Get started with search

New to search with Elasticsearch? Start building a search experience by setting up your first deployment, refining your search goals, and adding data. These core search capabilities are available to you regardless of your deployment type, solution, or project type.

Note

If you're looking for an introduction to the Elastic Stack or the Elasticsearch product, go to Elastic fundamentals or The Elasticsearch data store.

Agent skill available

A skill is available to help AI agents with this topic.

This skill guides users through search concepts and helps create a working search use case.

Learn more about agent skills for Elastic

Choose your deployment type

Elastic provides several self-managed and Elastic-managed options.

To get started, choose one of these options:

Create a Serverless project.
Create a local development installation.

Check out the full list of deployment types to learn more.

Create a Serverless project

For simplicity and speed, use Elastic Cloud Serverless.

Create a serverless project

Choose a Serverless project configuration

Both Elasticsearch projects and Elasticsearch Vector Database projects support vector search and use the same query APIs.

Project type	When to choose
Elasticsearch project	For general-purpose data storage and search, including mixed lexical, time series, and analytics workloads, or when you need to run custom models on machine learning nodes
Elasticsearch Vector Database project	When embeddings and similarity search are central to your workload, such as RAG, recommendations, semantic search, hybrid search, or multimodal search

There are two options to create a serverless project:

If you're a new user, sign up for a free 14-day trial to create a serverless project. For more information about Elastic Cloud trials, refer to Trial features.
If you're an existing customer, log in to Elastic Cloud. On the home page, you can create a serverless project. You need the admin predefined role or an equivalent custom role to create projects. Refer to User roles and privileges.

Create a local development installation

Create a local development installation in Docker:

curl -fsSL https://elastic.co/start-local | sh

(Optional) Try out a quickstart

Get hands-on experience with Elasticsearch using guided tutorials that walk you through common search scenarios:
- Index and search basics: Learn how to create indices, add documents, and perform searches
- Keyword search with Python: Build your first search query with Python
- Semantic search: Implement semantic search with vector embeddings using the semantic_text workflow
Identify your search goals

Depending on your use case, you can choose multiple search approaches, for example full-text and semantic search. Each approach affects your options for storing and querying your data.

If you're unsure which approaches match your goals, you can try them out with sample data. For example, Semantic search quickstart.

If you prefer to ingest your data first and transform or reindex it as needed later, skip to the next step.
Ingest your data

If your goals include vector or semantic AI-powered search, create vectorized data with built-in and third-party natural language processing (NLP) models and store it in an Elasticsearch vector database. The approach that requires the least configuration involves adding semantic_text fields when ingesting your data. This method is described in Semantic search with semantic_text.

To learn about adding data for other search goals, go to Ingest for search use cases. For a broader overview of ingestion options, go to Ingest: Bring your data to Elastic.

If you're not ready to add your own data, you can use sample data or create small data sets when you follow the instructions in the quickstarts.

The Elasticsearch home page in the UI also provides workflow guides for creating indices and ready-to-use code examples for ingesting data by using REST APIs.
Build your search queries

Your next steps will be to choose a method to write queries and interact with Elasticsearch. You can pick a programming language client that matches your application and choose which query languages you will use to express your search logic. Each decision builds on the previous ones, offering flexibility to mix and match approaches based on your needs.

Use these resources to learn more about Elasticsearch or get started in a different way: