💡 Pick it for a fuller dataset-to-deployment workflow and stronger team collaboration.
Roboflow is a platform that simplifies the process of managing and deploying computer vision models.
Starting at $19/month
Best MakeML Alternatives ranked by AI · updated Aug 2026
MakeML is a no-code computer vision tool for creating custom image classification and object detection models. It is aimed at developers, educators, and product teams who want to train models and export them for mobile or edge applications.
Top 6 MakeML alternatives
2
Edge Impulse
Edge Impulse
💡 Choose it when models must run on microcontrollers, sensors, or other edge hardware.
Edge Impulse is a development platform for building and deploying machine-learning models on edge devices, including models that process images, audio, and...
Pros
- Stronger than Perceptura for embedded and on-device inference
- Includes tools for data collection, optimization, and hardware deployment
- Supports multimodal edge-AI projects beyond computer vision
Cons
- Less suitable for centrally managed video operations and dashboards
- Requires more embedded and hardware expertise than Perceptura
- Enterprise features and support require a custom plan
Free for development, enterprise pricing custom
3
Create ML
Apple
💡 Pick it for free, native Core ML training inside an Apple-focused development workflow.
Create ML is Apple's tool for training machine learning models for apps running on Apple platforms. It provides macOS interfaces and APIs...
Pros
- Free and tightly integrated with Apple development tools
- Produces Core ML models suited to iPhone, iPad, Mac, and Apple Watch apps
- Offers a simpler workflow than MakeML for Apple-only projects
Cons
- Requires macOS and is much less cross-platform than MakeML
- Less flexible for non-Apple deployment targets
- Training and dataset workflows are less platform-neutral than Roboflow's
Free with Xcode
4
Google
Teachable Machine
💡 Choose it for the quickest free way to teach or prototype simple classification models.
Teachable Machine is a browser-based tool for training simple image, sound, and pose classification models without coding. It is intended primarily for...
Pros
- Much faster to start with than MakeML for simple classification projects
- Free and entirely browser-based
- Supports image, audio, and pose examples in one interface
Cons
- Does not provide MakeML-level object detection workflows
- Offers limited dataset management and model evaluation controls
- Less appropriate for production-scale training or deployment
5
Ultralytics HUB
Ultralytics
💡 Pick it for higher-performance YOLO detection, segmentation, and pose models.
Ultralytics HUB is a cloud platform for managing datasets, training computer vision models, and deploying applications built around Ultralytics models. It is...
Pros
- Particularly streamlined for YOLO-based computer vision projects
- Provides hosted training, dataset management, and model tracking
- Supports deployment across cloud, edge, and application environments
Cons
- Less model-framework-neutral than Roboflow
- Best features are concentrated around Ultralytics models
- Annotation and dataset tooling is narrower than dedicated labeling platforms
Freemium, paid plans from N/A
💡 Choose it when advanced annotation and complex professional vision datasets matter most.
Supervisely is a computer vision platform combining data annotation, dataset management, model training, and deployment tools. It targets research groups and ML...
Pros
- More complete computer vision workflow than Heartex alone
- Includes dataset management, training, and model deployment capabilities
- Rich support for segmentation, detection, and video annotation
Cons
- More focused on computer vision than Heartex's broader modality support
- Full enterprise functionality requires a paid plan
- Larger feature set can feel heavier for simple labeling tasks
Free community plan; paid plans custom
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