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Top 6,950 Other Analytics Software SaaS Companies in May 2026

As of May 2026, there are 6,950 SaaS companies in Other Analytics Software. They have combined revenues of $73.1B and employ 478.3K people. They have raised $72.6B and serve 6.6B customers combined.

Other Analytics Software encompasses a wide range of tools designed to analyze various types of data not covered by more mainstream analytics platforms. These tools often cater to specific niches or unique data types, enabling organizations to gain insights that may not be available through traditional analytics solutions. Use cases include performance tracking, advanced data modeling, and specialized market analysis, which are essential for data-driven decision-making across diverse sectors. Common features of Other Analytics Software may include custom reporting capabilities, unique data visualization tools, and integration with a variety of data sources. Workflows often involve data importing, processing, analysis, and visualization, allowing users to transform raw data into actionable insights tailored to their specific needs. Typical buyer personas include data analysts, marketing teams, and operations managers who seek specialized insights to inform strategic decisions.

Companies
6,950
Revenue
$73.1B
Funding
$72.6B
Employees
478.3K

Filters

Sorting: Highest -> Lowest

Filters

Top Other Analytics Software Companies

Showing 10 of 1,074 companies ranked by annual revenue.

1
Nanonets

San Francisco, United States

Nanonets leverages advanced OCR and Deep Learning technology to efficiently extract relevant information from unstructured text and documents. It enables the digitization of documents, extraction of specific data fields, and facilitates integration with everyday applications through APIs, all within a simple and intuitive interface. This technology significantly streamlines manual processes by automating tasks such as invoice, receipt, and document reviews. It notably reduces processing time by up to 90% and can save up to 50% on costs.

Revenue
$100M
Customers
-
Year founded
2017
Funding
-
Team size
101
Growth
150%
2
Venasolutions

Toronto, Ontario, Canada

FP&A software provider

Revenue
$100M
Customers
900
Year founded
2011
Funding
$476M
Team size
678
Growth
-
3
SambaNova Systems

Palo Alto, California, United States

SambaNova is the leading Enterprise AI company that delivers a full-stack infrastructure from silicon to software, specializing in machine learning and big data analytics platforms.

Revenue
$100M
Customers
-
Year founded
2017
Funding
$982M
Team size
417
Growth
-
4
Firstup

San Francisco, California, United States

world’s first intelligent communication platform

Revenue
$100M
Customers
1K
Year founded
2010
Funding
$114.8M
Team size
373
Growth
-
5
DevRev Inc.

Palo Alto, California, United States

DevRev is designed for AI to work alongside people by converging business infrastructure, which we call AgentOS. AgentOS unlocks the power of AI Agents, We can replace legacy systems with an interconnected platform to automate labor-intensive tasks, and enhance the customer experience with self-service and preemptive engagement. Despite its breadth, AgentOS begins to deliver value in hours rather than months through our powerful data replication, sync capabilities, and analytics AI agents.

Revenue
$100M
Customers
-
Year founded
2020
Funding
$150.8M
Team size
809
Growth
43.06%
6
Enveyo

Provo, Utah, United States

Enveyo is the leading provider of logistics data management, visibility, and shipping optimization software, helping shippers and 3PLs move their logistics forward through data-driven technology. From shipment analytics and automated carrier selection to post-purchase delivery experience management and carrier auditing, Enveyo is the only suite deploying solutions across the logistics lifecycle. Powered by a robust, enterprise data management platform, Enveyo Insights, Modeling, Cloudroute, Alerting, and Audit solutions enable organizations to make business-transforming shipping decisions. To learn more about how Enveyo moves logistics forward, visit enveyo.com.

Revenue
$100M
Customers
-
Year founded
-
Funding
-
Team size
25
Growth
-
7
Quantum Metric

Colorado Springs, Colorado, United States

digital experience analytics platform

Revenue
$100M
Customers
-
Year founded
2015
Funding
$251M
Team size
453
Growth
211.18%
8
Quantexa Limited

London, England, United Kingdom

AI data analytics platfor

Revenue
$100M
Customers
1K
Year founded
2016
Funding
$88M
Team size
872
Growth
9.37%
9
Cohere

Toronto, Ontario, Canada

the leading AI platform for enterprise, suited to the needs of business, unlocking unprecedented ease-of-use, accessibility, and data privacy

Revenue
$100M
Customers
-
Year founded
2019
Funding
$1B
Team size
843
Growth
17.65%
10
Algolia

San Francisco, California, United States

Algolia is owned by Algolia Inc., a privately held company headquartered in San Francisco, California, USA. Algolia is a search-as-a-service platform that enables developers to build fast, relevant, and intuitive search experiences for their websites and applications. The company was founded in 2012 by Nicolas Dessaigne and Julien Lemoine and has since grown to serve thousands of customers worldwide, including big names like Twitch, Stripe, and Under Armour. Algolia's mission is to make every search interaction meaningful and rewarding for users, and to help businesses improve engagement and conversion rates through better search experiences.

Revenue
$100M
Customers
17K
Year founded
2012
Funding
$334.2M
Team size
862
Growth
33.33%

Inclusion Criteria

- The software must facilitate advanced data analysis functionalities. - It should provide unique reporting and visualization capabilities that are not typically found in mainstream analytics tools. - The product must support integration with a variety of data sources, including specialized databases. - It should cater to specific industries or use cases that require niche analytical features. - Not just data visualization; it must also include tools for data processing and modeling. - The software should be user-friendly for non-technical users while still offering advanced features for data experts.