What Is OpenSearch and how does it support AI workloads?

OpenSearch is an open source search and analytics suite that originated as a fork of Elasticsearch and Kibana. The suite includes a scalable search engine, a visualization interface, and plugins that extend its functionality, especially for security, machine learning, and alerting. OpenSearch is compatible with Elasticsearch 7.10.2 APIs and is distributed under the Apache 2.0 license.

OpenSearch supports features suitable for building and scaling AI and machine learning applications:

  • Integrated vector database: Enables low-latency similarity searches using k-nearest neighbors (k-NN). This allows developers to perform searches based on the contextual meaning of documents, images, or audio, rather than relying solely on exact keyword matches.
  • Neural search capabilities: Improves the relevancy of results for natural language queries. These capabilities are powered by OpenSearch’s extensible ML framework, which supports pre-trained models, custom models, and connections to externally hosted models. This flexibility allows teams to operationalize AI quickly.
  • Production-grade reliability: Delivers high performance at scale, supporting tens of billions of vectors with consistent low latency.
  • Filtering techniques and vector quantization: These help optimize performance and cost, reducing index sizes and improving query speeds with minimal impact on accuracy.
  • Anomaly detection: Powered by the random cut forest algorithm, allowing users to monitor data in near real time and identify outliers.
  • Hybrid search and analytics capabilities: These features make OpenSearch a unified platform for managing structured, unstructured, and vectorized data in AI-driven applications.

Key use cases for AI in OpenSearch

Generative AI agents

Generative AI agents rely on retrieving relevant information to ground their responses. OpenSearch supports this through retrieval-augmented generation (RAG), combining full-text and vector search to extract semantically relevant content from structured or unstructured data sources. Developers can index large corpora—such as documentation, knowledge bases, or customer records—using dense vectors derived from language models.

When an agent receives a user prompt, OpenSearch retrieves similar entries using k-NN or approximate nearest neighbor (ANN) algorithms. This context is passed to an external or embedded LLM, which generates a grounded response. OpenSearch Dashboards can also be extended to visualize agent behavior, track input-output pairs, and audit the relevance of retrieved context.

Recommendation engine

In recommendation systems, OpenSearch provides the infrastructure to model user-item relationships using vector embeddings. Items (such as products, articles, or videos) and user profiles are encoded into dense vectors using algorithms like matrix factorization, sentence transformers, or custom neural encoders. These vectors are indexed in OpenSearch and queried in real time to find similar items based on cosine or dot-product similarity.

The engine supports multi-modal recommendations by combining metadata, behavioral data, and content embeddings. For example, it can recommend products based on user clicks, textual descriptions, image embeddings, or co-purchase behavior. Developers can build collaborative or content-based filters and use hybrid scoring techniques to combine vector similarity with traditional filters like category, price, or availability.

User-level content targeting

OpenSearch enables content targeting by allowing segmentation and behavioral analysis directly within the search stack. User events—such as searches, clicks, and purchases—can be ingested in real time and modeled as feature vectors. These vectors are clustered or classified using integrated ML capabilities, such as k-means or classification models.

Once segments are defined, OpenSearch uses vector search to identify content most relevant to each user group. For example, in digital marketing, different user cohorts can be shown ads or content tuned to their behavior. Combined with filtering and scoring rules, this allows for controlled, testable personalization strategies. Dashboards can monitor engagement metrics by cohort, allowing teams to adjust targeting models based on observed performance.

Automated pattern matching and de-duplication

Pattern recognition and de-duplication are critical in domains like content moderation, news aggregation, and document management. OpenSearch helps by encoding documents into semantic embeddings and comparing them for similarity. Instead of relying on exact text matches, it uses cosine similarity between vectors to find paraphrased, translated, or rephrased content.

This approach helps in detecting spam, cloned pages, or copied documents that traditional search would miss. The system supports batch and real-time workflows, with APIs to update indexes as new content is ingested. Developers can implement thresholds to automatically suppress or flag duplicates, or integrate human-in-the-loop workflows for edge cases.

Combined with metadata filters (like date, source, or author), OpenSearch can accurately isolate novel content while suppressing noise.

Tips from the expert

Kassian Wren

Kassian Wren

Open Source Technology Evangelist

Kassian Wren is an Open Source Technology Evangelist specializing in OpenSearch. They are known for their expertise in developing and promoting open-source technologies, and have contributed significantly to the OpenSearch community through talks, events, and educational content

In my experience, here are tips that can help you better operationalize AI workloads with OpenSearch:

  1. Leverage model ensembling for richer relevance scoring: Instead of relying on a single ML model for generating embeddings or predictions, combine outputs from multiple models (e.g., BERT + FastText) to create composite embeddings or hybrid scores. This increases robustness and captures different semantic nuances.
  2. Use asynchronous inference pipelines for scale: Decouple inference from indexing/search pipelines using message queues (e.g., Apache Kafka). Asynchronously compute and index embeddings to prevent bottlenecks during high-throughput ingest or query operations.
  3. Apply structured sparsity to reduce vector dimensionality: Train embedding models with sparsity constraints or post-process vectors with techniques like PCA, SVD, or feature pruning. This shrinks index size and improves ANN search speed with minimal accuracy loss.
  4. Design embedding drift detectors using time-windowed metrics: Continuously monitor embedding similarity distributions over time. Abrupt shifts can signal data drift or embedding quality degradation, prompting retraining or quality assurance checks.

Tutorial: Getting started with vector search with OpenSearch

This tutorial walks through the process of building a basic vector search application using OpenSearch. It uses a simple example involving hotel locations on a coordinate plane, but the same structure applies to more complex AI use cases like semantic search and recommendations. Instructions are adapted from the OpenSearch documentation.

Step 1: Create a vector index

Begin by creating an index that supports vector search. Set index.knn to true in the index settings. Define a field of type knn_vector in the mappings and specify the number of dimensions—in this example, 2. You also need to set the space_type, which determines how similarity is calculated. Here, Euclidean distance (l2) is used:

Step 2: Ingest vector data

Next, add documents to the index. Each document represents a hotel, and its location is expressed as a 2D vector. Use the bulk API to load the data efficiently:

Step 3: Run a vector search

To search for hotels closest to a given point—say, [5, 4]—use a k-NN query. Set k to define how many nearest results to retrieve. The following query finds the three hotels closest to the input vector:

The response will return the top three matching documents based on similarity score. These are the nearest neighbors to the vector [5, 4] in the defined vector space.

Best practices for AI in OpenSearch

Organizations should consider the following practices to improve the use of AI in OpenSearch.

1. Normalize vectors for better relevance

Vector normalization ensures that all embeddings are on a consistent scale, which is crucial for reliable similarity comparisons. When vectors are not normalized, larger magnitude vectors can disproportionately influence similarity scores, especially when using dot-product or cosine similarity. This can lead to unexpected ranking results where less relevant documents are favored due to scale variance rather than true semantic closeness.

Before indexing vectors into OpenSearch, normalize them (e.g., using L2 normalization) so that the length of each vector is 1. This makes the similarity metric more interpretable and consistent. If using sentence transformers or other embedding models, include normalization as part of your preprocessing pipeline. While OpenSearch itself does not perform normalization automatically, ensuring consistency in vector magnitude improves search accuracy and stability.

2. Use hybrid search to combine signals

Hybrid search in OpenSearch combines traditional keyword matching (sparse retrieval) with dense vector similarity to improve both precision and recall. This is particularly effective in situations where user queries might include terms with ambiguous meanings or where semantic understanding is required to surface relevant content.

Hybrid search can be implemented using the script_score query to combine BM25 relevance with vector-based similarity. Developers can assign weights to balance the influence of each signal type, or use rank fusion techniques like reciprocal rank fusion (RRF). This approach mitigates the weaknesses of relying solely on either method: sparse search may miss semantically similar results, while dense-only retrieval may ignore key exact-match signals.

3. Continuously retrain models with updated data

Over time, embedding models can become outdated as the content, vocabulary, or user behavior they were trained on shifts. This concept drift can degrade the performance of search, recommendations, and personalization features. To mitigate this, retraining models on a regular basis using updated datasets is essential.

Establish a workflow to ingest new data (e.g., recent documents, search logs, user interactions) and fine-tune or retrain models accordingly. Automate this process where possible, including model evaluation and validation. Once updated models are ready, redeploy them via OpenSearch’s ML Commons interface and reindex affected documents. Keeping embeddings in sync with evolving content ensures your search system remains relevant and accurate.

4. Evaluate hosted vs. self-hosted embedding models

Choosing between hosted (e.g., OpenAI, AWS Bedrock, or SageMaker) and self-hosted models depends on factors like latency, cost, compliance, and control. Hosted models offer quick access to state-of-the-art capabilities without infrastructure setup, but may introduce network latency and raise concerns around data residency or usage limits.

Self-hosted models, on the other hand, allow full control over model versions, inference performance, and data handling. They can be optimized for local workloads, especially in regulated industries or low-latency environments. However, they require more operational overhead, including GPU provisioning, scaling, and monitoring. OpenSearch supports both approaches via model connectors and inference APIs.

5. Monitor search quality with human-in-the-loop feedback

Understanding how users interact with AI-powered search results is critical to refining model quality and query relevance. Human-in-the-loop (HITL) systems provide mechanisms for collecting and acting on user feedback—either implicit (e.g., clickthrough rates, dwell time) or explicit (e.g., ratings, flags).

Use OpenSearch Dashboards to visualize feedback metrics and correlate them with specific queries or result sets. Implement workflows for relevance assessments, where domain experts or annotators score result quality for a sample of queries. This data can be fed back into model tuning or search re-ranking strategies.

Instaclustr for OpenSearch: Benefiting AI workloads

When it comes to powering artificial intelligence applications, the right data infrastructure is not just a nice-to-have; it’s the engine that drives innovation. Instaclustr for OpenSearch provides a powerful, managed solution that is perfectly tuned for the demanding nature of AI workloads. By leveraging the full capabilities of OpenSearch, we offer a platform that empowers you to build, deploy, and scale sophisticated AI applications with confidence and ease.

One of the most significant advantages of using Instaclustr for OpenSearch is its incredible scalability. AI models, especially those involving deep learning and natural language processing, thrive on massive datasets. Our platform is designed to scale seamlessly with your data needs, ensuring you never hit a performance bottleneck. Whether you’re training a model with terabytes of data or serving millions of real-time predictions, our managed OpenSearch clusters can expand effortlessly, providing the computational power and storage you need, exactly when you need it. This eliminates the complexities of capacity planning and allows your teams to focus on developing groundbreaking AI, not managing infrastructure.

The ability to process data in real time is another key benefit for AI applications. From fraud detection systems that must act in milliseconds to recommendation engines that personalize user experiences on the fly, speed is critical. Instaclustr for OpenSearch excels at ingesting and indexing high-velocity data streams, making them immediately available for querying and analysis. This real-time capability allows your AI applications to make faster, more accurate decisions based on the most current information available, giving you a distinct competitive advantage.

Finally, we understand that security is paramount, especially when handling the sensitive and valuable data often used in AI. Our managed OpenSearch solution is built with a multi-layered security framework to protect your data at every turn. We provide robust features like end-to-end encryption, network isolation through private network peering, and comprehensive access controls. With Instaclustr, you can be confident that your data infrastructure meets stringent security and compliance standards, allowing you to innovate responsibly while keeping your critical assets secure.

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