items in a multi-dimensional space. So similarity between vectors implies semantic similarity between the actual texts or items. This allows matching queries to documents, products to user interests etc. much better than just keyword matching.

2. How does ChromaDB perform vector search?

ChromaDB uses vector indexes optimized specifically for similarity search like HNSW graphs. At ingestion time, data like text is converted to dense vectors using models like sentence transformers.

At query time, the query is also encoded into a vector. Then highly optimized vector similarity algorithms are applied to find the closest vectors in the index to the query vector.

This results in very fast and accurate vector search right out of the box without complex tuning.

3. How does Elasticsearch perform vector search?

In Elasticsearch, vectors are stored as dense_vector or sparse_vector data types within JSON documents. On queries, these vector fields can be searched using cosine similarity or other distance functions.

However, the default scoring and relevance models in Elasticsearch are optimized for full-text search, not vector search. To properly rank results by vector similarity, additional configuration is required like:

  • Setting custom similarity algorithm for the vector field
  • Disabling text-centric scoring factors like BM25
  • Tuning similarity query performance with caching

So Elasticsearch provides the capability, but requires tuning to match the purpose-built vector search optimizations of ChromaDB.

4. When is ChromaDB a better choice over Elasticsearch?

ChromaDB is better suited for applications where blazing fast vector search is critical down to millisecond latencies. For example, real-time recommendations, chatbots, or vector-based search.

It works great out-of-the-box on a single machine. Simple ingestion of vectors and querying makes it easy to get started.

5. When is Elasticsearch a better choice over ChromaDB?

Elasticsearch is better if you need:

  • Ability to scale to huge datasets by distributing across multiple nodes
  • Full text search capabilities along with vector search
  • More complex query capabilities like filters, aggregations etc.
  • Visualizations and reporting with Kibana integrations
  • Commercial support and enterprise features

So if you have existing investment and expertise in Elasticsearch, need to combine various kinds of search, or require complex analytics, then Elasticsearch is a stronger choice.

6. Can ChromaDB and Elasticsearch be used together?

Absolutely! A common pattern is to use ChromaDB for ultra-fast vector lookups and retrievals. Then fetch the full source documents from Elasticsearch to display search results.

This takes advantage of ChromaDB's speed while leveraging Elasticsearch's features around document storage, text search, and analytics.

7. How does data ingestion differ between ChromaDB and Elasticsearch?

ChromaDB only deals with vectors so data ingestion is simpler - vectors can be directly added to collections without much encoding. Text is automatically vectorized.

Elasticsearch accepts JSON documents, so vectors and metadata need to be modeled as JSON containing dense_vector fields. This provides more flexibility for complex data but the ingestion code is more involved.

8. How do the query APIs differ?

ChromaDB query API is very minimal - just pass the search vectors and optionally filters. Elasticsearch exposes the entire Query DSL over REST API for very sophisticated queries.

So ChromaDB makes basic similarity search easy while Elasticsearch offers more power and control.

9. How does the scale and performance compare?

ChromaDB focuses on optimizing vector search on a single machine with multiple CPU cores and large RAM. It can handle billions of vectors on one box.

Elasticsearch scales horizontally and can handle trillions of documents across a cluster. But there is overhead to coordinate across nodes that can impact latency.

For pure vector search, ChromaDB provides better latency. But Elasticsearch scales much bigger across nodes.

10. What are some advanced features that Elasticsearch has over ChromaDB?

  • Alerting, monitoring, security features
  • Spark integration for big data pipelines
  • Graph and geospatial capabilities
  • Commercial licensing and support options
  • External ecosystem of tools like Kibana

So Elasticsearch provides an extensive platform and tooling for big search applications. ChromaDB does one thing - vector search - but does it blazingly fast on a single machine.

Rasheed Rabata

Is a solution and ROI-driven CTO, consultant, and system integrator with experience in deploying data integrations, Data Hubs, Master Data Management, Data Quality, and Data Warehousing solutions. He has a passion for solving complex data problems. His career experience showcases his drive to deliver software and timely solutions for business needs.

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