The Tool Desk
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How the image analyzer works
The application sits between the user and Google Cloud Vision. A request reaches your Cloud Run service, the service checks the image and chooses an input method, and a server-side call asks Vision for specific annotations. Your application then reshapes the response into something useful to the caller.
- Receive: accept an image upload or a reference to an image.
- Validate: check the file type and size, and reject unsupported input before calling Vision.
- Choose access: send the image bytes inline, reference an object in Cloud Storage, or use a publicly accessible URI.
- Analyze: request the appropriate Vision feature or combination of features.
- Return: extract useful fields from Vision’s response and provide them in your application’s response format.
- Host: deploy the HTTP application as a Cloud Run service and set its access, capacity, and resource configuration.
This design keeps API credentials on the server rather than exposing them to a browser or mobile client. The application can offer a single endpoint while allowing callers to request different analysis outcomes.
Choose Vision features for the job
“Image analysis” is not one fixed model or response. Cloud Vision offers separate annotation features. Choose based on the user outcome instead of requesting every feature for every image; each applied feature can affect cost and response handling.
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| Need | Vision feature | What it returns or when to use it |
|---|---|---|
| Read text embedded in an ordinary image | TEXT_DETECTION |
OCR intended for text in general images, including sparse text within a larger image. |
| OCR a dense scanned document | DOCUMENT_TEXT_DETECTION |
Document-oriented text extraction. For structured parsing or entity extraction from dense documents, Google recommends considering Document AI. |
| Describe the general contents of an image | Label detection | Generalized labels with confidence and topicality information. |
| Find objects and their locations | Object localization | Object labels and normalized bounding polygons. |
| Locate faces | Face detection | Facial locations and attributes. It does not identify a specific individual. |
| Screen for defined explicit-content categories | SafeSearch | Likelihood values for adult, spoof, medical, violence, and racy categories. |
| Recognize a known place or brand mark | Landmark or logo detection | Names or descriptions, confidence, and location data as documented for the feature. |
| Find web matches or related images | Web detection | Web entities and information about matching images or pages. |
| Get dominant colors or crop suggestions | Image properties or crop hints | Image properties include color information; crop hints can be requested for multiple aspect ratios. |
These are distinct outputs, not interchangeable descriptions. For example, label detection supplies broad concepts, while object localization supplies labels tied to positions in the image. OCR output includes text structures, and localization features provide geometry such as bounding boxes or polygons. Shape your response around the output the caller actually needs.
See Google Cloud Vision’s feature list for documented features and their outputs.
Choose how the service accesses images
The Vision request can identify image content in three documented ways. Pick one based on where the image originates, how large it is, and who is allowed to access it.
| Input method | How it works | Important consideration |
|---|---|---|
| Inline base64 content | The service includes the encoded image bytes in the request. | Convenient for an HTTP upload already received by the service; request-size limits still apply. |
| Cloud Storage URI | The request points to an image stored in Cloud Storage. | Configure storage access deliberately, especially for private images and retention requirements. |
| Public URI | The request references an image that is publicly accessible. | Public accessibility may be inappropriate for private or sensitive images. |
The API documentation describes these input choices, but it does not decide your application’s privacy policy, storage retention, or access-control design. Avoid making an image public merely to simplify processing. Validate the content type and size at your service boundary, and define how long uploaded or stored images remain available.
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Review the Vision API request guide for supported image sources and request structure.
Make an authenticated annotation request
The REST endpoint for synchronous image annotation is POST https://vision.googleapis.com/v1/images:annotate. The authenticated JSON body contains a requests list. Each annotation request supplies an image source and one or more requested feature types. Multiple features can be requested for an image, so a service can combine, for example, OCR and labels when both are needed.
A simplified request has this shape; replace the example source and feature with values appropriate to your service:
{
"requests": [
{
"image": {
"content": "BASE64_ENCODED_IMAGE"
},
"features": [
{
"type": "TEXT_DETECTION"
}
]
}
]
}This is a request-body illustration, not a complete runnable application: the HTTP call must be authenticated, and the image field can instead use a supported Cloud Storage or public URI source. Google also provides client libraries; the REST approach makes the request and response shape explicit.
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Keep authentication on the Cloud Run side. Use the service identity and permissions appropriate to the project, and keep secrets out of source code. Exact IAM permissions depend on how the service accesses Google Cloud resources; confirm them against the current Cloud Run service identity guidance when configuring the deployment.
Shape the response for your application
Vision responses can contain feature-specific detail. Return only the fields that matter to the caller, with names and structure that your application can keep stable even if it later adds another Vision feature.
- For labels, present the label and relevant confidence or topicality values.
- For object localization, preserve each label and its normalized polygon so a client can draw boxes or markers over the original image.
- For OCR, return the extracted text and, when needed, the text hierarchy or position information.
- For SafeSearch, present the likelihood ratings by documented category rather than treating them as a binary guarantee.
- For face detection, expose detection results without implying that they identify a person.
Also define how your endpoint reports invalid input, authentication failures, quota errors, and upstream API failures. A clear application-level error is more useful to clients than returning unfiltered upstream JSON.
Deploy the HTTP application to Cloud Run
Cloud Run runs containerized HTTP services. Your application must listen on the TCP port supplied in the PORT environment variable; the documented default is 8080. You can deploy a container image or use a source-code deployment flow. Deployment configuration lets you choose a region, authentication behavior, scaling, concurrency, resources, request timeout, secrets, and service account.
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- Package the application: build a container that starts the HTTP server and listens on
PORT. - Choose access control: make the service public only if unauthenticated requests are intended. Otherwise, require authentication and restrict access to approved callers.
- Set the service identity: assign an identity suited to the resources the service must access; do not embed credentials in the application.
- Configure capacity: set concurrency, memory, timeout, and instance scaling with the image-processing workload and Vision limits in mind.
- Deploy: publish the container image or use the documented source deployment flow, then test the service endpoint with valid and invalid requests.
Use Google’s Cloud Run documentation and deployment guide for current deployment options and configuration labels.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Plan for quotas, payload limits, and traffic
Vision quotas are enforced at the Google Cloud project level, separately from Cloud Run’s capacity. The quota page retrieved in 2026 lists 1,800 requests per minute for common Vision request types and 1,800 per-minute feature quotas for label and text detection. It also lists a 20 MB image file limit, a 10 MB JSON request-object limit, up to 16 images in a synchronous images:annotate request, and up to 2,000 images in an asynchronous image batch request. These values are subject to change, and some limits apply per image or page while others apply per request; verify the current quota table and your project’s quotas before designing around them.
Cloud Run can scale out automatically, but more service instances do not increase the project’s Vision quota. If requests arrive faster than Vision can accept them, the application needs an explicit strategy, such as limiting concurrent API calls, returning a useful retry response, or queuing work for asynchronous processing. Synchronous annotation is suited to interactive requests; large collections may fit an asynchronous batch workflow better.
Cloud Run normally scales to zero when idle. Minimum instances can keep capacity warm at the cost of changing resource use, while maximum instances can bound scale and protect a backing service. Balance startup latency and request bursts against Vision quotas and your application’s concurrency. See Cloud Run autoscaling documentation and Vision API quotas and limits.
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Estimate costs by feature and workload
Vision pricing is based on images and the features applied: each feature applied to an image is a billable unit, and multi-page files are billed page by page. The Google Cloud pricing page retrieved in 2026 lists the first 1,000 monthly units as free for features in its table. For monthly usage from 1,001 through 5,000,000, it displayed $1.50 per 1,000 units for label detection, text detection, document text detection, face detection, landmark detection, logo detection, and image properties; $3.50 for web detection; and $2.25 for object localization. The page’s publication date was not stated, and higher tiers have different rates. Check the live pricing page and currency-specific SKUs rather than treating these retrieved figures as a quote.
Estimate the Vision portion by counting images or pages and the features applied to each. Then include Cloud Run configuration and traffic, plus any storage or network services your design uses. Cloud Run’s cost depends on its resource and scaling configuration, including whether minimum instances stay warm; there is no single total that applies to every analyzer. Use the Vision pricing page and Cloud Run pricing page for current rates.
Operate and improve the analyzer
Once deployed, monitor both sides of the application: Cloud Run request volume, latency, startup behavior, and errors, alongside Vision API failures, quota consumption, and feature usage. Test realistic image sizes and traffic patterns, including malformed files and quota-related failures. Keep the endpoint’s accepted image types and maximum upload size aligned with the limits in force for your project.
For privacy-sensitive use, restrict who can invoke the service, avoid public image URIs unless public access is intentional, and establish image retention rules for uploads and Cloud Storage objects. For reliability, choose timeouts and concurrency based on observed workload rather than allowing Cloud Run autoscaling to send unbounded pressure toward a project-level API quota.
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