Streamline Your Visual Content with Advanced Image Tagging APIs

19 Jun 2024

Have you ever wondered how businesses keep their huge collections of images organized and easy to find? The secret is Image Tagging APIs.

Managing images well can greatly improve your business processes in today’s world, where visual content is everywhere. This blog will discuss how Image Tagging APIs work and their importance.

First, we’ll examine how image tagging with AI is better than doing it by hand. Then, we’ll explain how these APIs work and what technology they use.

We will also point out the key features you should look for when choosing an API to make the best decision.

Next, we’ll explore the practical uses of Image Tagging APIs in different industries. We will also be showing their wide-ranging benefits. We’ll offer tips on what to consider to help you pick the suitable API.

Lastly, we’ll share best practices for using these APIs to get the most out of them.

Let’s learn how Image Tagging APIs can change how you handle images and boost your business.

What Is the Power of Image Tagging Beyond Manual Labeling?

Manual image tagging takes a lot of time and effort. People have to look at each image and assign tags one by one. This process is slow and prone to mistakes.

Keeping tags accurate and consistent gets harder as the number of images grows. It also can’t keep up with the fast pace businesses need today. Therefore, leading to delays and inefficiencies.

Image Tagging APIs

Automated Image Tagging APIs use artificial intelligence (AI) to automate image labeling. These tools can look at images, recognize objects and scenes, and assign the right image tags. Here are some benefits:

👉Automated tagging is much faster than manual tagging.

👉AI reduces human error, making tags more accurate.

👉APIs can handle large numbers of images quickly.

👉Automation cuts down the need for human labor, saving money.

Use Cases Across Industries

Image Tagging APIs are useful in many fields. Here are some examples:

👉Online stores use image tagging to organize products. Tags like color, size, and style help customers find what they want. Amazon tags products to improve search results and recommendations.

👉Media companies tag images to manage their vast libraries. This helps them find and organize content quickly. Netflix tags images to suggest relevant shows and movies to viewers.

👉Schools and educational platforms tag images to sort materials. This makes it easier for students and teachers to find resources. Khan Academy tags videos by subject and grade level.

👉Hospitals tag medical images like X-rays and MRIs to organize patient records. This helps doctors diagnose and treat patients faster. Hospitals use image tagging to manage medical records efficiently.

How Do Image Tagging APIs Work?

Image tagging APIs help organize and label pictures using smart technology. Furthermore, they use AI techniques like object recognition to identify items in a photo. For example, the API can recognize the sand, water, and people in a beach photo.

Scene understanding helps the API see the bigger picture. It not only identifies objects but also understands their context. Furthermore, this makes tagging more accurate. The new Filestack EyeQ update improves image quality, helping the AI work better.

Metadata is data about data. For images, it includes tags, descriptions, and categories. Moreover, this makes finding and organizing pictures easier and faster.

“Computer vision” is a field that enables computers to see and understand images. “Image classification” is part of this field. It means sorting images into different groups based on their content. These technologies make image tagging APIs powerful and useful for managing photos.

Let us give you an example with Filestack image captioning. Suppose we have the below image:

happy man

We can get the tags for this image using the below Filestack URL:<FILESTACK_API_KEY>/security=p:<POLICY>,s:<SIGNATURE>/tags/<EXTERNAL_URL> 

After adding the API Key, signature, and policy, here is the response:

  "tags": {
    "auto": {
      "adult": 98,
      "architecture": 99,
      "brick": 99,
      "building": 99,
      "clothing": 88,
      "face": 99,
      "happy": 87,
      "head": 99,
      "knitwear": 88,
      "long sleeve": 56,
      "male": 98,
      "man": 98,
      "pants": 55,
      "person": 99,
      "photography": 99,
      "portrait": 99,
      "sitting": 99,
      "sleeve": 57,
      "smile": 87,
      "sweater": 88,
      "sweatshirt": 67,
      "wall": 99
    "user": null

What Are the Key Features to Look for in an Image Tagging API?

When choosing an image tagging API, look for these key features:

✔️The API should correctly identify and tag images with a higher accuracy.

✔️It should quickly handle large numbers of images.

✔️The API should grow with your image library.

✔️It should allow adjustments for your specific needs. In other words, it should have better customization features.

✔️The API should work well with your current content management systems (CMS) and digital asset management (DAM) platforms.

✔️Make sure the pricing is reasonable and fits your budget.

What Are the Practical Applications of Image Tagging APIs?

Image tagging APIs have many useful applications:

✔️Accurate tags make images easier to find on search engines by boosting the SEO. Therefore improving website visibility.

✔️Tags help quickly find specific images in a media library or website.

✔️The API automatically sorts and categorizes images. Furthermore, reducing manual work.

✔️Tags describe images for visually impaired users. Hence making websites more inclusive.

✔️By analyzing image content, the API can recommend personalized content to users.

How Do You Choose the Right Image Tagging API for Your Needs?

Choosing the right image tagging API depends on your needs and budget. Here are some popular options:

1. Google Cloud Vision

Known for high accuracy and features like text and landmark detection. Besides, it works well with other Google Image Tagger software services but can be pricey for large-scale use.

2. Amazon Rekognition

This image tagging software automates service and offers strong image and video analysis, including facial recognition. Moreover, it scales well and integrates with other AWS services. However, costs can add up with heavy use.

3. Clarifai

User-friendly with customizable models for tagging images. Moreover, it’s fast and easy to use but might not be as accurate in complex cases as Google or Amazon.

When choosing the best image tagging work API for you, consider these points:

  • Higher Accuracy
  • Advanced Features
  • Easy Integration
  • Higher Scalability
  • Affordable Cost

What Are the Best Practices for Implementing Image Tagging APIs?

Implementing an image tagging API can improve your image management. Here are some best practices:

  • Name your images consistently to find them easily.
  • Use clear, high-quality images for better tagging accuracy.
  • Organize images into folders or groups to make tagging easier for personal photo libraries.

Integrating the API with Your Workflows

  • Choose an API that works well with your CMS or digital asset management (DAM) platforms.
  • Automate the tagging process to save time and ensure consistency.
  • Tag large numbers of images at once to increase efficiency to upload images.

Data Privacy and Security

🛡️Make sure the API follows data privacy laws like GDPR or CCPA to protect user data.

🛡️Use encryption to keep image data safe during transfer and storage.

🛡️Limit who can access and change image data to improve security.


Automatic Image Tagging APIs help businesses manage large collections of images quickly and accurately. Moreover, these APIs use AI to tag images. Besides, they are saving time and reducing mistakes compared to manual tagging.

In this blog, we learned:

👉How Automated Picture Tagging APIs work and why they are important.

👉Image tagging APIs’ key features

👉How they are used in different industries

👉Tips for choosing the right one for your needs and budget.

👉Best practices for getting the most out of these APIs.

Using Image Tagging APIs can improve your SEO, make finding images easier, automate organization, and help with accessibility. Moreover, they also allow personalized content recommendations. Hence making your site or app more engaging through relevant tags.


What is an images tagging process?

The automatically tag images tagging process involves labeling images with descriptive tags using AI.

Can Filestack help with the image tagging process faster?

Yes. Filestack speeds up the image tagging process effectively.

Is Filestack secure enough to be trusted for images tagging APIs?

Filestack is secure and trustworthy for image tagging APIs due to its higher security strategies.