Video auto-tagging API

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• Amazon Rekognition Video (AWS): Provides comprehensive computer vision and machine learning APIs for video analysis. It automatically detects objects, scenes, specific frames, prominent text, slates, studio logos, credits, and human actions, returning frame-accurate timecodes and confidence scores.[oneuptime.com](https://aws.amazon.com/rekognition/media-analysis/)
• Microsoft Azure AI Video Indexer: A specialized cloud service built on top of Azure AI that extracts deep insights from videos using multiple channels (speech, audio, and visual). It automatically generates transcripts, identifies people, extracts keywords, labels visual scenes, and provides a rich API to index and tag video content for search.
• Google Cloud (Gemini / Vertex AI): While Google's legacy Video Intelligence API is deprecated for future sunsets, Google Cloud's modern multimodal Gemini models via Vertex AI serve as the premier ecosystem approach for video understanding. Developers can send video files or streams through the API to generate rich contextual auto-tags, detailed descriptions, and targeted timestamped metadata.[oneuptime.com](https://docs.cloud.google.com/video-intelligence/docs)
• Clarifai: Offers modular computer vision and NLP APIs that can be custom-trained or configured out-of-the-box to extract concept tags, recognize logos, detect specific objects, and moderate video content programmatically.
• Veritone (aiWARE): An enterprise AI platform and operating system designed heavily around media asset management and cognitive computing. It integrates multiple AI engines through an API layer to auto-tag video and audio archives via speech recognition, face detection, and sentiment analysis.

1. Google Cloud Video Intelligence API — my pick for conventional auto-tagging

This is probably the closest match to “take a video from our DAM and automatically add useful tags.”

Its LABEL_DETECTION capability identifies objects, locations, activities, animal species, products, etc. Importantly for a DAM, labels can be associated with video-level, shot-level, and frame-level timestamps.docs.cloud.google.com

For example, a marketing video might yield:

with timestamps indicating where those concepts occur.

It accepts videos from Google Cloud Storage and exposes a REST API, so a typical DAM integration would be:

DAM upload → webhook/queue → Google Video Intelligence → normalize tags → write metadata back to DAM

Google currently lists 1,000 free minutes/month, with stored-video label detection thereafter priced at $0.10/minute under its standard consumption pricing.cloud.google.com

Google Video Intelligence documentation

2. Amazon Rekognition Video — excellent if your DAM uses S3

Rekognition Video's StartLabelDetection API analyzes a video stored in Amazon S3 and returns detected labels plus when they occur in the video. Labels include objects, events, concepts and activities.docs.aws.amazon.com

It has a particularly natural architecture if your DAM's underlying storage is already AWS:

DAM → S3 → Rekognition → SNS/SQS/Lambda → DAM metadata

The asynchronous design is well suited to large video libraries: submit the job, receive completion through SNS, then retrieve the results.docs.aws.amazon.com

AWS also has Custom Labels, which is interesting if your organization has proprietary categories—for example, automatically identifying your company's products, equipment or specific visual assets. Custom Labels generally involves training your own model rather than simply turning on generic tagging.docs.aws.amazon.com

Amazon Rekognition Video documentation

3. Azure AI Video Indexer — best for rich metadata

This is worth serious consideration if your DAM needs more than visual tags.

Azure Video Indexer analyzes both visual and audio aspects of videos and exposes the resulting insights through APIs.learn.microsoft.com

That makes it useful for generating a much richer metadata record, potentially including things such as:

• visual concepts
• spoken-word transcription
• OCR/text
• people/faces
• scenes/shots
• other audiovisual insights

The API supports uploading media either via a URL (including Azure Blob/SAS URLs) or as a byte array, which gives you flexibility in connecting it to an existing DAM.learn.microsoft.com

One caveat: Microsoft currently requires a paid/standard account for API access, rather than the trial account.learn.microsoft.com

Azure Video Indexer API reference

4. TwelveLabs — most interesting for an AI-native DAM

I'd look at TwelveLabs if by "tagging" you actually mean understanding the contents of the video.

Its Marengo models analyze visual and audio information, including actions, objects, events, OCR, logos, ambient sound, music and speech.docs.twelvelabs.io

It can also generate titles, topics and hashtags directly from video content.docs.twelvelabs.io

More importantly, it supports natural-language video search. You could potentially let a DAM user search for:

“interview with someone wearing a red jacket in an outdoor setting”

rather than requiring the system to have generated exactly the tag red jacket.

Its search API can combine visual, audio and transcription modalities, and returns the relevant video moments/timestamps.docs.twelvelabs.io

That makes it particularly compelling for a next-generation DAM where semantic discovery is as important as conventional metadata tagging.

TwelveLabs API documentation

What I'd choose for your team

If you primarily need automatic DAM keywords:
→ Google Video Intelligence

If your infrastructure is already AWS/S3:
→ Amazon Rekognition Video

If you want a broad metadata package including audio/transcription:
→ Azure AI Video Indexer

If you want users to search the actual meaning/content of videos:
→ TwelveLabs

One important architectural point: I wouldn't necessarily write the vendor's raw labels directly into the DAM. I'd put a small metadata-normalization layer between the AI API and DAM:

Video → AI API → normalized taxonomy → confidence filtering → DAM metadata

For example, map vendor outputs such as automobile, car, and motor vehicle into your organization's canonical Vehicle > Automobile taxonomy. You can also set a confidence threshold and send low-confidence tags for human review.

If you tell me which DAM you're using (Bynder, Brandfolder, Adobe Experience Manager, Canto, Cloudinary, Aprimo, etc.), I can narrow this down to the APIs that integrate most cleanly with it and sketch the actual integration architecture.

• Core Feature : Shot change detection, explicit content detection, object tracking, and speech transcription.
• Tagging Output : Generates hierarchical labels for objects inside video segments and creates searchable closed captions or transcripts.
• Integration Pattern : Supports synchronous requests for short videos and asynchronous JSON/REST operations for large-scale media file processing.