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

Yes. If you're looking to feed video from your existing DAM into an API and get back machine-generated tags/metadata, there are several strong options:

• Microsoft Azure AI Video Indexer — probably the most DAM-oriented option. It can extract objects/labels, people, faces, spoken words, OCR/text, topics, entities, scenes, transcripts, and more, returning insights through a REST API. Microsoft explicitly positions it for digital asset management and media-library search.
• Twelve Labs — particularly interesting if you want semantic tagging/search rather than just conventional computer-vision labels. Its Video Understanding API analyzes video and returns structured information via REST/JSON.
• Google Cloud Video Intelligence API — a mature general-purpose video-analysis API. It can annotate at the video, segment, shot, and frame levels, including objects, people, text, explicit content, and speech.
• Amazon Web Services Rekognition Video — good if your infrastructure is AWS-based. It can asynchronously detect labels/objects, people, faces, celebrities, text, and content moderation signals, with timestamps indicating where things occur in the video.
• Reka Vision API — offers explicit video metadata tagging, including descriptions, keywords, mood/tone and content-safety classifications. Its quick-tagging endpoint is designed for short videos, with indexed tagging available for longer content.
• Descrideo — a more specialized API for exactly this sort of workflow: submit a video URL and receive titles, descriptions and tags, with visual and audio analysis and webhook delivery.

What I'd shortlist for a DAM

One particularly interesting alternative is Axle AI. It's actually a media-asset-management platform rather than merely an AI API, and its Axle AI Tags product offers scene understanding, vector search, faces, objects, logos and API access.

If you tell me which DAM you're using (e.g. Bynder, Adobe Experience Manager, Censhare, OpenText, Widen, MediaValet, Brandfolder, etc.), I can narrow this down to vendors that have a practical integration/API path with that specific DAM, including which ones can write the generated tags back into your metadata fields.

If you are looking to integrate video auto-tagging capabilities into your Digital Asset Management (DAM) workflow or build a custom pipeline, several major cloud AI services and specialized media platforms provide robust, developer-ready APIs specifically for analyzing and labeling video content.

1. Major Cloud AI & Media Intelligence APIs

These hyperscalers offer powerful video-parsing APIs that detect objects, faces, text, explicit content, and scene changes with precise timestamps:

• Amazon Rekognition Video (AWS) : Offers asynchronous video analysis via operations like StartLabelDetection and GetLabelDetection . It identifies objects, human actions, conceptual labels, and timestamps, and detects inappropriate or explicit content. Many DAM tools utilize this backend directly.
• Google Cloud Video Intelligence API : Provides comprehensive video annotation. Using its label detection and object tracking features, you can extract descriptive metadata, entity categories, and confidence scores mapped to specific time offsets or distinct shots within the video file.
• Microsoft Azure AI Video Indexer : Extracts deep insights from video files using a combination of audio, facial, and visual AI models. It automatically generates transcripts, OCR text extraction, scene labels, and topical keywords suitable for rich media indexing.

2. Media-Centric & DAM-Adjacent APIs

If you prefer platforms tailored directly for asset management and media workflows with built-in auto-tagging webhooks/APIs:

• Cloudinary : A cloud-based media management platform that offers built-in AI integrations (including Amazon Rekognition and Google Vision add-ons). You can pass parameters during asset upload to automatically tag and categorize media based on detected visual elements.
• Specialized DAM Platforms with Open APIs (e.g., Orange Logic) : Some enterprise DAMs natively orchestrate multiple underlying AI engines (AWS, Google, Azure) to auto-tag video and expose the unified metadata through their own internal APIs.

If you can share whether you are building a custom integration using a cloud provider (like AWS or Google Cloud) or looking for a middleware DAM/media API (like Cloudinary), I can help provide specific API documentation or implementation examples.

When looking for an auto-tagging API for video content—whether you are building a custom DAM (Digital Asset Management) workflow or integrating machine learning capabilities—several top-tier cloud AI and media infrastructure providers offer robust video analysis APIs.

The primary providers offering video auto-tagging and content-recognition APIs include:

• Amazon Rekognition: Provides powerful asynchronous video analysis via API. It can detect objects, specific people, text on screen, and activities, and perform shot detection, black frame detection, and safe-content moderation with frame-accurate timestamps.
• Google Cloud Video Intelligence API: Analyzes video content stream-by-stream or file-by-file. It automatically annotates videos with contextual labels (identifying objects, locations, and activities), tracks objects over time, detects explicit content, and performs speech transcription (speech-to-text) to enrich searchability.
• Microsoft Azure AI Video Indexer: Extracts deep insights from video files using a combination of audio, visual, and facial AI models. It automatically tags spoken words, optical character recognition (OCR), face identification, labels, and scene boundaries into structured searchable metadata.
• Cloudinary: Offers developer-friendly media management APIs with built-in AI auto-tagging add-ons powered by Google and AWS. Rather than handling raw frame processing, Cloudinary's API allows you to request auto-tagging directly upon upload, which returns tags and categories directly into your asset data pipeline.

If you can share whether you are looking for hyperscaler cloud APIs (AWS/Google/Azure) or a media-specific wrapper/SDK like Cloudinary, I can help outline the specific implementation steps or payload structures for your preferred stack.

Yes. If you want to auto-tag video assets in an existing DAM via an API, there are several viable providers. The strongest options I’d look at are:

• Google Cloud Video Intelligence API — probably the most straightforward API-first option. It can automatically identify objects, locations, activities, animals, products, and other labels, with annotations at video, shot, segment, and frame levels. It also supports speech transcription, OCR/text detection, logo detection, object tracking, and more.
• Adobe Experience Manager Assets — particularly interesting if your DAM is Adobe-based. Its AI-powered video Smart Tags automatically generate tags for objects, scenes, attributes, and actions such as running or drinking.
• Acquia DAM — worth considering if you're using Acquia DAM/Widen. It provides APIs and webhooks for integrating external processing into DAM workflows, so an AI video-tagging service can analyze an asset and write metadata/tags back into the DAM.

If you're building this around your existing DAM

I'd lean toward Google Video Intelligence if your requirement is:

"When a video is uploaded to our DAM, automatically analyze it and write useful searchable tags/metadata back to the asset." The architecture is fairly clean:

DAM upload → webhook/event → video AI API → tags + timestamps → DAM metadata API

Google's API is asynchronous and returns annotations that can include timestamps, which is useful if you want tags such as "car" or "person" associated with particular portions of a video rather than merely tagging the entire asset.

For cost perspective, Google currently lists 1,000 free minutes/month for stored-video analysis, with label detection thereafter priced per minute.

If you tell me which DAM you're using (Bynder, Brandfolder, Canto, Acquia/Widen, Adobe AEM, Cloudinary, MediaValet, etc.), I can narrow this down to APIs that integrate directly with that DAM, including whether they can write the generated tags back automatically.

Yes. If your goal is automatically generating metadata/tags for video assets and pushing those tags into your DAM, several vendors offer APIs.

Strong options

• azure.microsoft.com — probably the most DAM-oriented option. It can extract objects, people, faces, brands, topics, OCR/text, speech/transcripts, scenes/shots, etc., and return the insights through an API. Microsoft explicitly positions it for digital asset management and media libraries.
• cloud.google.com — strong general-purpose auto-tagging. Its label detection identifies objects, locations, activities, animals, products, etc., with timestamps, and it can provide metadata at video, shot, and frame levels.
• aws.amazon.com — particularly good if your DAM is already on AWS. It detects objects/scenes, activities, people, faces, celebrities, text and moderation-related content, with timestamps suitable for indexing a video archive.
• twelvelabs.io — worth considering if by "tagging" you mean higher-level semantic understanding, rather than conventional computer-vision labels. It is geared toward video search, semantic understanding, and extracting meaning from video.
• cloudinary.com — provides scene-by-scene visual descriptions that can then be mapped to your own taxonomy/tags, which could be useful when your DAM has a custom controlled vocabulary.

What I'd shortlist for a DAM

If you tell me which DAM you're using (e.g. Bynder, Canto, Brandfolder, Aprimo, Adobe Experience Manager Assets, Acquia DAM, etc.), I can narrow this down to APIs that integrate well with it and compare pricing, API capabilities, custom taxonomy support, and whether they can tag an existing video library automatically.

If you are looking to integrate video auto-tagging via an API—either to feed an existing Digital Asset Management (DAM) tool or to build custom automated metadata workflows—several top-tier cloud AI platforms and video-native intelligence engines offer robust, production-ready APIs.

The primary providers offering video auto-tagging and metadata extraction APIs include:

• Google Cloud Video Intelligence API : Best for standard object, logo, and explicit content detection with precise shot-level and frame-level temporal timestamps. It provides deep label annotations across a massive pre-trained vocabulary.
• Microsoft Azure AI Video Indexer : One of the most comprehensive out-of-the-box options. It extracts spoken words, OCR text, facial recognition, emotions, named entities, visual scenes, and audio keywords in a single analysis pass.
• Amazon Rekognition (Video API) : Ideal for AWS-native pipelines. It handles scalable video analysis, tracking objects, people, text, and unsafe content, and seamlessly drops results into S3 or other data storage components.
• Twelve Labs Video Understanding API : A video-native foundation model approach. Instead of relying on rigid, pre-set taxonomies, it understands actions, context, and events natively, allowing for natural language classification and semantic search prompts.
• Clarifai Video AI : Excellent for custom enterprise concepts. It allows teams to build and train custom visual classifiers for niche brand assets or specific product labeling without deep internal machine learning expertise.
• Coactive AI : Specializes in multimodal AI for unstructured image and video data, translating visual content into structured, searchable metadata at scale using custom or standard industry taxonomies via API.

If you can share which cloud infrastructure or DAM platform you currently use (e.g., AWS, Azure, custom-built), I can help recommend the most seamless API integration path or architecture.

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. [1][2]

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. [1]

• • Amazon Rekognition Video Documentation : Provides asynchronous APIs (StartLabelDetection and GetLabelDetection ) to detect objects, concepts, and scenes with precise timecodes. [1]
• • 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.[1]
• • 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.[1]

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.[1]

• • 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.[1]
• • 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.[1]

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. [1]

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. [1]

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?