facesearching and AWS Rekognition both deal with faces, but they serve fundamentally different markets and solve different problems. facesearching is a consumer-facing reverse face search engine: you upload a photo and it returns links to where that face appears across social media, news, blogs, and video. AWS Rekognition is a developer-facing cloud API that lets engineers embed facial analysis and recognition capabilities into their own applications. This comparison helps you understand which platform fits your specific needs — whether you are an individual verifying an online date or an engineering team building identity verification into a product. For a look at another cloud-based competitor, see our facesearching vs Microsoft Azure Face API comparison.
Platform Overviews
facesearching is designed for end users who want immediate answers from the public web. The workflow is intentionally simple: upload a photo, wait seconds, and review a ranked list of matches with clickable links to original sources. It indexes a broad mix of social media profiles, news articles, blog posts, and video thumbnails, so results come with useful context like a profile page or a news story. AWS Rekognition, by contrast, is a suite of machine learning APIs offered through Amazon Web Services. Developers call these APIs to detect faces in images, compare two faces, estimate attributes like age range and emotion, and build custom face collections within their own applications. It does not ship with a searchable index of the public web; you supply the image database yourself.
Feature Comparison
The two platforms differ significantly in their feature sets and intended purposes. The table below summarizes the key differences at a glance.
| Feature | facesearching | AWS Rekognition |
|---|---|---|
| Primary purpose | Reverse face search across the public web | Developer API for face detection and recognition |
| Built-in web index | Yes, pre-built index of social media, news, blogs, and video | No, you must supply your own image database |
| Face detection | Yes, automatic | Yes, with detailed attribute analysis |
| Face comparison (1:1) | Yes, as part of search results | Yes, dedicated CompareFaces API |
| Face search (1:N) | Yes, against public web index | Yes, against your own face collections |
| Emotion and age estimation | No | Yes |
| Celebrity recognition | Implicit through web results | Yes, dedicated feature |
| Text in image (OCR) | No | Yes, via Rekognition Text |
| Content moderation | No | Yes, dedicated moderation API |
| No-code web interface | Yes, browser-based | No, requires API integration |
| Photo handling | Deleted immediately after search | Per AWS data handling policy |
| Pricing model | Pay-as-you-go from $2 per search | Per API call, billed to AWS account |
Pricing Comparison
facesearching uses transparent, consumer-friendly pricing: a $2 single search, a $29 Starter plan, and a $79 Pro plan. You pay only for what you use, with no subscription lock-in and no infrastructure to manage. AWS Rekognition is priced per API call against your AWS bill. Face detection starts at a low per-1,000-images rate, and costs scale with usage volume, which is natural for an enterprise cloud service but requires ongoing engineering cost management. For an occasional personal search, facesearching's $2 tier is far simpler and cheaper. For an application processing millions of images per month, Rekognition's per-call pricing may be more economical at scale — but only after you account for the development, infrastructure, and maintenance costs that Rekognition demands and facesearching does not. For another pricing perspective in the cloud API space, see our facesearching vs Google Cloud Vision comparison.
Privacy Practices
Privacy handling differs in structure and intent. facesearching deletes uploaded photos immediately after each search completes and does not retain them — a policy designed for individual users who care about how long their image sits on a server. AWS Rekognition, as an AWS service, operates under Amazon's broader data handling commitments. Customer image data is generally not used to train AWS models, and customers can delete their stored face collections and images at any time. However, because Rekognition is a tool you build on top of, the privacy of any end-to-end experience depends heavily on how the developer configures it. facesearching's deletion policy is explicit and applies out of the box, while Rekognition's privacy posture is largely shaped by the application built around it.
Ease of Use
This is where the two diverge most sharply. facesearching is built for non-technical users. There is nothing to install, no code to write, and no infrastructure to manage; the entire experience happens in a browser. AWS Rekognition is built for developers and engineering teams. Using it requires an AWS account, familiarity with API integration, IAM permissions management, and the ability to build and maintain your own application logic around the API responses. For an individual who wants to verify a dating match or investigate a suspicious photo, facesearching is ready in seconds. For a company building identity verification into its own product, Rekognition provides the building blocks but none of the finished experience.
Accuracy and Search Scope
facesearching comes with a pre-built index of publicly available web content, so a single upload immediately returns matches from across the internet. The value is in the index: you do not need to gather images yourself. AWS Rekognition has no comparable public index. Its face comparison feature tells you whether two images contain the same person, and its face collection feature lets you store and search your own set of faces, but the scope is limited to whatever images you provide. If your goal is to find where a stranger's face appears online, Rekognition alone cannot do that because it has no web index to search. It can power that capability inside an app you build, but only if you supply the source images and build the indexing infrastructure.
facesearching gives you answers from the web in seconds. AWS Rekognition gives you the tools to build a face recognition system. One is a finished product; the other is a construction kit.
Pros and Cons
facesearching strengths and limitations:
- Ready to use instantly with no code or setup
- Pre-built index of social media, news, blogs, and video
- Pay-as-you-go pricing from $2 with no subscription
- Photos deleted immediately after each search
- Not designed for embedding into custom applications
- No emotion, age, or content moderation analysis
AWS Rekognition strengths and limitations:
- Powerful, programmable face detection, comparison, and analysis APIs
- Rich attribute estimation including emotion, age, and gender
- Scales to enterprise volumes within AWS infrastructure
- Lets developers build fully custom recognition experiences
- Requires coding, an AWS account, and your own image database
- No built-in public web index for reverse face search
Which Should You Choose?
The right choice depends entirely on what you are trying to accomplish. The two platforms are not really competitors; they serve different needs. If you are an individual who wants to find where a face appears online, verify a match, or protect yourself from deception, facesearching is the clear fit: it works immediately, needs no technical skill, and returns results from across the public web. If you are a developer or engineering team building facial recognition into your own product, AWS Rekognition provides the APIs you need — provided you supply the image data and build the experience around them. Some organizations even use both: Rekognition to power in-app face matching and facesearching for ad-hoc web investigations that fall outside their own database. For more developer-focused comparisons, read our facesearching vs Clarifai comparison. Ready to try a consumer face search? Start a free search on facesearching now.