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  4. Search As A Service
  5. Azure Cognitive Search vs Found Elasticsearch

Azure Cognitive Search vs Found Elasticsearch

OverviewComparisonAlternatives

Overview

Found Elasticsearch
Found Elasticsearch
Stacks11
Followers15
Votes0
Azure Cognitive Search
Azure Cognitive Search
Stacks39
Followers67
Votes1

Azure Cognitive Search vs Found Elasticsearch: What are the differences?

Introduction

Azure Cognitive Search and Found Elasticsearch are both powerful tools for searching and analyzing large volumes of data. However, there are key differences between the two that make them suited for different use cases.

  1. Data Source Integration: Azure Cognitive Search provides out-of-the-box integrations with various data sources such as Azure Blob Storage, Azure SQL Database, and Azure Cosmos DB. On the other hand, Found Elasticsearch can ingest data from a wide range of sources including databases, file systems, and streaming platforms, making it more flexible in terms of data source integration.

  2. Scalability and Performance: Azure Cognitive Search offers built-in scalability and high-performance search capabilities, with options to scale up or down as needed. On the contrary, Found Elasticsearch is known for its highly scalable and distributed architecture, making it ideal for handling massive volumes of data and supporting high-performance search operations.

  3. Natural Language Processing (NLP) and AI Capabilities: Azure Cognitive Search incorporates pre-built AI models for natural language processing tasks, such as entity recognition, language detection, and sentiment analysis. It also has features for OCR (Optical Character Recognition) and image analysis. In contrast, Found Elasticsearch is primarily focused on providing powerful search and analytics capabilities, without incorporating pre-built NLP or AI functionalities.

  4. Full-Text Search and Query Language: Azure Cognitive Search provides a rich full-text search experience, allowing users to perform keyword searches, fuzzy searches, and advanced search operations using a simple query syntax. Found Elasticsearch, being built on Apache Lucene, offers a highly expressive and feature-rich query language (Query DSL) for complex search scenarios, including boolean queries, term queries, and wildcard queries.

  5. Managed Service vs. Self-Managed: Azure Cognitive Search is a managed service provided by Microsoft, which means that the underlying infrastructure and maintenance tasks are handled by Microsoft. Found Elasticsearch, on the other hand, requires self-management and administration of the Elasticsearch clusters, giving more control to the users but also requiring more expertise and resources for setup and maintenance.

  6. Ecosystem and Integration: Azure Cognitive Search is tightly integrated with other Azure services, allowing seamless integration with various PaaS and SaaS offerings within the Azure ecosystem. Found Elasticsearch has a vibrant ecosystem of plugins and integrations, enabling integration with a wide range of third-party tools and systems.

In summary, Azure Cognitive Search is a managed service with rich AI capabilities and out-of-the-box integrations, while Found Elasticsearch is a highly scalable self-managed platform with powerful search and analytics functionalities and a wide range of integration options. Choosing between the two depends on factors such as the level of control, scalability needs, AI requirements, and ecosystem integration preferences.

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Detailed Comparison

Found Elasticsearch
Found Elasticsearch
Azure Cognitive Search
Azure Cognitive Search

Create your own fully managed and hosted Elasticsearch cluster. You get a dedicated cluster with reserved memory, giving you predictable performance. There are no arbitrary limits on how many indexes or documents you can store. Scale your clusters as and when needed, without any downtime.

It is the only cloud search service with built-in AI capabilities that enrich all types of information to easily identify and explore relevant content at scale. Formerly known as Azure Search, it uses the same integrated Microsoft natural language stack that Bing and Office have used for more than a decade and AI services across vision, language and speech. Spend more time innovating and less time maintaining a complex cloud search solution.

Hosted and managed: You get your own fully hosted and managed Elasticsearch cluster. No need to host and maintain your own costly search infrastructure.; Reserved Memory and storage: Your clusters get reserved memory and storage. No shared clusters and no arbitrary limits on how many indexes or documents you can store.; Scalable and flexible: Start small, grow big. You can scale your cluster as and when needed, without any downtime. There are several Elasticsearch versions to choose from, and upgrading is easier than ever.; Developer friendly: Our HTTPS API is developer-friendly and existing Elasticsearch libraries such as Tire, PyES and others works out of the box. We even provide an unmodified Elasticsearch API, so for those who have an existing Elasticsearch integration it is easy to start using Found.; Security: Communication to and from our service is securely transmitted over HTTPS (SSL) and your data is stored behind multiple firewalls and proxies. All clusters run in isolated containers (LXC) and customizable ACLs allow for restricting access to trusted people and hosts.; Availability: For production and mission critical environments we provide replication and automatic failover, protecting your cluster against unplanned downtime. In addition, data is continuously backed up to Amazon S3. In the event of a datacenter failure, your cluster is automatically failed over to a working datacenter or, in the case of a catastrophic event, rebuilt from backup.; Multi region: Found Elasticsearch is available in multiple regions.; Found Elasticsearch is also available on Heroku, AppHarbor and CloudControl.
Start, maintain and scale with minimal investment;Create searchable content using integrated AI;Customise to meet goals and industry requirements
Statistics
Stacks
11
Stacks
39
Followers
15
Followers
67
Votes
0
Votes
1
Pros & Cons
No community feedback yet
Pros
  • 1
    111
Integrations
No integrations available
Postman
Postman
Java
Java
Node.js
Node.js
Python
Python
C#
C#
PowerShell
PowerShell

What are some alternatives to Found Elasticsearch, Azure Cognitive Search?

Elasticsearch

Elasticsearch

Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).

Algolia

Algolia

Our mission is to make you a search expert. Push data to our API to make it searchable in real time. Build your dream front end with one of our web or mobile UI libraries. Tune relevance and get analytics right from your dashboard.

Typesense

Typesense

It is an open source, typo tolerant search engine that delivers fast and relevant results out-of-the-box. has been built from scratch to offer a delightful, out-of-the-box search experience. From instant search to autosuggest, to faceted search, it has got you covered.

Amazon CloudSearch

Amazon CloudSearch

Amazon CloudSearch enables you to search large collections of data such as web pages, document files, forum posts, or product information. With a few clicks in the AWS Management Console, you can create a search domain, upload the data you want to make searchable to Amazon CloudSearch, and the search service automatically provisions the required technology resources and deploys a highly tuned search index.

Amazon Elasticsearch Service

Amazon Elasticsearch Service

Amazon Elasticsearch Service is a fully managed service that makes it easy for you to deploy, secure, and operate Elasticsearch at scale with zero down time.

Manticore Search

Manticore Search

It is a full-text search engine written in C++ and a fork of Sphinx Search. It's designed to be simple to use, light and fast, while allowing advanced full-text searching. Connectivity is provided via a MySQL compatible protocol or HTTP, making it easy to integrate.

Azure Search

Azure Search

Azure Search makes it easy to add powerful and sophisticated search capabilities to your website or application. Quickly and easily tune search results and construct rich, fine-tuned ranking models to tie search results to business goals. Reliable throughput and storage provide fast search indexing and querying to support time-sensitive search scenarios.

Swiftype

Swiftype

Swiftype is the easiest way to add great search to your website or mobile application.

MeiliSearch

MeiliSearch

It is a powerful, fast, open-source, easy to use, and deploy search engine. The search and indexation are fully customizable and handles features like typo-tolerance, filters, and synonyms.

Quickwit

Quickwit

It is the next-gen search & analytics engine built for logs. It is designed from the ground up to offer cost-efficiency and high reliability on large data sets. Its benefits are most apparent in multi-tenancy or multi-index settings.

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