Open-source IBM Watson Discovery alternatives

Elasticsearch is a distributed, RESTful search engine written in Java. It stores documents as JSON, indexes them so they can be searched quickly, and exposes everything through an HTTP API, which makes it a common backbone for site search, application search and log analytics. It is built on the Apache Lucene library.

Beyond full-text queries it supports filtering, aggregations for analytics and, in recent versions, vector search for semantic and AI-assisted retrieval. Data is spread across nodes in a cluster using shards and replicas, so capacity and resilience grow by adding machines. It is the core of the Elastic Stack, usually paired with Kibana for exploration and with ingestion tools for logs and metrics.

The repository describes the project as free and open source, while its license metadata is listed as 'Other', so review the current license terms before relying on it commercially. It can be self-managed on your own servers or used as a managed service through Elastic Cloud.

Onyx is an open-source AI platform that acts as a knowledge and context layer for teams and their AI agents. It connects to more than 50 applications, pulls in documents along with their metadata and permissions, and builds an internal representation so that LLMs can answer questions grounded in what your company actually knows.

The project argues that this indexed approach gives more reliable, lower-latency and cheaper context than approaches that have an agent search many tools iteratively through MCP. Features include agentic RAG on a hybrid index, a Deep Research mode for multi-step reports, custom agents with their own knowledge, instructions and actions, and live web search through providers such as Serper, Google PSE, Brave and SearXNG. Sandboxes and skills extend what models can do, and the chat interface works with every major LLM.

Onyx emphasizes data sovereignty through flexible self-hosted deployments, with a single-command deploy option. The code is written in Python and Next.js, and the license is listed as 'Other' on GitHub, so check the terms for the enterprise features. It suits organizations that want a private, ChatGPT-style assistant connected to internal documents.

OpenSearch is an open-source search and observability suite that makes large amounts of unstructured data searchable and analyzable. It is a distributed engine with a RESTful API, written in Java, and developed in the open by the OpenSearch community under the Apache 2.0 licence.

Teams use it for full-text search, log and event analytics, and monitoring of applications and infrastructure. The project provides downloads, documentation, forums and Slack channels, and it follows a formal release and maintainer process. Because it is distributed, clusters can be scaled across multiple nodes as data volumes grow.

OpenSearch includes certain Apache-licensed code derived from Elasticsearch, as its trademark notice explains. You can run it on your own servers, or use managed services offered by cloud providers. Security issues are reported privately by email rather than through public issues, and the project maintains a code of conduct for contributors.

Vespa is a platform for applications that must pick a subset of data from a large, changing corpus, evaluate machine-learned models over it, organize and aggregate the results and return them quickly. Typical examples are search, recommendation and personalization, and the platform adds vector search and retrieval-augmented generation as well, as shown in its topics.

Doing this over large data sets distributed across many nodes and evaluated in parallel is hard, and Vespa handles it with high availability and performance. It handles searching, running inference over and organizing vectors, tensors, text and structured data while serving. According to the README, Vespa has been developed over many years and runs behind several large internet services.

The repository contains all the code needed to build and run Vespa yourself under the Apache 2.0 licence, and new releases are made from the master branch on weekday mornings. You can deploy applications to the Vespa Cloud service, which offers a free trial, or run your own instance following the getting started guide. It is written in Java and C++ and suits teams building large-scale search and recommendation systems.

SWIRL is an AI search and retrieval-augmented generation tool that works without moving your data. Instead of copying content into a vector database, it queries your sources live with each user's own permissions, re-ranks the results, and can generate an answer with citations using the language model of your choice. The Community edition in this repository is released under the Apache-2.0 license.

Connectors reach many business applications, and the project's tagline refers to more than 100 apps. The approach avoids building ETL pipelines, standing up a vector store, or maintaining a second copy of data that needs security and audit controls, since permissions are enforced at the source. The stack is Python and Django, and a Docker quick start gets an instance running in about two minutes.

A separate SWIRL Enterprise edition adds a three-pass reranker, canonical answers, an MCP server for agents, and managed support. The Community edition is free to self-host. It suits IT and data teams wanting enterprise search across many systems while keeping data in place.

Apache Solr is a search platform from the Apache Software Foundation, built on top of Apache Lucene. It lets organizations index large collections of documents and query them quickly, and its README describes support for full-text, vector and geospatial search used by many large organizations.

Solr is a server you run yourself and talk to over HTTP. It includes example configurations to get started, a reference guide with a deployment guide and tutorials, and an administration interface available on the local server once it is running. The project topics also link it to NoSQL-style storage, information retrieval and backend search engine use cases.

The software is written in Java and released under the Apache-2.0 licence, with downloads on the Apache site. It can be installed from a distribution, run with the official Docker image, or deployed on Kubernetes using the Solr Operator. Support comes through mailing lists, Slack and IRC. It suits engineering teams adding search to websites, catalogs or internal tools without relying on a hosted search vendor.

Fess is an open-source enterprise search server that you install and run on any platform with a Java runtime. It is built on OpenSearch but does not require prior OpenSearch knowledge, because everything is configured in a browser-based administration interface. It can also serve as a free site search for your own website.

A built-in crawler collects documents from web sites, file systems and data stores such as databases, CSV files, cloud storage and SaaS sources, and handles many formats including Microsoft Office files, PDF and archives. Search supports faceting, sorting and suggestions, results can be filtered by role and permission, and single sign-on works with LDAP, OpenID Connect, SAML, SPNEGO and Microsoft Entra ID. It offers a REST API, text analysis for over 20 languages, plugins, and AI features for RAG and semantic search.

Fess is written in Java and licensed under Apache-2.0. It needs Java 21 or newer for the ZIP, RPM and DEB packages, and Docker images bundle OpenSearch. It suits organizations that want a private, self-managed alternative to Elasticsearch-based setups or Google Site Search.

Open Semantic Search is a research tool aimed at journalists, researchers and analysts who need to search, browse, analyze and explore large collections of documents, going well beyond simple keyword search. It combines a semantic search engine with a full text mining and text analytics platform.

It includes an ETL framework for processing documents, covering crawling, text extraction, text analysis, named entity recognition for persons, organizations and locations, and OCR for images and embedded images inside PDFs. On top of that data pipeline, it provides metadata management through thesauri and ontologies using the SKOS standard, and search user interfaces and applications supporting full-text search, faceted search, exploratory search and knowledge graph search. Its topics specifically mention OSINT and investigative journalism as use cases.

Open Semantic Search can be built as a Debian or Ubuntu package, as a desktop search appliance for VirtualBox, or as a set of Docker images using the project's docker-compose configuration, which starts all the needed services together. It is written in Shell and Python, documented with MkDocs, and released under the GPL-3.0 license.

OpenSearchServer is enterprise-class search engine software built on Lucene. With its web user interface, crawlers and JSON web service, you can add advanced full-text search to an application quickly. It runs on Linux, Unix, BSD and Windows.

Search functions include phonetic search, boolean queries with a query language, faceted and collapsed results, filters via sub-requests, geolocation, spell-checking, relevance customization and auto-completion suggestions. Indexing supports 18 languages with per-language analyzers, lemmatization, n-grams, synonyms, automatic language recognition, named entity recognition and automatic classification.

It can index HTML, Microsoft Office and OpenOffice documents, PDFs with OCR, RTF, plain text, audio metadata and images through OCR, using web, file system and database crawlers. OpenSearchServer is licensed under Apache-2.0; the README notes a Docker image was still to come, and documentation and binaries are on the project site.