Software Alternatives, Accelerators & Startups

Nanonets VS MongoDB

Compare Nanonets VS MongoDB and see what are their differences

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Nanonets logo Nanonets

Worlds best image recognition, object detection and OCR APIs. NanoNets’ platform makes it straightforward and fast to create highly accurate Deep Learning models.

MongoDB logo MongoDB

MongoDB (from "humongous") is a scalable, high-performance NoSQL database.
  • Nanonets Landing page
    Landing page //
    2023-10-23

NanoNets is a Deep Learning web platform that makes it easier than ever before to use Deep Learning in practical applications. It combines the convenience of a web-based platform with Deep Learning models to create image recognition and object classification applications for your business. You can easily build and integrate deep learning models using NanoNets’ API. You can also work with our pre-trained models which have been trained on huge datasets and return accurate results. NanoNets has leveraged recent advances in Deep Learning to build rich representations of data which are transferable across tasks. It’s as simple as uploading your input, generating the output and getting a functioning and highly accurate Deep Learning model for your AI needs. NanoNets is revolutionary because it allows you to train models without large datasets. With just 100 images you can train a model on our platform to detect features and classify images with a high degree of accuracy. NanoNets benefits you in four important ways: ● It reduces the amount of data needed to build a Deep Learning Model ● NanoNets handles the infrastructure for hosting and training the model, and for the run time ● It reduces the cost of running deep learning models by sharing infrastructure across models ● It is possible for anyone to build a deep learning model

  • MongoDB Landing page
    Landing page //
    2023-10-21

Nanonets

$ Details
freemium
Platforms
Browser REST API Docker Cross Platform Python JavaScript Java PHP Go C++
Release Date
2017 January

MongoDB

Pricing URL
-
$ Details
Platforms
-
Release Date
-

Nanonets features and specs

  • Ease of Use
    Nanonets offers a user-friendly interface that makes it accessible for users without extensive technical knowledge, allowing easy setup and deployment of AI models.
  • Versatility
    The platform supports a wide range of use cases, including document extraction, image recognition, and OCR (Optical Character Recognition), making it adaptable to various business needs.
  • Automation
    Nanonets offers automation features that help streamline workflows, reducing manual effort and increasing efficiency for repetitive tasks.
  • Scalability
    Nanonets can handle large volumes of data and scale up as business requirements grow, ensuring consistent performance and reliability.
  • Integration Capabilities
    The platform can integrate with numerous third-party applications through APIs, enhancing its functionality and compatibility with existing business systems.

Possible disadvantages of Nanonets

  • Cost
    While providing valuable features, Nanonets can be expensive for small businesses or startups with limited budgets, potentially making it less accessible for these users.
  • Customization Limitations
    Despite its versatility, some advanced users may find the customization options lacking, especially for highly specialized or niche applications.
  • Learning Curve
    Although the interface is user-friendly, some users may still experience a learning curve when utilizing more advanced features and integrations.
  • Data Privacy Concerns
    As with any cloud-based platform, there may be concerns about data privacy and security, especially when dealing with sensitive or confidential information.
  • Dependency on Internet Connectivity
    Nanonets is a cloud-based service, which means that a stable internet connection is required for optimal performance. Any disruptions in connectivity can hinder productivity.

MongoDB features and specs

  • Scalability
    MongoDB offers horizontal scaling through sharding, allowing it to handle large volumes of data and enabling distributed computing.
  • Flexible Schema
    It allows for a flexible schema design using BSON (Binary JSON), making it easier to iterate and change application data models.
  • High Performance
    MongoDB is optimized for read and write throughput, making it suitable for real-time applications.
  • Rich Query Language
    Supports a rich and expressive query language that allows for efficient querying and analytics.
  • Built-in Replication
    Provides robust replication mechanisms for high availability and redundancy.
  • Geospatial Indexing
    Offers powerful geospatial indexing capabilities, useful for location-based applications.
  • Aggregation Framework
    Enables complex data manipulations and transformations using the aggregation pipeline framework.
  • Cross-Platform
    Works on multiple operating systems, enhancing its versatility and deployment options.

Possible disadvantages of MongoDB

  • Memory Usage
    MongoDB can consume a large amount of memory due to its use of memory-mapped files, which may be a concern for some applications.
  • Complex Transactions
    While MongoDB supports ACID transactions, they can be more complex to implement and less efficient compared to traditional relational databases.
  • Data Redundancy
    The flexible schema design can lead to data redundancy and increased storage costs if not managed carefully.
  • Limited Joins
    Joins are supported but can be less efficient and more limited compared to relational databases, affecting complex relational data querying.
  • Indexing Overhead
    Extensive indexing can introduce overhead and impact performance, especially during write operations.
  • Learning Curve
    Requires a different mindset and understanding compared to traditional relational databases, which can present a learning curve for new users.
  • Lacks Mature Analytical Tools
    The ecosystem for analytical tools around MongoDB is not as mature as those for traditional relational databases, which might limit advanced analytics capabilities.
  • Cost
    The cost of using MongoDB's cloud services (MongoDB Atlas) can be high, especially for large-scale deployments.

Nanonets videos

Nanonets Airtable Walkthrough

MongoDB videos

MySQL vs MongoDB

More videos:

  • Review - The Good and Bad of MongoDB
  • Review - what is mongoDB

Category Popularity

0-100% (relative to Nanonets and MongoDB)
AI
100 100%
0% 0
Databases
0 0%
100% 100
Developer Tools
100 100%
0% 0
NoSQL Databases
0 0%
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Nanonets and MongoDB

Nanonets Reviews

7 Best OCR Software of 2022 (Free and PAID)
Nanonets use artificial intelligence to extract data from documents without any human intervention. It is designed to be easy to use and accurate and can handle a variety of different languages.
The best alternatives to Abbyy FineReader
Top five alternatives to Abbyy FineReader PDF1. Klippa DocHorizonPros of Klippa DocHorizonConsKlippa DocHorizon is used in industries such asKlippa DocHorizon offers you data extraction for multiple file types such asPricing2. VeryfiPros of VeryfiConsVeryfi is used in industries such asVeryfi’s OCR software offers data extraction for multiple file types such asPricing3....
Source: www.klippa.com

MongoDB Reviews

10 Top Firebase Alternatives to Ignite Your Development in 2024
MongoDB’s superpower lies in its flexibility. Its document-based model lets you store data in a free-form, schema-less way, making it adaptable to evolving application needs. Need to add a new field or change the structure of your data? No problem, MongoDB handles it with ease.
Source: genezio.com
Top 7 Firebase Alternatives for App Development in 2024
MongoDB Realm provides a robust alternative to Firebase, especially for apps requiring a flexible data model. Key features include:
Source: signoz.io
Announcing FerretDB 1.0 GA - a truly Open Source MongoDB alternative
MongoDB is no longer open source. We want to bring MongoDB database workloads back to its open source roots. We are enabling PostgreSQL and other database backends to run MongoDB workloads, retaining the opportunities provided by the existing ecosystem around MongoDB.
16 Top Big Data Analytics Tools You Should Know About
The database added a new feature to its list of attributes called MongoDB Atlas. It is a global cloud database technology that allows to deploy a fully managed MongoDB across AWS, Google Cloud, and Azure with its built-in automation for resource, workload optimization and to reduce the time required to handle the database.
9 Best MongoDB alternatives in 2019
MongoDB is an open source NoSQL DBMS which uses a document-oriented database model. It supports various forms of data. However, in MongoDB data consumption is high due to de-normalization.
Source: www.guru99.com

Social recommendations and mentions

Based on our record, MongoDB should be more popular than Nanonets. It has been mentiond 18 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Nanonets mentions (6)

  • Healthcare Automation Can Improve Patient Engagement
    Want to automate repetitive manual tasks? Check our Nanonets workflow-based document processing software. Source: almost 3 years ago
  • Document Automation for Probate
    Nanonets is a no-code, workflow-based, and AI-enhanced intelligent document processing platform. It automates all document processes and is built on a robust, intelligent, self-learning OCR API that allows users to extract required data from documents in minutes. Source: almost 3 years ago
  • Promote your business, week of May 16, 2022
    Check out our website here https://nanonets.com/ for more. We also have some free tools where you can experience our product for free (like https://nanonets.com/online-ocr). Source: about 3 years ago
  • How would you annotate resumes for object detection?
    Here is another company, which I just came across by accident, which do the same: https://nanonets.com/. Source: about 3 years ago
  • Automate Exam Research with Django, Nanonets and Google Search API
    We will be using Python3.6+, Django web framework, Nanonets for character extraction from an image, Cloudinary for image storage and Google Search API for performing the searches. - Source: dev.to / over 3 years ago
View more

MongoDB mentions (18)

  • Creating AI Memories using Rig & MongoDB
    In this article, we’ll build a CLI tool using the Rig AI framework and MongoDB for retrieval-augmented generation (RAG). This tool will store summarized conversations in a database and retrieve them when needed, enabling the AI to maintain context over time. - Source: dev.to / 2 months ago
  • The Adventures of Blink S2e2: Database, Contained
    Have a Mongo database holding the various phrases we're going to use and potentially configuration data for the frontend as well. - Source: dev.to / 9 months ago
  • Introducing Perseid: The Product-oriented JS framework
    It's also worth mentioning that Perseid provides out-of-the-box support for React, VueJS, Svelte, MongoDB, MySQL, PostgreSQL, Express and Fastify. - Source: dev.to / 8 months ago
  • DocumentDB Elastic Cluster Pricing
    Does anyone know if the most basic Elastic Cluster instance of DocumentDB carries any monthly fixed cost or is it just on-demand cost? Another words if I run like 10,000 queries against the DB per month, what kind of bill would I expect? This is for a super small app. I am currently using mongodb free tier , but want to migrate everything to AWS. Can't seem to find a straight answer to the pricing question. Source: over 2 years ago
  • I wrote some scripts for converting the UTZOO Usenet archive to a Mongo Database
    You can use either MongoDB.com's dashboard (if you host a remote database) or Mongo Compass to run queries on the data or you can modify the express middleware with your own queries. I'm still working on the API, so it's not very robust yet. I will update this when it is. Source: over 2 years ago
View more

What are some alternatives?

When comparing Nanonets and MongoDB, you can also consider the following products

Docsumo - Extract Data from Unstructured Documents - Easily. Efficiently. Accurately.

Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.

DocParser - Extract data from PDF files & automate your workflow with our reliable document parsing software. Convert PDF files to Excel, JSON or update apps with webhooks.

PostgreSQL - PostgreSQL is a powerful, open source object-relational database system.

DocuClipper - Automate data extraction from bank statements, invoices, tax forms and more.

MySQL - The world's most popular open source database