Best Deep Learning Software in India
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Compare the Top Deep Learning Software in India as of October 2025
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What is Deep Learning Software in India?
Deep learning software provides tools and frameworks for developing, training, and deploying artificial neural networks, particularly for complex tasks such as image and speech recognition, natural language processing (NLP), and autonomous systems. These platforms leverage large datasets and powerful computational resources to enable machines to learn patterns and make predictions. Popular deep learning software includes frameworks like TensorFlow, PyTorch, Keras, and Caffe, which offer pre-built models, libraries, and tools for designing custom models. Deep learning software is essential for industries that require advanced AI solutions, including healthcare, finance, automotive, and entertainment. Compare and read user reviews of the best Deep Learning software in India currently available using the table below. This list is updated regularly.
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1
Vertex AI
Google
Vertex AI provides deep learning capabilities that allow businesses to create powerful machine learning models for complex tasks such as image recognition, natural language processing, and autonomous decision-making. These models leverage neural networks and large datasets to learn patterns and make predictions with high accuracy. With Vertex AI’s scalable infrastructure, businesses can train deep learning models on large volumes of data and deploy them for real-time inference. New customers receive $300 in free credits, giving them an opportunity to explore and experiment with deep learning models. This functionality provides businesses with the tools needed to solve sophisticated challenges and drive innovation in AI-driven applications.Starting Price: Free ($300 in free credits) -
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Qloo
Qloo
Qloo is the “Cultural AI”, decoding and predicting consumer taste across the globe. A privacy-first API that predicts global consumer preferences and catalogs hundreds of millions of cultural entities. Through our API, we provide contextualized personalization and insights based on a deep understanding of consumer behavior and more than 575 million people, places, and things. Our technology empowers you to look beyond trends and uncover the connections behind people’s tastes in the world around them. Look up entities in our vast library spanning categories like brands, music, film, fashion, travel destinations, and notable people. Results are delivered within milliseconds and can be weighted by factors such as regionalization and real-time popularity. Used by companies who want to incorporate best-in-class data in their consumer experiences. Our flagship recommendation API delivers results based on demographics, preferences, cultural entities, metadata, and geolocational factors. -
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Fraud.net
Fraud.net, Inc.
Fraudnet's AI-driven platform empowers enterprises to prevent threats, streamline compliance, and manage risk in real-time. Our sophisticated machine learning models continuously learn from billions of transactions to identify anomalies and predict fraud attacks. Our unified solutions: comprehensive screening for smoother onboarding & improved compliance, continuous monitoring to proactively identify new threats, & precision fraud detection across channels and payment types. With dozens of data integrations and advanced analytics, you'll dramatically reduce false positives while gaining unmatched visibility. And, with no-code/low-code integration, our solution scales effortlessly as you grow. The results speak volumes: Leading payments companies, financial institutions, innovative fintechs, and commerce brands trust us worldwide—and they're seeing dramatic results: 80% reduction in fraud losses and 97% fewer false positives. Request your demo today and discover Fraudnet. -
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DataMelt
jWork.ORG
DataMelt (or "DMelt") is an environment for numeric computation, data analysis, data mining, computational statistics, and data visualization. DataMelt can be used to plot functions and data in 2D and 3D, perform statistical tests, data mining, numeric computations, function minimization, linear algebra, solving systems of linear and differential equations. Linear, non-linear and symbolic regression are also available. Neural networks and various data-manipulation methods are integrated using Java API. Elements of symbolic computations using Octave/Matlab scripting are supported. DataMelt is a computational environment for Java platform. It can be used with different programming languages on different operating systems. Unlike other statistical programs, it is not limited to a single programming language. This software combines the world's most-popular enterprise language, Java, with the most popular scripting language used in data science, such as Jython (Python), Groovy, JRuby.Starting Price: $0 -
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Microsoft Cognitive Toolkit
Microsoft
The Microsoft Cognitive Toolkit (CNTK) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers. CNTK can be included as a library in your Python, C#, or C++ programs, or used as a standalone machine-learning tool through its own model description language (BrainScript). In addition you can use the CNTK model evaluation functionality from your Java programs. CNTK supports 64-bit Linux or 64-bit Windows operating systems. To install you can either choose pre-compiled binary packages, or compile the toolkit from the source provided in GitHub. -
6
Keras
Keras
Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages. It also has extensive documentation and developer guides. Keras is the most used deep learning framework among top-5 winning teams on Kaggle. Because Keras makes it easier to run new experiments, it empowers you to try more ideas than your competition, faster. And this is how you win. Built on top of TensorFlow 2.0, Keras is an industry-strength framework that can scale to large clusters of GPUs or an entire TPU pod. It's not only possible; it's easy. Take advantage of the full deployment capabilities of the TensorFlow platform. You can export Keras models to JavaScript to run directly in the browser, to TF Lite to run on iOS, Android, and embedded devices. It's also easy to serve Keras models as via a web API. -
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Clarifai
Clarifai
Clarifai is a leading AI platform for modeling image, video, text and audio data at scale. Our platform combines computer vision, natural language processing and audio recognition as building blocks for developing better, faster and stronger AI. We help our customers create innovative solutions for visual search, content moderation, aerial surveillance, visual inspection, intelligent document analysis, and more. The platform comes with the broadest repository of pre-trained, out-of-the-box AI models built with millions of inputs and context. Our models give you a head start; extending your own custom AI models. Clarifai Community builds upon this and offers 1000s of pre-trained models and workflows from Clarifai and other leading AI builders. Users can build and share models with other community members. Founded in 2013 by Matt Zeiler, Ph.D., Clarifai has been recognized by leading analysts, IDC, Forrester and Gartner, as a leading computer vision AI platform. Visit clarifai.comStarting Price: $0 -
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Ray
Anyscale
Develop on your laptop and then scale the same Python code elastically across hundreds of nodes or GPUs on any cloud, with no changes. Ray translates existing Python concepts to the distributed setting, allowing any serial application to be easily parallelized with minimal code changes. Easily scale compute-heavy machine learning workloads like deep learning, model serving, and hyperparameter tuning with a strong ecosystem of distributed libraries. Scale existing workloads (for eg. Pytorch) on Ray with minimal effort by tapping into integrations. Native Ray libraries, such as Ray Tune and Ray Serve, lower the effort to scale the most compute-intensive machine learning workloads, such as hyperparameter tuning, training deep learning models, and reinforcement learning. For example, get started with distributed hyperparameter tuning in just 10 lines of code. Creating distributed apps is hard. Ray handles all aspects of distributed execution.Starting Price: Free -
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Metacoder
Wazoo Mobile Technologies LLC
Metacoder makes processing data faster and easier. Metacoder gives analysts needed flexibility and tools to facilitate data analysis. Data preparation steps such as cleaning are managed reducing the manual inspection time required before you are up and running. Compared to alternatives, is in good company. Metacoder beats similar companies on price and our management is proactively developing based on our customers' valuable feedback. Metacoder is used primarily to assist predictive analytics professionals in their job. We offer interfaces for database integrations, data cleaning, preprocessing, modeling, and display/interpretation of results. We help organizations distribute their work transparently by enabling model sharing, and we make management of the machine learning pipeline easy to make tweaks. Soon we will be including code free solutions for image, audio, video, and biomedical data.Starting Price: $89 per user/month -
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Auger.AI
Auger.AI
Auger.AI has the most complete solution for ensuring machine learning model accuracy. Our MLRAM tool (Machine Learning Review and Monitoring) ensures your models are consistently accurate. It even computes the ROI of your predictive model! MLRAM works with any machine learning technology stack. If your ML system lifecyle doesn’t include consistent measurement of model accuracy, you’re likely losing money from inaccurate predictions. And frequent retraining of models is both expensive and, if they’re experiencing concept drift, may not fix the underlying problem. MLRAM provides value to both the data scientist and business user with features like accuracy visualization graphs, performance and accuracy alerts, anomaly detection and automated optimized retraining. Hooking up your predictive model to MLRAM is just a single line of code. We offer a free one month trial of MLRAM to qualified users. Auger.AI is the most accurate AutoML platform.Starting Price: $200 per month -
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V7 Darwin
V7
V7 Darwin is a powerful AI-driven platform for labeling and training data that streamlines the process of annotating images, videos, and other data types. By using AI-assisted tools, V7 Darwin enables faster, more accurate labeling for a variety of use cases such as machine learning model training, object detection, and medical imaging. The platform supports multiple types of annotations, including keypoints, bounding boxes, and segmentation masks. It integrates with various workflows through APIs, SDKs, and custom integrations, making it an ideal solution for businesses seeking high-quality data for their AI projects.Starting Price: $150 -
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OTO
OTO Systems
OTO allows call centers 100% visibility of what is said during customer calls within 20 hours. Complement your NPS scoring with in-call intonation analytics. Identify call agent engagement and proactively set your WFM plan. Pick calls for QA faster. OTO is language-agnostic and gives you output parameters on various angles. Our API allows companies to start analyzing 100% of in-call conversations within a couple of hours. Sign up for a free trial and start analyzing your call data! Voice is the most valuable touchpoint between you and your customer. We're here to help you truly understand and leverage your voice data at scale. Whether you're building a mobile app or data analytics dashboards, our lightweight DeepToneTM engine gives you access to our powerful voice models on any device, providing you with a rich layer of acoustic labels for nearly every audio format.Starting Price: $100 per month -
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SoapBox
Soapbox Labs
SoapBox is built for kids. Our mission is to transform play and learning experiences for kids everywhere using voice technology. Our low-code, scalable platform is licensed by education and consumer companies globally to deliver world-class voice experiences for literacy and English language tools, smart toys, games, apps, and robots to the market. Our independent, proprietary technology delivers 95% accuracy for kids of all ages from 2-12 years old. It also caters to global accents and dialects and has been independently verified to show no racial or socio-economic bias. The SoapBox platform has been built using a privacy-by-design approach. Protecting kids' fundamental right to voice data privacy is a cornerstone of our work and philosophy.Starting Price: upon request -
14
Comet
Comet
Manage and optimize models across the entire ML lifecycle, from experiment tracking to monitoring models in production. Achieve your goals faster with the platform built to meet the intense demands of enterprise teams deploying ML at scale. Supports your deployment strategy whether it’s private cloud, on-premise servers, or hybrid. Add two lines of code to your notebook or script and start tracking your experiments. Works wherever you run your code, with any machine learning library, and for any machine learning task. Easily compare experiments—code, hyperparameters, metrics, predictions, dependencies, system metrics, and more—to understand differences in model performance. Monitor your models during every step from training to production. Get alerts when something is amiss, and debug your models to address the issue. Increase productivity, collaboration, and visibility across all teams and stakeholders.Starting Price: $179 per user per month -
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Interplay
Iterate.ai
Interplay Platform is a patented low-code platform with 475 pre-built connectors (enterprise, AI, IoT, Startup Technologies). It's used as middleware and as a rapid app building platform by big companies like Circle K, Ulta Beauty, and many others. As middleware, it operates Pay-by-Plate (frictionless payments at the gas pump) in Europe, Weapons Detection (to predict robberies), AI-based Chat, online personalization tools, low price guarantee tools, computer vision applications such as damage estimation, and much more. It also helps companies to go to market with their digital solutions 10X to 17X faster than in old ways. -
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Amazon Rekognition
Amazon
Amazon Rekognition makes it easy to add image and video analysis to your applications using proven, highly scalable, deep learning technology that requires no machine learning expertise to use. With Amazon Rekognition, you can identify objects, people, text, scenes, and activities in images and videos, as well as detect any inappropriate content. Amazon Rekognition also provides highly accurate facial analysis and facial search capabilities that you can use to detect, analyze, and compare faces for a wide variety of user verification, people counting, and public safety use cases. With Amazon Rekognition Custom Labels, you can identify the objects and scenes in images that are specific to your business needs. For example, you can build a model to classify specific machine parts on your assembly line or to detect unhealthy plants. Amazon Rekognition Custom Labels takes care of the heavy lifting of model development for you, so no machine learning experience is required. -
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Planisware
Planisware
Planisware Enterprise captures your strategy and aligns your portfolios, projects, and teams to make an impact on the bottom line. Planisware Orchestra enables project decision-making across the entire portfolio and helps you reach the next maturity level. Planisware Enterprise is an integrated solution that brings together budgets, forecasts, schedules, resources, and actuals. Global organizations like Ford, Philips, Pfizer, and Société Générale, and dynamic mid-sized innovators such as Zebra, Beam Suntory, and MSA Safety alike trust Planisware to manage their project pipeline. Shape your strategy and assess results through roadmaps, budgets, and investment buckets. Define, prioritize, and manage your portfolio of projects through investment scenarios, and simulations. Gain visibility and manage your resources through capacity planning, resource scheduling, and time tracking. Control your projects through scheduling, costs, and deliverable management. -
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Caffe
BAIR
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. Check out our web image classification demo! Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices. Extensible code fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models. Speed makes Caffe perfect for research experiments and industry deployment. Caffe can process over 60M images per day with a single NVIDIA K40 GPU. -
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The Intel® Deep Learning SDK is a set of tools for data scientists and software developers to develop, train, and deploy deep learning solutions. The SDK encompasses a training tool and a deployment tool that can be used separately or together in a complete deep learning workflow. Easily prepare training data, design models, and train models with automated experiments and advanced visualizations. Simplify the installation and usage of popular deep learning frameworks optimized for Intel® platforms. Easily prepare training data, design models, and train models with automated experiments and advanced visualizations. Simplify the installation and usage of popular deep learning frameworks optimized for Intel® platforms. The web user interface includes an easy to use wizard to create deep learning models, with tooltips to guide you through the entire process.
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20
TFLearn
TFLearn
TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed up experimentations while remaining fully transparent and compatible with it. Easy-to-use and understand high-level API for implementing deep neural networks, with tutorial and examples. Fast prototyping through highly modular built-in neural network layers, regularizers, optimizers, metrics. Full transparency over Tensorflow. All functions are built over tensors and can be used independently of TFLearn. Powerful helper functions to train any TensorFlow graph, with support of multiple inputs, outputs, and optimizers. Easy and beautiful graph visualization, with details about weights, gradients, activations and more. The high-level API currently supports most of the recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, Generative networks. -
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Deeplearning4j
Deeplearning4j
DL4J takes advantage of the latest distributed computing frameworks including Apache Spark and Hadoop to accelerate training. On multi-GPUs, it is equal to Caffe in performance. The libraries are completely open-source, Apache 2.0, and maintained by the developer community and Konduit team. Deeplearning4j is written in Java and is compatible with any JVM language, such as Scala, Clojure, or Kotlin. The underlying computations are written in C, C++, and Cuda. Keras will serve as the Python API. Eclipse Deeplearning4j is the first commercial-grade, open-source, distributed deep-learning library written for Java and Scala. Integrated with Hadoop and Apache Spark, DL4J brings AI to business environments for use on distributed GPUs and CPUs. There are a lot of parameters to adjust when you're training a deep-learning network. We've done our best to explain them, so that Deeplearning4j can serve as a DIY tool for Java, Scala, Clojure, and Kotlin programmers. -
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Deep learning frameworks such as TensorFlow, PyTorch, Caffe, Torch, Theano, and MXNet have contributed to the popularity of deep learning by reducing the effort and skills needed to design, train, and use deep learning models. Fabric for Deep Learning (FfDL, pronounced “fiddle”) provides a consistent way to run these deep-learning frameworks as a service on Kubernetes. The FfDL platform uses a microservices architecture to reduce coupling between components, keep each component simple and as stateless as possible, isolate component failures, and allow each component to be developed, tested, deployed, scaled, and upgraded independently. Leveraging the power of Kubernetes, FfDL provides a scalable, resilient, and fault-tolerant deep-learning framework. The platform uses a distribution and orchestration layer that facilitates learning from a large amount of data in a reasonable amount of time across compute nodes.
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MXNet
The Apache Software Foundation
A hybrid front-end seamlessly transitions between Gluon eager imperative mode and symbolic mode to provide both flexibility and speed. Scalable distributed training and performance optimization in research and production is enabled by the dual parameter server and Horovod support. Deep integration into Python and support for Scala, Julia, Clojure, Java, C++, R and Perl. A thriving ecosystem of tools and libraries extends MXNet and enables use-cases in computer vision, NLP, time series and more. Apache MXNet is an effort undergoing incubation at The Apache Software Foundation (ASF), sponsored by the Apache Incubator. Incubation is required of all newly accepted projects until a further review indicates that the infrastructure, communications, and decision-making process have stabilized in a manner consistent with other successful ASF projects. Join the MXNet scientific community to contribute, learn, and get answers to your questions. -
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IntelliHub
Spotflock
We work closely with businesses to find out what are the common issues preventing companies from realising benefits. We design to open up opportunities that were previously not viable using conventional approaches Corporations -big and small, require an AI platform with complete empowerment and ownership. Tackle data privacy and adopt to AI platforms at a sustainable cost. Enhance the efficiency of businesses and augment the work humans do. We apply AI to gain control over repetitive or dangerous tasks and bypass human intervention, thereby expediting tasks with creativity and empathy. Machine Learning helps to give predictive capabilities to applications with ease. You can build classification and regression models. It can also do clustering and visualize different clusters. It supports multiple ML libraries like Weka, Scikit-Learn, H2O and Tensorflow. It includes around 22 different algorithms for building classification, regression and clustering models. -
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DeepPy
DeepPy
DeepPy is a MIT licensed deep learning framework. DeepPy tries to add a touch of zen to deep learning as it. DeepPy relies on CUDArray for most of its calculations. Therefore, you must first install CUDArray. Note that you can choose to install CUDArray without the CUDA back-end which simplifies the installation process. -
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Deci
Deci AI
Easily build, optimize, and deploy fast & accurate models with Deci’s deep learning development platform powered by Neural Architecture Search. Instantly achieve accuracy & runtime performance that outperform SoTA models for any use case and inference hardware. Reach production faster with automated tools. No more endless iterations and dozens of different libraries. Enable new use cases on resource-constrained devices or cut up to 80% of your cloud compute costs. Automatically find accurate & fast architectures tailored for your application, hardware and performance targets with Deci’s NAS based AutoNAC engine. Automatically compile and quantize your models using best-of-breed compilers and quickly evaluate different production settings. Automatically compile and quantize your models using best-of-breed compilers and quickly evaluate different production settings. -
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Autogon
Autogon
Autogon is a leading AI and machine learning company, that simplifies complex technology to empower businesses with accessible, cutting-edge solutions for data-driven decisions and global competitiveness. Discover the empowering potential of Autogon models as they enable industries to leverage the power of AI, fostering innovation and fueling growth across diverse sectors. Experience the future of AI with Autogon Qore, your all-in-one solution for image classification, text generation, visual Q&A, sentiment analysis, voice cloning, and more. Empower your business with cutting-edge AI capabilities and innovation. Make informed decisions, streamline operations, and drive growth without the need for extensive technical expertise. Empower engineers, analysts, and scientists to harness the full potential of artificial intelligence and machine learning for their projects and research. Create custom software using clear APIs and integration SDKs. -
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Amazon EC2 G4 Instances
Amazon
Amazon EC2 G4 instances are optimized for machine learning inference and graphics-intensive applications. It offers a choice between NVIDIA T4 GPUs (G4dn) and AMD Radeon Pro V520 GPUs (G4ad). G4dn instances combine NVIDIA T4 GPUs with custom Intel Cascade Lake CPUs, providing a balance of compute, memory, and networking resources. These instances are ideal for deploying machine learning models, video transcoding, game streaming, and graphics rendering. G4ad instances, featuring AMD Radeon Pro V520 GPUs and 2nd-generation AMD EPYC processors, deliver cost-effective solutions for graphics workloads. Both G4dn and G4ad instances support Amazon Elastic Inference, allowing users to attach low-cost GPU-powered inference acceleration to Amazon EC2 and reduce deep learning inference costs. They are available in various sizes to accommodate different performance needs and are integrated with AWS services such as Amazon SageMaker, Amazon ECS, and Amazon EKS. -
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NVIDIA DeepStream SDK
NVIDIA
NVIDIA's DeepStream SDK is a comprehensive streaming analytics toolkit based on GStreamer, designed for AI-based multi-sensor processing, including video, audio, and image understanding. It enables developers to create stream-processing pipelines that incorporate neural networks and complex tasks like tracking, video encoding/decoding, and rendering, facilitating real-time analytics on various data types. DeepStream is integral to NVIDIA Metropolis, a platform for building end-to-end services that transform pixel and sensor data into actionable insights. The SDK offers a powerful and flexible environment suitable for a wide range of industries, supporting multiple programming options such as C/C++, Python, and Graph Composer's intuitive UI. It allows for real-time insights by understanding rich, multi-modal sensor data at the edge and supports managed AI services through deployment in cloud-native containers orchestrated with Kubernetes. -
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Qualcomm Cloud AI SDK
Qualcomm
The Qualcomm Cloud AI SDK is a comprehensive software suite designed to optimize trained deep learning models for high-performance inference on Qualcomm Cloud AI 100 accelerators. It supports a wide range of AI frameworks, including TensorFlow, PyTorch, and ONNX, enabling developers to compile, optimize, and execute models efficiently. The SDK provides tools for model onboarding, tuning, and deployment, facilitating end-to-end workflows from model preparation to production deployment. Additionally, it offers resources such as model recipes, tutorials, and code samples to assist developers in accelerating AI development. It ensures seamless integration with existing systems, allowing for scalable and efficient AI inference in cloud environments. By leveraging the Cloud AI SDK, developers can achieve enhanced performance and efficiency in their AI applications.
