The Best AI Workout & Motion-Tracking APIs (2026)

Disclosure: KinesteX, one of the four SDKs below, is this site's own product, and KinesteX funds this site. It is listed last, described to the same factual bar as the others, and nothing here ranks it first. Every vendor fact on this page comes from the vendor's own repositories, documentation or published pages as read on October 17 and 18, 2026.

The two layers: pose primitives vs coaching SDKs#

There are two very different things people mean by an "AI workout tracking API," and confusing them is the most common mistake.

The bottom layer is pose-estimation primitives: MediaPipe/BlazePose, TensorFlow MoveNet, Apple Vision and Google's ML Kit. These give you a skeleton of joint keypoints per video frame (17 to 33 landmarks in 2D and, in some cases, 3D) and nothing more. They are free, run on-device, and are permissively licensed or bundled with the platform.

The top layer is coaching SDKs: QuickPose, Sency, Kemtai and KinesteX. These sit on the keypoints and add the application logic that turns a stream of landmarks into a product: rep counting, per-exercise form rules, exercise libraries, assessments, and feedback UI.

Here is the key thing to internalize before you compare vendors: the keypoints are the easy, commoditized part. The recurring cost is everything after them. Getting 33 landmarks per frame is a solved, free problem. Turning noisy landmarks into reliable rep counts and per-exercise form feedback across body types, camera angles, lighting, and low-end devices is the actual engineering cost, and it recurs as maintenance rather than being a one-time build. Every new exercise needs its own state logic and joint-angle thresholds; accuracy across real-world conditions needs continuous tuning, test data, and QA. That second column of work is exactly what the coaching SDKs sell.

How to compare AI workout-tracking SDKs#

Four criteria decide this category for most builders:

• Scope fit. Are you building consumer fitness, or clinical/rehab? The vendors specialize differently, and paying for rehab-grade scope you don't need (or missing it when you do) is a common mismatch.
• Platform coverage. iOS-only, or do you need Android, web, Flutter, and React Native at parity? This alone rules some options in or out.
• Access and pricing model. Published plans with a free tier, or a demo request and a quote? This affects how fast you can start and how you budget.
• How much you still build. A pose-and-logic toolkit leaves the exercise programming and UI to you; a white-label platform ships more of the product but gives you less control over the exact form logic.

One accuracy note that applies to every product below: none of these vendors publishes an independent benchmark, and we have not measured any of them. Treat every accuracy or speed figure as a marketing claim to confirm in a trial; how to evaluate motion SDKs sets out a protocol.

Comparison table#

Prices, tiers and platform details are volatile: everything above is as of October 2026, so verify in the vendor's own pages.

The options in detail#

QuickPose#

QuickPose is a pose-estimation toolkit built on Google's MediaPipe: its iOS README describes "developer-oriented cutting edge ML features of MediaPipe and BlazePose", and its Swift package ships MediaPipe lite, full and heavy libraries as modules. It runs on iOS 14 and later (its product page says all iPhones from iPhone 8 onwards), on Android from API 26, and in React Native through a plugin, and its product pages say all inference runs on the device with no video frames sent to a server. It provides joint overlays, range-of-motion measurement for shoulders, hips, knees, back and neck, counters for 20 named exercises, body-position feedback, and yoga pose detection; you can use its overlays or consume raw landmarks and build your own. Its distinguishing trait is access: a free SDK key, and a published price list by monthly active device, free for up to 10 devices and then from $25 a month for 100 devices to $1,000 a month for 100,000, with every SDK feature on every plan (QuickPose pricing). It is best for teams that want MediaPipe pose plus ready-made rep and range-of-motion logic without a sales call to start. Watch out: it is a pose-and-logic SDK rather than a workout platform, so exercise programming and UI are on you, and the Android README's feature table is shorter than the iOS one.

Sency#

Sency is a motion SDK for fitness and health apps that uses only a phone camera, in two kits: SMKit, which its version table calls a "Native no-UI SDK" where you own the camera preview and UI, and SMKitUI, a white-label Branded Kit. It ships for iOS, Android, React Native and Flutter, with JavaScript and Unity marked "NA". Its README leads with movement and fitness assessments, monthly progress evaluations and personalized workout plans, and its pricing page describes a four-step assessment that takes less than 4 minutes and covers functional movement, core strength, mobility and endurance. Its FAQ says data is analyzed on the user's device and never sent to the cloud, but its iOS and Android SDKs download their models from Sency's server during configuration, with no bundled fallback. Sency prices by monthly active user on two pages that disagree about the start: its packages page lists a free Starter plan for up to 100 users and a Growth plan from $499 a month for up to 3,800, while its main pricing page describes a free trial and then a minimal monthly fee covering up to 100 active users, with pay-as-you-go above that (Sency pricing). Its main pricing page invites you to start with a free trial, "no commitment required". Sign-up runs through a contact form, and commercial use falls under a separate paid agreement in its Terms of Use. It is best for consumer fitness and health apps that want assessments and plans in a native kit. Watch out: the first configuration needs a connection, and the two pricing pages need reconciling in writing.

Kemtai#

Kemtai is a computer-vision movement-analysis platform oriented toward physiotherapy, rehab and medical fitness. Its site says it works on any device with a camera, with no sensors or wearables, and that it ships as embeddable components plus data APIs that return exercise scores, range-of-motion changes and adherence data to your system. It cites a library of more than 2,000 exercises and movement variations, processing on the device with no video recorded, and an FDA listing and CE marking as a medical device. It says its accuracy was validated in a study against a gold-standard motion lab that used 10 3D cameras and body sensors, a vendor claim about a study worth asking to see, and two of its own pages give different body-point counts, 111 and 113. Its API documentation is behind an email request and it publishes no prices. It is best for clinical, PT and MSK products that need a large exercise library plus assessment and adherence data. Watch out: the positioning is clinical-first, so a pure consumer-fitness team may pay for scope it doesn't need.

KinesteX#

KinesteX (this site's own product, described here to the same factual bar as the others) is a white-label AI fitness coaching SDK whose iOS, Android, Flutter, React Native and web SDKs embed a hosted camera workout experience through a WebView or iframe, plus a content API. It provides ready-made workouts and plans, challenges and leaderboards, AI movement assessments with risk-level outputs, rep counting, mistake detection, posture tracking and calorie estimates, and it sends rep, mistake, exercise and workout events back to your app. Its SDK READMEs describe "optional edge processing (no cloud transmission)", while its security page says all camera and vision processing stays on-device and its HIPAA page says KinesteX is HIPAA compliant; confirm what your configuration does. It is best for teams that want a brandable, multi-platform coaching layer, including the web, without building pose logic in-house. Watch out: it publishes no prices (its FAQ describes usage-based plans priced per active user per month; access starts with a contact form), the experience is the vendor's hosted web app rather than native rendering you control, and its accuracy figures (above 90 percent in a README, 97.2 percent in its FAQ) are its own claims.

The build-your-own alternative: pose primitives#

If motion analysis is your core product, you can skip the coaching SDKs and build on free primitives. All run on-device and are commercially usable:

• MediaPipe / BlazePose (Google AI Edge) detects 33 body landmarks with 3D world landmarks, is positioned by its model card for on-device real-time fitness, and runs on Android, iOS, Python, and web under an Apache-2.0 license. See MediaPipe Pose Landmarker models.
• TensorFlow MoveNet detects 17 keypoints in a latency-focused "Lightning" and a higher-accuracy "Thunder" variant, plus a MultiPose model for up to 6 people, and runs in TensorFlow Lite and TensorFlow.js. The older standalone PoseNet package is deprecated in its favour. See MoveNet models.
• Apple Vision provides on-device body-pose detection inside the Apple SDK: 19 2D joints from iOS 14, plus a 3D request from iOS 17, free within the Apple toolchain, on Apple platforms only. See Apple Vision body pose.
• ML Kit Pose Detection returns 33 landmarks for one person on Android and iOS, offered in beta. See ML Kit pose detection.

The catch is the one from the top of this page: these give you keypoints and stop there. Rep logic, form rules, exercise coverage, calibration, and edge-case handling are all on you, and that work recurs. Whether owning it is worth it is a build-vs-buy decision, not a feature checklist. How to build an AI fitness app maps this layer and the five others, with a build-versus-buy position for each, and pose estimation models compared covers the model choice.

Which should you pick?#

Match the tool to the job rather than hunting for a single winner:

• If you want building blocks inside your own UI and a price you can read today → QuickPose, for its free key, published per-device plans and MediaPipe foundation.
• If you want assessments and workout plans in a native kit, or Flutter → Sency, with its two pricing pages reconciled in writing.
• If you're in physiotherapy, rehab, or medical fitness → Kemtai, for its exercise library, embeddables and clinical positioning, with its regulatory and validation claims confirmed first-hand.
• If you want a white-label coaching experience with content across mobile and the web → KinesteX, which funds this site; KinesteX alternatives lists the reasons you might not.
• If motion analysis is your core IP, you have CV/ML talent, or you need exercises the vendors don't cover → build on MediaPipe, MoveNet, Apple Vision or ML Kit, and accept that the rep, form and library logic is yours to build and maintain.

Head-to-heads with the same sourcing: QuickPose vs Sency, KinesteX vs Sency and KinesteX vs QuickPose.

Watch the fine print#

Pricing tiers, device and user thresholds, exercise counts and platform parity all change, and two of these four vendors publish no prices at all. Confirm every volatile number against the vendor's own pages before you commit, and use trials and free tiers to verify accuracy and platform parity on your actual exercises and target devices.