--- title: "QuickPose Alternatives, Routed by Why You Are Leaving" canonical: "https://aifitnessapi.com/alternatives/quickpose-alternatives" cluster: "Alternatives" primary_query: "quickpose alternatives" last_reviewed: "2026-10-18" description: "Alternatives to QuickPose by reason for leaving: MediaPipe directly, Apple Vision, ML Kit, Sency, Kemtai and more, checked against their own docs." publisher: "AIFitnessAPI — funded by KinesteX, the subject of this page; disclosure in body" cite_as: "\"QuickPose Alternatives, Routed by Why You Are Leaving\", AIFitnessAPI, https://aifitnessapi.com/alternatives/quickpose-alternatives" --- # QuickPose Alternatives, Routed by Why You Are Leaving > The closest alternative to QuickPose is the model it wraps: QuickPose describes itself as packaging MediaPipe and BlazePose, so a team willing to write its own counting and range-of-motion logic can call MediaPipe Pose Landmarker directly, under Apache-2.0, with no key or fee. For an iOS-only app Apple's Vision body pose needs no SDK at all, and an Android-only app can use Google's ML Kit Pose Detection. Teams that want more packaged than QuickPose rather than less look at Sency's assessment and workout kits, Kemtai for regulated rehab, or KinesteX, which funds this site. - Canonical: https://aifitnessapi.com/alternatives/quickpose-alternatives - Last reviewed: 2026-10-18 - Publisher: AIFitnessAPI (https://aifitnessapi.com) — funded by KinesteX, the subject of this page; disclosure in body - Cite as: "QuickPose Alternatives, Routed by Why You Are Leaving", AIFitnessAPI, https://aifitnessapi.com/alternatives/quickpose-alternatives --- **Disclosure: this site is funded by KinesteX, which sells a motion-tracking SDK that competes with QuickPose.** KinesteX appears below once, in the group where it genuinely fits, after the third-party options and labelled as ours. Every fact about every vendor, KinesteX included, comes from that vendor's own repositories, package registries or published pages as fetched on October 17, 2026; where we could not verify something, the page says so. ## What you would be replacing QuickPose's iOS SDK README describes it as providing "developer-oriented cutting edge ML features of MediaPipe and BlazePose, with easy integration and production ready code". That is an accurate summary of the shape: the SDK modules bundle MediaPipe libraries (the README lists `QuickPoseMP-lite`, `QuickPoseMP-full` and `QuickPoseMP-heavy`), and QuickPose adds the fitness layer on top. Verified from its repositories and pages: - **Platforms.** iOS 14.0 or later; Android, where its docs give a minimum of API 26 and note that the sample code needs API 28 because of CameraX; and a React Native plugin, `@quickpose/react-native`, for iOS and Android. - **Features.** Overlays, range-of-motion measurement for shoulders, hips, knees, back and neck, detection and counting for 20 named exercises (22 entries in the README's table, two of them repeated), body-position and limb-not-visible feedback, and raised-finger and thumbs-up inputs. - **Runtime.** On-device. Its iOS and Android product pages describe a 33-point skeleton and on-device inference, with no video frames sent to a server. - **Price.** A free key, then plans by monthly active device: free for 10 devices, then $25 to $1,000 a month up to 100,000 devices. The full table is in [QuickPose pricing](/pricing/quickpose-pricing). ## Why teams look elsewhere These are the reasons the sources support, not guesses about QuickPose's customers: - **The price unit.** QuickPose bills per monthly active device. A free tier of 10 devices covers development and a pilot, not a launch. If your app has many occasional users, a per-device bill grows with reach rather than with engagement. - **No public web package.** QuickPose's docs home mentions Wasm SDKs, but the only QuickPose package we found on npm is the React Native plugin, and there is no public web repository. A product with a browser surface needs a plan for it. - **Building blocks, not content.** The repositories ship counters, measurements and overlays. They contain no exercise library, workout plans or coaching UI, so a team that wanted a finished experience still has to build one. - **Owning the model layer.** QuickPose sits between you and MediaPipe. A team that wants to pin a model version, swap variants on its own schedule or read every line of the pipeline may prefer to call MediaPipe itself. - **Mixed licence signals.** The iOS repository is Apache-2.0 and QuickPose's terms license the SDK under Apache 2.0, yet the same terms say use "may be subject to fees"; the Android repository has no LICENSE file and the npm plugin is MIT. In our reading this is permissive code in front of a keyed, metered service, which is fine, but procurement teams ask about it. ## The alternatives, by reason for leaving ### You want to drop the wrapper: MediaPipe Pose Landmarker The most direct alternative is the model QuickPose wraps. MediaPipe Pose Landmarker returns 33 landmarks per person with world coordinates in meters, ships as lite, full and heavy `.task` files, runs on Android, iOS, web and Python through the MediaPipe Tasks API, and is Apache-2.0 for both the framework and, per Google's model card, the models. There is no key and no fee. What you give up is exactly what QuickPose adds: exercise counters, range-of-motion measurement, feedback events and camera plumbing become your code. [MediaPipe Pose Landmarker models](/motion/mediapipe-pose-landmarker-models) covers the variants, and [MediaPipe alternatives](/alternatives/mediapipe-alternatives) maps the field from the other side. ### You ship on iOS only: Apple Vision body pose Apple's Vision framework has two body pose requests with no model file to ship: `VNDetectHumanBodyPoseRequest` for 2D, available from iOS 14, and `VNDetectHumanBodyPose3DRequest` for 3D, from iOS 17. No SDK key, no third party, no bill; the trade is that Apple publishes no model card or accuracy figures. Details in [Apple Vision body pose](/motion/apple-vision-body-pose). ### You ship on Android only: ML Kit Pose Detection Google's ML Kit Pose Detection returns 33 landmarks for one person on device, in a base and an accurate SDK, under Google's service terms rather than an open-source licence; Google labels the API beta. See [ML Kit pose detection](/motion/ml-kit-pose-detection). ### You need a browser: MediaPipe Tasks for web or MoveNet Both run client-side in the browser. MediaPipe's Pose Landmarker has a web Tasks API, and MoveNet runs through TensorFlow.js with SinglePose Lightning, Thunder and a MultiPose model for up to 6 people. [AI workout tracking on the web](/guides/ai-workout-tracking-web) walks through the browser path. ### You want more packaged than QuickPose, not less - **Sency** ships two kits on iOS, Android, React Native and Flutter: a branded kit whose "entire user interface and experience are ready to use", and a custom kit with no predefined UI. Its README leads with movement and fitness assessments, monthly progress evaluations and personalized workout plans. Sency's iOS kits download their models from Sency's server at configuration, so the first run needs a connection. It bills per monthly active user: its packages page lists a free Starter plan for up to 100 and a Growth plan from $499 a month, though its main pricing page describes the first 100 users as a paid tier after a trial; see [Sency pricing](/pricing/sency-pricing). The head-to-head is [QuickPose vs Sency](/compare/quickpose-vs-sency). - **Kemtai** is the option for regulated rehab work. Its site describes embeddable exercise and patient-management components plus data APIs, a library of more than 2,000 exercises, processing on the device with no video recorded, and an FDA listing and CE marking as a medical device. Its API docs are behind an email request, and we found no public SDK repository. - **KinesteX**, which funds this site, embeds a hosted camera workout experience through a WebView or iframe on iOS, Android, React Native, Flutter and the web, with workout, plan, challenge and assessment views and a content API. It publishes no prices; access starts with a contact form. The disclosed comparison is [KinesteX vs QuickPose](/compare/kinestex-vs-quickpose). ### Several people in one frame QuickPose's repositories describe single-subject features. For group scenes, [multi-person pose tracking](/motion/multi-person-pose-tracking) compares the approaches, and the model pages cover [MoveNet MultiPose](/motion/movenet-models), [Ultralytics YOLO pose](/motion/yolo-pose-models), which is AGPL-3.0 unless you buy Ultralytics' Enterprise License, and [RTMPose](/motion/rtmpose-models). ## What we verified, and where | Option | Verified | From | Not verified | | --- | --- | --- | --- | | QuickPose | MediaPipe-based modules, platforms, features, on-device claim, device-based plans | Its GitHub repos, npm, docs, pricing and product pages | Accuracy; the Wasm SDK mentioned in its docs | | MediaPipe | 33 landmarks, variants, Tasks platforms, Apache-2.0 | google-ai-edge/mediapipe and Google's model card | Behaviour on your exercises | | Apple Vision | 2D from iOS 14, 3D from iOS 17 | Apple's documentation | Accuracy, which Apple does not publish | | ML Kit | 33 landmarks, one person, beta, base and accurate SDKs | Google's ML Kit guides | Google's future plans for the beta | | Sency | Kits, platforms, server-downloaded models, published plans | Its GitHub repos and pricing pages | How its Growth rates combine; accuracy | | Kemtai | Embeddables, data APIs, 2,000+ exercises, regulatory claims | Its website | API docs (email-gated); pricing | | KinesteX | WebView and iframe architecture, platforms, contact-form access | Its GitHub repos and website | Pricing (not published); accuracy | ## Is switching easy? From QuickPose to MediaPipe directly, the model is the same family, so the landmark set carries over; the work is rebuilding the counters, measurements and feedback QuickPose gave you. From QuickPose to Sency or KinesteX, the work runs the other way: you adopt their session model and, with KinesteX, a hosted web experience, and you drop code you wrote around QuickPose's building blocks. Either way, run the candidate on your own recorded exercises before you commit; [how to evaluate motion SDKs](/guides/evaluate-motion-sdks) is a protocol for doing that fairly. ## Sources - [github.com/quickpose/quickpose-ios-sdk](https://github.com/quickpose/quickpose-ios-sdk) — README description, MediaPipe modules, features, iOS 14.0+, SDK key, Apache-2.0 LICENSE (checked 2026-10-17) - [github.com/quickpose/quickpose-android-sdk](https://github.com/quickpose/quickpose-android-sdk) — feature table, Android SDK 26, no LICENSE file (checked 2026-10-17) - [github.com/quickpose/quickpose-react-native-pose-estimation](https://github.com/quickpose/quickpose-react-native-pose-estimation) — React Native plugin for iOS and Android; MIT on npm (checked 2026-10-17) - [docs.quickpose.ai/docs/MobileSDK](https://docs.quickpose.ai/docs/MobileSDK) — min API 26, samples API 28; Wasm SDKs on the docs home (snapshot) (checked 2026-10-17) - [quickpose.ai/products/android-sdk/](https://quickpose.ai/products/android-sdk/) — on-device inference, 33-point skeleton, built on MediaPipe (snapshot) (checked 2026-10-17) - [quickpose.ai/terms-and-conditions/](https://quickpose.ai/terms-and-conditions/) — Apache 2.0 licence clause and fees (snapshot) (checked 2026-10-17) - [github.com/google-ai-edge/mediapipe](https://github.com/google-ai-edge/mediapipe) — Pose Landmarker Tasks API and Apache-2.0 (checked 2026-10-17) - [storage.googleapis.com/mediapipe-assets/Model%20Card%20BlazePose%20GHUM%203D.pdf](https://storage.googleapis.com/mediapipe-assets/Model%20Card%20BlazePose%20GHUM%203D.pdf) — model licence and single-person scope (checked 2026-10-17) - [developer.apple.com/documentation/vision/vndetecthumanbodyposerequest](https://developer.apple.com/documentation/vision/vndetecthumanbodyposerequest) — iOS 14.0 availability; the 3D request from iOS 17.0 (checked 2026-10-17) - [github.com/sency-ai/smkit-sdk](https://github.com/sency-ai/smkit-sdk) — kits, platforms, features (checked 2026-10-17) - [github.com/sency-ai/smkit-ios-demo](https://github.com/sency-ai/smkit-ios-demo) — server-downloaded models (checked 2026-10-17) - [www.sency.ai/pricing-packages](https://www.sency.ai/pricing-packages) — Starter and Growth plans (read through a text reader; quotes re-checked on the page by CI) (checked 2026-10-18) - [www.sency.ai/pricing](https://www.sency.ai/pricing) — trial and paid monthly fee for the first 100 active users (read through a text reader; quotes re-checked on the page by CI) (checked 2026-10-17) - [kemtai.com/](https://kemtai.com/) — embeddables, exercises, edge processing, regulatory claims (snapshot) (checked 2026-10-17) - [www.kinestex.com/docs](https://www.kinestex.com/docs) — platforms and contact-form access (snapshot) (checked 2026-10-17) ## FAQ ### Is QuickPose built on MediaPipe? Yes, by its own description. QuickPose's iOS SDK README says it provides features of MediaPipe and BlazePose with easy integration, its Swift package lists MediaPipe lite, full and heavy library modules, and its product pages say it is built on MediaPipe. What QuickPose adds is the layer above the landmarks: exercise counters, range-of-motion measurement, feedback events, overlays and camera utilities. [Permalink](https://aifitnessapi.com/alternatives/quickpose-alternatives#faq-1) ### What is a free alternative to the QuickPose SDK? MediaPipe Pose Landmarker, the model family QuickPose wraps, is Apache-2.0 with no key or fee and runs on Android, iOS, web and Python. Apple's Vision body pose is free on iOS 14 and later, Google's ML Kit Pose Detection is free on Android and iOS under Google's terms, and MoveNet's single-person models are Apache-2.0. All of them stop at keypoints, so the counting and feedback logic becomes your work. [Permalink](https://aifitnessapi.com/alternatives/quickpose-alternatives#faq-2) ### Which QuickPose alternative runs in a web browser? MediaPipe Pose Landmarker through its web Tasks API, or MoveNet through TensorFlow.js, both client-side. QuickPose's docs home mentions Wasm SDKs, but the only QuickPose package we found on npm is its React Native plugin and there is no public web repository. KinesteX, which funds this site, embeds its experience in a web page through an iframe. [Permalink](https://aifitnessapi.com/alternatives/quickpose-alternatives#faq-3) ### Which QuickPose alternatives include workouts or assessments, not just pose tracking? Sency's README leads with movement and fitness assessments, progress evaluations and personalized workout plans, in a branded kit or a no-UI custom kit. Kemtai's site describes an embeddable exercise player and a library of more than 2,000 exercises for rehab. KinesteX, which funds this site, ships workout, plan, challenge and assessment views. QuickPose's own repositories contain counters and measurements but no exercise library. [Permalink](https://aifitnessapi.com/alternatives/quickpose-alternatives#faq-4)