AI Vision

What it does

Analyses the visual content of images and video. Core capabilities include:

• Image Analysis — detects objects, scenes, colours, brands, and generates captions.

• OCR (Read API) — extracts printed and handwritten text from images and PDFs.

• Face — detects, verifies, and identifies human faces (subject to responsible AI access controls).

• Spatial Analysis — counts people and tracks movement in video streams (requires on-premises container).

When you'd use it

• You have image or video content and need to extract structured information without training a custom model.

• You need to make images searchable (auto-tagging) or need to moderate visual content.

• You are processing scanned documents at scale and need to extract text quickly.

Sample use case

A retail chain wants to automatically tag product images uploaded by suppliers so that items are discoverable in their e-commerce catalogue. Azure AI Vision's Image Analysis API tags each image with object labels and dominant colours; the results are stored in Azure AI Search to power filtered product browsing.

Example reference architecture

Pricing & How Costs Work

Azure AI Vision is priced per 1,000 transactions (API calls). Each feature is metered independently, and different features are priced at different rates:

Key pricing mechanics:

• Free tier (F0): 5,000 transactions/month for Image Analysis and OCR; sufficient for development.

• Standard (S1) tier: Per-1,000-transaction pricing with volume tiers (first N million transactions at a higher rate, subsequent at a lower rate).

• Face API: Requires access approval under Microsoft's Responsible AI policy. Face identification/verification is more expensive than face detection.

• Spatial Analysis container: Unlike the cloud APIs, Spatial Analysis runs inside a Docker container on an edge device (or VM). The cost model is a flat per-device-per-day charge, making it predictable and independent of frame rate or stream volume.

• OCR pricing split: Images are priced per transaction; PDFs are priced per page. Submitting a 50-page PDF counts as 50 billable units.

• Combining features: When you call Image Analysis with multiple visual features enabled (e.g. tags + captions + objects), it is still one API call and one transaction.

What Drives Costs

Cost optimisation levers:

• Only request the visual features you actually need — avoid requesting all available features if only tags are used downstream.

• For OCR workloads, split multi-page PDFs and only submit pages that require extraction (e.g. skip cover pages or blank pages).

• Cache analysis results keyed on image hash — identical images submitted multiple times each incur a full transaction cost.

• Use thumbnail generation or smart crops only when the UI requires it, rather than generating crops for every image by default.

• For Spatial Analysis, consolidate multiple camera feeds into the minimum number of containers to minimise the per-device-day charge.

Azure Cost Data — Meters & Meter Subcategories

Azure AI Vision appears under the Cognitive Services service family in Cost Management. Resource type: Microsoft.CognitiveServices/accounts with kind ComputerVision.

Meter subcategory notes:

• Computer Vision and Face appear on separate meters in billing exports, even though they are hosted under the same ComputerVision kind resource — this is because they are conceptually separate APIs despite sharing the account.

• OCR has a dual meter: image calls generate a Transactions meter row; PDF submissions generate a Pages meter row. Both can appear in the same billing period for the same resource.

• The Spatial Analysis meter Quantity represents device-days rather than API call counts, which makes it structurally different from all other Vision meters — important to note when building a cost model or anomaly detection rule.

• In Cost Management, filtering by MeterSubCategory = Standard S1 and ServiceName = Cognitive Services with MeterName containing Computer Vision or Face is the quickest way to isolate Vision costs from other Cognitive Services on a shared account.

• If the Vision resource is shared across teams, resource tagging is the primary cost allocation mechanism — billing data does not carry caller-identity information.

Common Developer Mistakes That Drive Up Costs

• Requesting all visual features when only one is needed. A single Image Analysis call can request tags, captions, dense captions, objects, brands, colours, and smart crops simultaneously. Each call is still one transaction regardless of which features are enabled — but enabling dense captioning when only tags are consumed downstream adds a higher per-transaction rate unnecessarily.

• Submitting every page of a multi-page PDF to OCR without filtering. The Read OCR API bills per page for PDFs. Submitting a 100-page document to extract text from 3 relevant pages bills all 100 pages. Pre-filtering to submit only the relevant pages is one of the highest-impact optimisations for document-heavy workloads.

• Re-analysing unchanged images on every pipeline run. Image processing pipelines that re-submit all images in a blob container on each run (rather than tracking which have been processed) generate repeated transactions for identical images. A product catalogue that adds 10 images per day but reprocesses 10,000 on each daily run pays for 10,000 transactions when only 10 are new.

• Using face identification/verification when face detection is sufficient. Face identification (matching faces against a person group) is priced at a higher rate than basic face detection. Using identification in scenarios where detection is all that is needed (e.g. checking whether a face is present, not who it is) overpays for the capability used.

• Generating smart crops or thumbnails for every image as a default. Smart crop and background removal are billed per transaction. Applying these to every image in a catalogue as a blanket default — rather than only for images used in UI contexts where cropping matters — inflates call volume.

• Not caching analysis results by image hash. Identical images submitted multiple times (e.g. the same product hero image appearing in multiple contexts) each incur a full transaction charge. Caching keyed on the image content hash eliminates duplicate analysis costs.

What Could Make Your Bill Go Up or Down Next Month

Could go UP:

• A new supplier onboarding event brings a large batch of product images to be analysed, generating a one-time spike in Image Analysis transactions.

• Dense captioning is enabled globally after a feature change, increasing the per-transaction rate for all image analysis calls.

• OCR is extended to process multi-page PDF reports in addition to single-page images, significantly increasing billable pages.

• Background removal is added as a default step in the image upload pipeline rather than being triggered on demand.

• Face identification is enabled for a new security feature, replacing detection-only calls with higher-rate identification calls.

• A pipeline bug removes the processed-image tracking check, causing all images to be re-analysed on every run.

Could go DOWN:

• Image analysis results are cached by content hash; re-submissions of unchanged images are served from cache.

• Multi-page PDF submissions are filtered to submit only pages containing relevant content rather than the full document.

• Visual features are audited and dense captioning is disabled for workloads where standard captions are sufficient.

• Smart crop and background removal are moved from a default pipeline step to an on-demand API call triggered only when the image is selected for a UI context.

• A seasonal drop in new product uploads reduces the volume of new images requiring analysis.

Most Common Optimisation Techniques