---
title: The Transactional Elasticsearch Alternative
description: An open source, ACID-compliant alternative to Elasticsearch
canonical: https://docs.paradedb.com/welcome/introduction
---

![ParadeDB Banner](/images/paradedb_banner.png)

ParadeDB is an open source, ACID-compliant alternative to Elasticsearch. ParadeDB is built on Postgres as a Postgres extension, not a fork of Postgres.

## Why ParadeDB?

There are many search engines in the world, but they are mostly built for static datasets and eventual consistency. ParadeDB is for users who
want first-class search and analytics performance, but frequently update data and need consistency between their source and their indexes.

ParadeDB may be a good fit for your stack if:

- **You frequently update** the data you want to run search queries over
- **Elasticsearch lag, shard loss, or stale indexes** keep you up at night
- You spend more time **babysitting ETL processes** than doing things that matter
- You **enjoy using Postgres**, you **hate using Elasticsearch**, or something in between
- **You have tried tsvector and the GIN index for Postgres text search**, but lack of fuzzy matching, BM25 relevance scoring, advanced query types, etc. leads to low-quality results
- Analytical queries in Postgres (e.g. `COUNT`, `GROUP BY`, etc.) **hit timeouts**
- You **value simplicity** when designing systems

## ParadeDB vs. Alternatives

People usually compare ParadeDB to two other types of systems: OLTP databases like vanilla Postgres and search engines like Elastic.

|                             | **OLTP database**                                                        | **Search engine**                                                                                      | **ParadeDB**                                                                                                              |
| --------------------------- | ------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------- |
| **Primary role**            | System of record                                                         | Search and retrieval engine                                                                            | System of record **and** search/analytics engine                                                                          |
| **Examples**                | Postgres, MySQL                                                          | Elasticsearch, OpenSearch                                                                              |                                                                                                                           |
| **ACID guarantees**         | Full ACID compliance, read-after-write guarantees                        | No transactions, atomic only per-document, eventual consistency, durability not guaranteed until flush | Full ACID compliance, read-after-write guarantees                                                                         |
| **Update & delete support** | Built for fast-changing data                                             | Struggles with updates/deletes                                                                         | Built for fast-changing data                                                                                              |
| **Search features**         | Basic FTS (no BM25, weak ranking)                                        | Rich search features (BM25, fuzzy matching, faceting, hybrid search)                                   | Rich search features (BM25, fuzzy matching, faceting, hybrid search)                                                      |
| **Analytics features**      | Not an analytical DB (no column store, batch processing, etc.)           | Column store, batch processing, parallelization via sharding                                           | Column store, batch processing, parallelization via Postgres [parallel workers](/documentation/performance-tuning/writes) |
| **Lag**                     | None in a single cluster                                                 | At least network, ETL transformation, and indexing time                                                | None in a single cluster                                                                                                  |
| **Operational complexity**  | Simple (single datastore)                                                | Complex (ETL pipelines, managing multiple systems)                                                     | Simple (single datastore)                                                                                                 |
| **Scalability**             | Vertical scaling in a single node, horizontal scaling through Kubernetes | Horizontal scaling through sharding                                                                    | Vertical scaling in a single node, horizontal scaling through [Kubernetes](/deploy/self-hosted/kubernetes)                |
| **Language**                | SQL                                                                      | Custom DSL                                                                                             | Standard SQL with custom search operators                                                                                 |

## Production Readiness

As a company, ParadeDB is over two years old. ParadeDB launched in the [Y Combinator (YC)](https://ycombinator.com) S23 batch and has been validated in
production since December 2023.

[ParadeDB Community](https://github.com/paradedb/paradedb), the open-source version of ParadeDB, has been deployed over 400,000 times in the past 12 months.
ParadeDB Enterprise, the durable and production-hardened edition of ParadeDB, powers core search and analytics use cases at enterprises ranging from Fortune 500s to fast-growing startups. A few
examples include:

- **Alibaba Cloud**, the largest Asia-Pacific cloud provider, uses ParadeDB to power search inside their data warehouse. [Case study available](https://www.paradedb.com/blog/case-study-alibaba).
- **Bilt Rewards**, a rent payments technology company that processed over $36B in payments in 2024. [Case study available](https://www.paradedb.com/blog/case-study-bilt).
- **Modern Treasury**<sup>1</sup>, a financial technology company that automates the full cycle of money movement.
- **UnifyGTM**<sup>1</sup>, one of the fastest-growing startups in AI sales automation.
- **TCDI**<sup>1</sup>, a giant in the legal software and litigation management space.

_1. Case study coming soon_

## Next Steps

You're now ready to jump into our guides.

<CardGroup cols={2}>
  <Card
    title="Getting Started"
    icon="forward-fast"
    href="/documentation/getting-started/install"
  >
    Get started with ParadeDB in under five minutes.
  </Card>
  <Card
    title="Architecture"
    icon="diagram-project"
    href="/welcome/architecture"
  >
    Learn how ParadeDB is built.
  </Card>
  <Card
    title="Reference"
    icon="magnifying-glass"
    href="/documentation/full-text/overview"
  >
    API reference for full text search and analytics.
  </Card>
  <Card title="Deploy" icon="server" href="/deploy/overview">
    Deploy ParadeDB as a Postgres extension or standalone database.
  </Card>
</CardGroup>
