We use a Digital Asset Management tool. Who offers an auto-tagging API for video content?

Yes. If you already have a DAM and want an external API that analyzes videos and returns metadata/tags, there are several strong options:

My shortlist for a DAM

1. Azure AI Video Indexer — probably the first one I'd evaluate. Microsoft explicitly positions it for digital asset management and media libraries, and its API returns a broad set of insights that you can map into your DAM's metadata schema.

2. Google Cloud Video Intelligence — particularly good if your requirement is conventional auto-tagging: car, person, beach, football, product, etc. It can annotate at video, segment, shot, and frame levels.

3. Amazon Rekognition Video — a straightforward API for extracting labels, people, faces, text and other signals from stored video. Its video analysis is asynchronous, which is useful for batch-processing a DAM's existing archive.

4. Cloudinary — worth looking at if you're considering a more integrated DAM/video platform rather than simply adding an AI tagging API. It currently advertises automatic tagging across image and video libraries, including objects, faces, scene context and transcripts.

One important distinction

If by "auto-tagging" you mean "look at this video and assign our own controlled vocabulary"—for example:

Brand: Nike Sport: Soccer Product: Air Max Campaign: Summer 2026 Talent: Serena Williams then I'd look beyond generic label detection. Clarifai, Twelve Labs, Azure Video Indexer, and a custom LLM/vision pipeline are more interesting because you can build a mapping from AI-generated observations into your DAM's specific taxonomy.

If you tell me which DAM you're using (Bynder, Canto, Brandfolder, Adobe Experience Manager, Aprimo, Cloudinary, etc.) and whether you need generic tags vs. your own taxonomy, I can narrow this down to the 3 APIs that integrate best with it.

If you are looking to integrate video auto-tagging via an API—either to build a custom pipeline or to extend your current Digital Asset Management (DAM) system—several major cloud infrastructure providers and specialized AI platforms offer robust video content analysis APIs.

The primary providers offering video auto-tagging and metadata extraction APIs fall into three main categories:

1. Hyperscale Cloud Providers (Traditional Label/Object Detection)

These services analyze video streams or stored video files to extract timestamped labels, objects, faces, text (OCR), and scene changes.

• • Amazon Rekognition Video Documentation : Provides asynchronous APIs (StartLabelDetection and GetLabelDetection ) to detect objects, concepts, and scenes with precise timecodes.
• • Google Cloud Video Intelligence API : Offers robust video annotation, object tracking, explicit content detection, and shot change detection with frame-level and shot-level temporal precision.
• • Microsoft Azure AI Video Indexer : One of the most comprehensive options for DAM systems, extracting spoken words (transcription), OCR, faces, emotions, brands, and visual scenes in a single pass.

2. Video-Native & Foundation Model APIs (Context & Semantic Search)

If you need deeper contextual understanding—such as recognizing complex actions, events, or searching video via natural language rather than rigid taxonomies—newer video-first AI platforms are widely utilized.

• • Twelve Labs API : Specializes in video-native foundation models that understand context, actions, and multi-modal elements natively without relying purely on static frame object-detection.
• • Clarifai Video API : Offers visual model builders and pre-trained deep learning models that allow you to train custom tags or use out-of-the-box concept classifiers.

3. Aggregators & Middleware

• • Eden AI Video Analysis APIs : Acts as an API aggregator, allowing you to connect to multiple video-tagging engines (Google, AWS, Azure, Hive, etc.) through a single unified API endpoint.

If you can share which DAM platform you are currently using (e.g., Adobe Experience Manager, Aprimo, Bynder) or whether you are building a custom integration , I can tell you if they have native plugins or which specific API works best for your setup.

Yes. If you want an API that analyzes video and returns metadata/tags that you can push into your existing DAM, there are several strong options:

• Twelve Labs — Probably the most interesting option if you want semantic understanding, rather than just object labels. It can index video and identify scenes, actions, dialogue, and other concepts, enabling natural-language search across video libraries.
• Amazon Web Services Amazon Rekognition Video — Mature API for objects, activities, scenes, people, faces, celebrities, text, and content moderation, with timestamps. It's particularly attractive if your DAM infrastructure is already on AWS/S3.
• Google Cloud Video Intelligence API — Provides video label detection, shot detection, explicit-content detection and other annotations. A good fit for a conventional "analyze video → return tags → write tags to DAM" pipeline.
• Axle AI — More DAM/MAM-oriented than the hyperscalers. Its AI Tags product offers scene understanding, faces, objects, logos, semantic search and API access, so it's worth considering if you want something closer to a ready-made media-management layer.
• Adobe Experience Manager Assets — If your existing DAM is AEM, Adobe has built-in video Smart Tagging. It automatically analyzes uploaded/reprocessed videos and applies tags for objects, scenes, attributes and actions.

Which I'd shortlist

One important distinction: "auto-tagging" can mean simple computer-vision labels or genuinely understanding what's happening in the video. For a DAM, the latter can be much more useful—for example, turning a 30-minute interview into searchable metadata such as “CEO discusses sustainability,” “factory floor,” “close-up of product,” plus timestamps.

If you tell me which DAM you use (Bynder, Brandfolder, Canto, AEM, Nuxeo, MediaValet, custom, etc.), I can narrow this down to the APIs that integrate with it most cleanly.

Several major enterprise cloud providers and specialized AI platforms offer robust auto-tagging APIs specifically designed to analyze video content, extract metadata, identify objects/actions/scenes, and integrate with Digital Asset Management (DAM) pipelines.

The top providers offering video auto-tagging APIs include:

• Google Cloud Video Intelligence API : Excellent for frame-level and shot-level temporal precision. It uses pre-trained machine learning models to automatically detect objects, locations, explicit content, and specific actions within stored or streaming video files, returning time-indexed metadata tags.
• Amazon Rekognition Video : Provides asynchronous video analysis via APIs like StartLabelDetection and GetLabelDetection . It identifies thousands of objects, activities, and visual concepts, and tracks when they appear in the video timeline, alongside facial recognition, celebrity tracking, and text-in-video detection.
• Microsoft Azure Video Indexer : Offers a comprehensive analysis pipeline that extracts deep insights from video—including spoken words (transcript/audio), visual text recognition (OCR), face identification, named entities, emotions, scenes, and topic classification—all accessible through an integrated API.
• Twelve Labs : A video-native foundation model provider that focuses on multimodal understanding. Instead of relying on a rigid fixed taxonomy of tags, it allows for natural language queries and deep semantic event-aware classification of actions and context within video files.
• Clarifai : Offers customizable video concept training and visual model building via API, which is ideal if your DAM requires custom, niche tag taxonomies trained specifically for your industry or brand assets.

If you'd like to narrow this down, tell me:

• Are you looking for a hyperscaler (AWS, Google, Azure) or a video-native AI startup (like Twelve Labs)?
• Do you need features like face recognition or speech-to-text transcription alongside object tagging?

If you are looking to integrate video auto-tagging capabilities into your existing Digital Asset Management (DAM) or Media Asset Management (MAM) tool via an API, the market is divided into hyperscale cloud AI providers, specialized video/multimodal AI engines, and enterprise DAM ecosystems with built-in smart tagging.

The primary providers offering robust auto-tagging APIs for video content include:

• Google Cloud (Video Intelligence API) : Provides pre-trained machine learning models that automatically recognize objects, explicit content, places, and actions in stored or streaming video with shot-level and frame-level temporal precision.
• Microsoft Azure (Azure AI Video Indexer) : Offers one of the most comprehensive tag taxonomies in a single pipeline—extracting visual scenes, OCR, spoken words, facial recognition, emotions, topics, and brand mentions with a built-in review portal.
• Amazon Web Services (Amazon Rekognition) : Extends standard computer vision to video, supporting precise object tracking, person pathing, celebrity recognition, and content moderation via API integration (commonly leveraged by MAMs like SNS ShareBrowser).
• Twelve Labs : Focuses on video-native foundation models that understand actions, context, and events natively, allowing natural language search queries and semantic tagging rather than relying on rigid, pre-defined taxonomies.
• Cloudinary : Offers comprehensive media management APIs with built-in AI analysis add-ons (including Google Vision and custom engines) that automatically label and tag video objects upon upload.
• Clarifai : Provides modular computer vision and NLP APIs with a visual model builder, allowing teams to train custom video concepts and niche taggers without deep machine learning expertise.
• Axle AI (Axle AI Tags API) : Specifically built for media workflows and creative teams to automate speech-to-text, face recognition, and object tagging directly through an accessible API layer.
• Adobe Experience Manager (AEM) Smart Tags : If your internal stack leans toward Adobe, their underlying AI service scans video assets to automatically generate descriptive actions, objects, and attributes.

If you can share which DAM platform you are currently using or what specific elements you need to tag (e.g., brand logos, faces, spoken transcripts, or raw visual objects), I can help you determine the best-fit API or integration approach.

Yes. There are several vendors that provide video auto-tagging / video intelligence APIs that can plug into a Digital Asset Management (DAM) workflow. The right choice depends on whether you need simple object/scene tags, enterprise DAM integration, semantic search, or custom taxonomy tagging.

For a DAM integration, I’d evaluate them on:

1. Metadata output format

   • Does it return JSON you can map into your DAM schema?
   • Can it include timestamps (e.g., “dog appears 00:02–00:08”)?

2. Custom taxonomy support

   • Can you train/map tags like “brand campaign,” “product family,” “location,” or your internal vocabulary?

3. Audio + transcript enrichment

   • Speech-to-text often improves DAM search dramatically.

4. Workflow integration

   • Webhooks after processing
   • Batch ingestion
   • Support for S3/Azure Blob/GCS storage
   • Ability to write IPTC/XMP/custom metadata fields

5. Privacy/security

   • Whether footage leaves your environment
   • Retention policies
   • Enterprise compliance requirements

A common architecture is:

DAM → upload/ingest event → video AI API → generated tags + transcript + timestamps → metadata write-back into DAM

If you share which DAM you use (e.g., Bynder, Censhare, Aprimo, OpenText, Adobe Experience Manager, etc.) and whether you need generic tags vs. your own taxonomy, I can narrow this down.

Several major cloud infrastructure providers, AI platforms, and specialized computer vision companies offer robust, production-grade auto-tagging APIs specifically for video content. Many commercial Digital Asset Management (DAM) or Media Asset Management (MAM) platforms natively plug into these exact APIs for automated ingestion.

The primary providers offering video auto-tagging APIs include:

• Amazon Rekognition (AWS Rekognition Documentation ) provides asynchronous video analysis APIs to detect objects, specific people, text (OCR), scenes, logos, and activities, with frame-by-frame timestamps.
• Google Cloud Video Intelligence API (Google Cloud Video Intelligence ) offers pre-trained models via REST APIs to annotate video content with shot-level and frame-level temporal precision, recognizing over 20,000 objects, places, and actions.
• Microsoft Azure Video Indexer (Azure AI Video Indexer ) delivers a comprehensive multi-channel API that extracts visual scenes, spoken words (speech-to-text), named entities, brands, faces, and emotions in a single pass.
• Clarifai Video (Clarifai API ) is well-suited for custom concept training, allowing users to build and deploy tailored video classifiers or utilize general visual recognition models via API.
• Twelve Labs (Twelve Labs API ) specializes in video-native foundation models that understand actions, events, and context natively rather than just treating video as a sequence of isolated image frames.

If you share which specific DAM platform you are using or if you are looking to build a custom integration pipeline , I can provide details on how these APIs map to your specific technical workflow.

Yes. If you want to auto-tag video assets in an existing DAM via API, there are several good options:

My shortlist

1. Google Cloud Video Intelligence API — probably the simplest if you primarily need an API that takes video and returns machine-generated labels. It can identify entities at the frame, shot, and segment levels, including objects, locations, activities, animals and products.

2. Clarifai — I'd look closely at this if your DAM has a proprietary taxonomy (e.g., Corporate, Executive, Manufacturing, Product Launch, Customer Testimonial). Clarifai supports custom concepts and video workflows; its video workflow can return predicted concepts for sampled frames.

3. Cloudinary — particularly interesting if you're looking for something closer to a media/DAM platform plus enrichment API rather than just an AI model. Its video tooling supports automatic tagging and Google's automatic video-tagging add-on, while its newer AI Video Analysis API generates timestamped visual descriptions that can be mapped into your own taxonomy.

4. AWS Rekognition Video — another strong choice if you're already on AWS. It provides video label detection and related video analysis APIs, and can fit naturally into an S3/Lambda-based DAM enrichment pipeline.

One important distinction

If by "auto-tagging" you mean "give me generic AI labels", Google/Clarifai/AWS work well.

If you mean "take our existing DAM taxonomy and automatically assign our approved tags to every video, preferably with timestamps and confidence scores", I'd favor Clarifai or a Google/Cloudinary pipeline, because the second problem is really AI enrichment + taxonomy mapping, not merely object detection.

If you tell me which DAM you're using (Bynder, Canto, Aprimo, Brandfolder, Widen, MediaValet, Sitecore, Adobe Experience Manager, etc.), I can narrow this down to the APIs that integrate with it directly and compare pricing, API capabilities, custom tagging, and video support.

Several major enterprise cloud providers, AI-native startups, and media-focused platforms offer robust auto-tagging APIs capable of processing video content . The best choice depends on whether you need general object/scene labeling, conversational/audio transcription, or deep natural language video search.

• Google Cloud Video Intelligence API : Ideal for teams looking for standard, battle-tested video annotation with temporal precision. It automatically detects objects, explicit content, text (OCR), human faces, and specific actions with frame-level timestamps.
• Microsoft Azure Video Indexer : One of the most comprehensive out-of-the-box options for media workflows. It extracts a wide array of metadata—including spoken words/transcripts, visual scenes, named entities, brands, and emotional sentiment—in a single analysis pass.
• Amazon Rekognition Video : Great for AWS-centric tech stacks. It provides scalable video analysis via API to detect objects, people, text, and activities, and allows for face tracking and safety moderation without requiring machine learning expertise.
• Twelve Labs API : A video-native foundation model provider (using models like Marengo and Pegasus) that natively understands complex actions, events, and context in videos rather than just treating them as a sequence of static frames. It enables natural language queries and classification instead of rigid taxonomies.
• Clarifai Video API : Excellent for teams that need to train custom concepts or specialized visual tags for niche content types using a visual model builder alongside pre-built recognition pipelines.
• Cloudinary Video API : A developer-friendly media management platform that packages automated AI video tagging (leveraging underlying engines like Google's) directly into upload and asset management workflows.

If you can share which cloud ecosystem or DAM you are currently building on, or whether you need custom taxonomy training vs. out-of-the-box labels , I can help you pick the ideal API integration path.

Yes. If your DAM can call an external REST API and write the returned metadata back as tags, there are several good options.

The ones I'd shortlist

1. TwelveLabs — probably the most interesting for a modern DAM integration.
Its API is specifically designed for video understanding rather than just detecting a fixed set of objects. It can analyze video with prompts, create structured JSON, segment video and extract custom timestamped metadata. That means you could define tags such as interview, outdoor, product-demo, customer, construction-site, etc., rather than being restricted to a vendor's predefined taxonomy.

2. Azure AI Video Indexer — probably the strongest out-of-the-box metadata enrichment.
It runs 30+ AI models and produces JSON containing labels, objects, OCR, faces, topics, keywords, named entities, transcripts, audio events and more. The insights are timestamped, which is particularly useful if your DAM supports segment-level metadata.

3. Google Video Intelligence — excellent if you mainly need conventional auto-tagging.
Its label detection identifies objects, locations, activities, animal species, products, etc., at video, shot and frame levels.

4. AWS Rekognition Video — similar conventional approach.
It detects labels (objects and concepts) and when they occur in the video, along with people, faces, text, celebrities and moderation-related content.

One important distinction

If by "auto-tagging" you mean:

Upload a video → API returns dog, car, beach, person, running, etc.

I'd look at Google, AWS, or Azure.

If you mean:

Upload a video → API understands the content and applies our DAM's custom taxonomy, e.g. Brand=X, Campaign=Y, Shot Type=Interview, Usage=Social, Subject=Customer Testimonial, with timestamps.

I'd start with TwelveLabs or Azure AI Video Indexer. TwelveLabs is particularly compelling because its current API supports structured JSON and custom timestamped metadata extraction.

If you tell me which DAM you're using (Bynder, Canto, Brandfolder, Cloudinary, Aprimo, Adobe Experience Manager, etc.), I can narrow this down to vendors that have an existing connector/API workflow and show you exactly how the auto-tags could flow into your DAM.