How Much Money & Time You Need for Image Recognition App Development

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The image recognition technology market is booming. From manufacturing to e-commerce to healthcarealmost every industry is now actively investing in image recognition software to make business more efficient and secure. 

A decision to cash in on this market will most certainly be lucrative, provided you go about it the right way as you can target various industries. But when it comes to the time and money you need to put into image recognition app development, the answer will always be that it depends.

In this article, we will use Unicsoft’s experience in building image recognition software to help you estimate the time & cost you may need to invest in the development. 

But first, let’s recap what makes image recognition technology a game-changer for so many industries.

How image recognition apps disrupt markets

Applicable across industries, image recognition is one of the most disruptive and widely used technologies. Its global market size will only swell in the following years, reaching up to $53 billion by 2025.  

Here is how different businesses use image recognition:


  • Identify early-stage symptoms of diseases by scanning the patient’s face
  • Detect and classify tumors, fractures, lesions, moles, tooth decay, and other abnormalities by scanning photos, X-rays, and MRIs 
  • Assist in training junior doctors, and more

Automotive industry

  • Detect when the vehicle is damaged, assess the damage, and offer appropriate assistance and course of action
  • Detect and notify the driver about red traffic lights, on-road obstacles, pedestrian crossings, and approaching cars
  • Track driver’s behavior using face recognition
  • Predict and eliminate the risk of collision


  • Scan content to optimize product listing and form personalized content for shoppers
  • Identify trends and analyze shoppers’ activity to forecast their behavior
  • Enable users to find and buy desirable items by detecting them on the uploaded images (CamFind is an example)
  • Spot and flag fake brands through the logo recognition function


  • Automate quality control and defect detection by benchmarking the visual qualities of products against the standards 
  • Identify parts of products
  • Detect dangerous situations and actions on the factory floor
  • Fully automate optical grading and sorting
  • Detect tool movement

Security and surveillance:

  • Manage access by scanning visitors’ faces and matching them with photos of staff
  • Secure offline and online banking operations and payments with facial authorization 
  • Track student attendance and spot any problematic activity on campus
  • Detect crimes and criminals in public places with CCTV cameras through face recognition, and more

The list of image recognition use cases goes on to retail, education, gaming, and marketing, but we’ll stop here for now. In all of them, image recognition technology helps improve user and customer experience, increase product quality, and streamline business processes.

What does image recognition app development entail?

There is no need to bury yourself in technicalities to build a good image recognition app, but still, you should know what’s under the hood. It will help you avoid overheads and keep up with the development process.  

Image recognition is part of the artificial intelligence (AI) / machine learning (ML) domain. It enables you to categorize images and identify objects, people, animals, and places. On top of that, ML learning algorithms help you classify and match the images following the requirements specified in the app. Learning approaches can be supervised or unsupervised.

To see the difference between these two approaches, let’s imagine that your app has to decipher the forms and colors of digital images. With supervised learning, we tell the machine that shapes with five sides and angles are known as pentagons, and shapes with four equal sides are squares. In terms of colors, we teach a computer that certain pixel values are classified as “green” and others as “red.” 

With unsupervised learning, we feed the system with the same data but don’t specify the characteristics of a particular class. A machine doesn’t know that this object is a pentagon and that one is a square, but it will work to recognize the objects with more or less the same characteristics.

The cost of development depends on the approach you pursue. Your choice will be guided by a range of factors, including the nature and the amount of data and the way you store and update it. If you know or can anticipate how to label your data and how it might behave, you can “supervise” the machine. If you’re not sure about the data patterns, you will leave it up to the machine to identify them and learn from its own mistakes.

When it comes to the development process, you can take one of the two routes: create the software from the ground up or build it on top of the existing API. It will heavily influence how much time and money will go into the development. 

Creating software from scratch

To develop an image recognition app from zero, you need to create, train, and maintain your own dedicated neural network, the backbone of image recognition algorithms. This complex and resource-intensive task comes with a hefty price tag, but it’s rewarding in the long run. Just look at some benefits of choosing this highly customized approach:

  • Full control over technology and its performance
  • The solution tailored to your business 
  • Better support and maintenance
  • Higher scalability and code reusability

But to reap those benefits, you’ll need to hire a team of highly qualified AI, ML, computer vision, and data science experts. Together with iOS or Android developers, they will take the tech burden off your shoulders. This path is most popular with large enterprises having the necessary time and budget. Small-to-medium businesses usually go for the second option.

Building an app on top of existing computer vision API

An alternative route is to incorporate prebuilt image recognition APIs into your app. The solutions of this kind offer out-of-the-box image recognition tools. They can be used to integrate pre-trained machine learning models into an existing app, build a specific feature, or develop an entire app.

The choice of public API depends on the characteristics and requirements of your app. Here are some recognized API providers to consider:

Google Cloud Vision AI

Google Cloud Vision API enables your app to automatically recognize objects, faces, and printed and handwritten text. You can also build custom models for image recognition.

Amazon Rekognition

Amazon Rekognition API is empowered with deep learning technology. It analyzes images and videos to recognize objects, faces, text, scenes, and activities. You can also scan images for inappropriate content. 

Amazon API is easy to use for someone without machine learning expertise. It also has multiple use cases for a variety of applications. 

Microsoft Computer Vision

Computer Vision by Microsoft is a powerful tool for image and video recognition. It can return content tags for various objects and concepts, extract text in multiple languages, generate image descriptions, moderate content, and identify movement. 

IBM Maximo Visual Inspection

Visual Inspection comes as part of Maximo Application Suit. It offers a broad business toolkit deployable without much expertise in deep learning. You can also import your neuro models to the platform, saving time for your data scientists.

You should opt for this option if:

  • Cost efficiency is your priority
  • You are limited on time 
  • The functionality of the existing APIs is more than enough for your app’s goals 
  • You don’t mind being dependent on an external technology

Remember that you will still need to hire a professional data scientist to develop, train, and supervise your neuro models. Saving on experts always equals a subpar product.

Now, let’s navigate through the development process.

Stages of image recognition software development

Everything that goes into the software development lifecycle (SDLC) will affect the cost of the end product. These are the main elements of SDLC that impact the price:

1. Requirements gathering. To outline the requirements, decide on the following:

  • Your target audience
  • The platform (iOS, Android, or both)
  • Identification objects
  • Additional features (item navigation, tracking, etc)
  • Monetization strategy

This document will be the backbone of your future development and determine your next steps.

2. Scope of work (features). It should be all clear here: the more basic features, the fewer hours to build them, and the other way round. Whatever features you decide to go for, it’s essential to discuss and document them well in advance.

3. Technology stack. Picking the right tech stack is essential, as it determines how scalable, performant, and secure your software will be. You’ll also have to choose between a cross-platform or native approach to mobile app development. The decision usually depends on the project requirements. Each option has its benefits and drawbacks, but do remember that cross-platform development is up to 30% faster.

4. Talent acquisition. Unless you have an in-house team, you’ll have to choose between freelancers and outsourcing or outstaffing companies. Each option has its pros and cons, and the costs vary.

5. Development, testing, and store publishing. Development, testing, and product iteration can take months. The speed and cost depend on various factors, including the project complexity and the work model (outsourcing, outstaffing, or freelance).

Let’s get a feel of how these steps translate into costs.

Calculating the price for your image recognition app 

Only a professional team can give you an exact price for your future application, but the following factors could still guide you on a ballpark figure right now.

From scratch vs. by incorporating APIs 

If we are talking about building a relatively simple and easy-deployable image recognition API, the average cost would be $20,000. It covers 30 working days for a developer with an hourly salary of $50. The final amount can go up or down depending on the complexity of API and the developers’ experience and hourly rate.

Integrating your app with an existing API will be considerably cheaper. The integration doesn’t take much time, so the development costs are lower.

However, you must pay fees to use existing APIs to process the images. The price depends on the number of images/videos processed by your app. For example, Amazon charges $0,0010 per image for up to 1 million images, which totals $1,000. 

Location and composition of the development team 

The developer’s hourly rate depends not only on their expertise but also on their location.

This is the average hourly pay in different regions according to Glassdoor:

RegionCost ($/hr)
North America45-200
South America30-71
Eastern Europe28-75

Don’t forget that a development team isn’t limited to developers only. It also needs the following specialists, whose rates also vary: 

  • Project manager
  • ML expert(s)
  • 3D modeling expert 
  • iOS/Android or cross-platform developers
  • UI/UX designer(s)
  • QA specialist(s)

This is an approximate team composition you might require to develop an image recognition app.

Number of features

The complexity of your app and its features directly translates into its final cost. Here is the rough estimate of application development costs depending on the scope of work:

TypeTime to developCost
Simple3-6 months$150,000 – 250,000
Medium6-10 months$200,000 – 400,000
Complex10+ months$400,000 – 700,000

MVP / POC vs. MLP development

Developing pilot versions of your app like MVP (Minimum Viable Product) or POC (Proof of Concept) will cost you cheaper than commissioning a full MLP (Minimum Lovable Product). MLP might take a few more months of work to perfect every feature, while MVP can be rolled out with the basic functionality. The difference in price may reach dozens of thousands of dollars.  

Platforms of choice: iOS, Android, or both

Building native iOS apps is usually easier, faster, and cheaper than building Android apps.

If you decide to create an app for both platforms, you can take one of the two ways: develop a native app for each platform or build a cross-platform (hybrid) app using one code base. 

The first option performs better but doubles the price as you must build two apps. The second one is quicker and cheaper to develop, but the functionality and user experience may suffer. 

Business domain 

Ranked last as a price-affecting factor, the business domain still impacts the cost of development. To develop an image recognition app in heavily regulated fields like healthcare or fintech, you must cover the services of regulatory advisors. Plus, you may need to implement industry-specific security systems to comply with regulations. 

That was a rough overview of the cost of developing an image recognition application. If you need help estimating your solution’s price, get a quote from Unicsoft experts. It’s free, no strings attached. 


Building an image recognition app is an attractive and financially promising idea. But to understand the cost of the venture, you must have a clear vision of your product. The development cost depends on many variables: the API, the platform, the features, the work model – freelance, outsourcing, or outstaffing – and, of course, the location of the development team. 

Every case is unique, though, and to get the estimate, you need to consider every detail. Contact us, and our team will guide you on the exact cost.