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Top 178 Deep Learning Software SaaS Companies in May 2026

As of May 2026, there are 178 SaaS companies in Deep Learning Software. They have combined revenues of $3.3B and employ 16.8K people. They have raised $6.7B and serve 557.1K customers combined.

Deep Learning Software refers to a category of applications that leverage deep learning techniques to analyze data, automate processes, and derive insights. These tools use artificial neural networks to mimic human cognitive processes, allowing for complex computation and pattern recognition. Common use cases include image and speech recognition, natural language processing, and predictive analytics across various industries such as healthcare, finance, and technology. Typical features of deep learning software include data preprocessing tools, model training capabilities, performance optimization, and deployment functions. Users often span a wide range of professions, including data scientists, IT professionals, and analysts, who apply these technologies to derive actionable insights from large datasets. As the technology matures, industries are increasingly adopting these solutions to enhance decision-making processes and drive innovation in their operations.

Companies
178
Revenue
$3.3B
Funding
$6.7B
Employees
16.8K

Filters

Sorting: Highest -> Lowest

Filters

Top Deep Learning Software Companies

Showing 10 of 35 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
Synthesia

London, England, United Kingdom

Synthesia.io is an innovative technology company specializing in artificial intelligence and deep learning solutions for video synthesis and content creation.

Revenue
$100M
Customers
55K
Year founded
2017
Funding
$536.6M
Team size
620
Growth
61.29%
3
GeologicAI

Calgary, Alberta, Canada

GeologicAI redefines geological and mining decision-making with advanced core scanning technology and AI-powered analytical and modeling solutions.

Revenue
$73.6M
Customers
-
Year founded
2013
Funding
-
Team size
186
Growth
-
4
Zenseact

Göteborg, Sweden

Zenseact’s purpose is to make safe and intelligent mobility real for everyone, everywhere. We live in an ever-changing environment at the center of which autonomous artificial intelligence is about to become reality. Sweden and China based Zenseact is a technology company that is committed to preserving all life on the road through its scalable ADAS/AD software platform.

Revenue
$68.7M
Customers
-
Year founded
2020
Funding
-
Team size
606
Growth
73.83%
5
Minieye

Shenzhen, China

Developer of a driver assistance system designed to make driving safer. The company's system uses vision algorithm with deep learning technology and performs robustly under complicated conditions of weather, light and traffic, enabling drivers in China to use autonomous driving in bad weather to save from accidents.

Revenue
$67M
Customers
-
Year founded
2013
Funding
$223.4M
Team size
47
Growth
-
6
퓨리오사에이아이

Tel Aviv, Tel Aviv, Israel

FuriosaAI designs and develops AI accelerators (NPUs) optimized for data center operations, focusing on high-performance and power-efficient solutions for computer vision, generative AI, and various demanding workloads.

Revenue
$65.6M
Customers
-
Year founded
2019
Funding
-
Team size
20
Growth
-
7
Qure AI

New York, New York, United States

Owner and operator of a healthcare technology company intended to make healthcare more affordable and accessible through the power of deep learning. The company's software uses deep learning and deep learning algorithms which is compatible with any X-ray, CT scan or MRI machine, which help in classifying radiology images as normal or abnormal, diagnose disease and highlight abnormalities that may otherwise be overlooked, enabling doctors to diagnose diseases and highlight abnormalities more accurately.

Revenue
$65.2M
Customers
-
Year founded
2016
Funding
$16M
Team size
309
Growth
54.48%
8
Syntiant Corp

Irvine, California, United States

Syntiant is a leader in edge-AI deployments, bringing deep-learning to any device with industry-leading Neural Decision Processors and hardware-agnostic machine learning models.

Revenue
$62.7M
Customers
-
Year founded
2017
Funding
-
Team size
251
Growth
-
9
InApp

Palo Alto, California, United States

Since 2000, InApp has been delivering full-cycle software development services to customers worldwide. Founded by a group of IT experts with several years of Big 5 consulting experience, InApp presently has offices in the USA, India, and Japan; a 400+ strong team of software engineers, and a solid client base ranging from Fortune 500 companies to SMBs. InApp offers an integrated portfolio of software services/technologies such as core technologies, cloud computing, analytics, blockchain solutions, AR & VR Solutions, AI & Deep Learning, and IoT. We deliver our high-performing products to the manufacturing and construction industries and ISVs.

Revenue
$51.7M
Customers
-
Year founded
2000
Funding
-
Team size
470
Growth
-
10
WORLD LABS TECHNOLOGIES

Barcelona, Catalonia, Spain

Develops AI models with spatial intelligence for 3D world perception and interaction

Revenue
$50M
Customers
-
Year founded
2023
Funding
$230M
Team size
43
Growth
-

Inclusion Criteria

- Must provide tools for building, training, and deploying deep learning models - Should include support for various data types, such as text, images, and audio - Must offer capabilities for model evaluation and performance metrics - Should enable integration with big data frameworks and cloud services - Not just focused on traditional machine learning; must specifically address deep learning methods - Must facilitate automation of repetitive tasks within the deep learning workflow - Should be suitable for use by professionals such as data scientists, engineers, and researchers