Supervisely vs Power Query
Side-by-side comparison to help you choose.
| Feature | Supervisely | Power Query |
|---|---|---|
| Type | Platform | Product |
| UnfragileRank | 43/100 | 32/100 |
| Adoption | 1 | 0 |
| Quality | 0 | 1 |
| Ecosystem |
| 0 |
| 0 |
| Match Graph | 0 | 0 |
| Pricing | Free | Paid |
| Capabilities | 14 decomposed | 18 decomposed |
| Times Matched | 0 | 0 |
Enables teams to annotate images using multiple geometric primitives (rectangles, polygons, skeletons, 3D lasso) with real-time collaboration, permission-based access control, and integrated AI models (SAM2, ClickSEG) that auto-generate annotations which annotators refine. The platform manages annotation state across concurrent users, tracks changes via audit logs, and enforces quality gates through review workflows before data enters training pipelines.
Unique: Integrates SAM2 and ClickSEG foundation models directly into the annotation UI for one-click mask generation, eliminating separate labeling tool + model inference pipeline; combines this with nested ontologies and key-value tagging for complex hierarchical classification schemes that most annotation tools handle as flat structures
vs alternatives: Faster annotation velocity than Labelbox or Scale AI because AI suggestions are generated in-browser without round-trip API calls, and supports more geometric primitives (3D lasso, skeletons) than CVAT for pose estimation and 3D tasks
Provides frame-by-frame and track-based annotation for video sequences with automatic object tracking across frames, off-screen detection marking, and multi-view synchronization for multi-camera footage. The system maintains temporal consistency by propagating annotations forward/backward and detecting tracking breaks, allowing annotators to correct trajectories in bulk rather than per-frame. Supports pre-recorded video with on-the-fly transcoding (requires Video Max add-on) and CDN acceleration for large files.
Unique: Implements track propagation with temporal consistency checking — annotations are not isolated per-frame but treated as continuous trajectories with automatic forward/backward propagation and break-detection, reducing manual frame-by-frame work by ~70% vs frame-independent annotation tools
vs alternatives: More efficient than CVAT for video annotation because track propagation is bidirectional and includes off-screen detection logic; cheaper than Scale AI's video labeling because pricing is subscription-based rather than per-video-hour
Generates synthetic training data by applying transformations (rotation, scaling, color jittering, blur) to existing annotations, or by rendering 3D models in simulated environments. Supports both image-level augmentation (modify existing images) and scene-level synthesis (render new scenes from 3D assets). Generated data is versioned and tracked separately from human-annotated data. Integration with model training allows teams to augment datasets on-the-fly during training.
Unique: Integrates synthetic data generation directly into the annotation platform with versioning and tracking, allowing teams to augment datasets without external tools — most teams use separate libraries (Albumentations, imgaug) or custom scripts, creating a disconnect between annotation and augmentation workflows
vs alternatives: More integrated than using Albumentations or imgaug separately because augmentation is tracked and versioned; more flexible than fixed augmentation pipelines because it supports both image-level and scene-level synthesis
Provides a training orchestration layer that manages model training runs, hyperparameter tuning, and result tracking. Supports integration with popular frameworks (PyTorch, TensorFlow — unclear if both are supported) and custom training scripts. Training runs are logged with dataset version, hyperparameters, metrics, and model weights. Results are compared across runs to identify best-performing models. Hardware specifications for training (GPU type, memory, timeout) are unknown.
Unique: Integrates model training orchestration directly into the annotation platform with automatic dataset version tracking and experiment comparison, eliminating the need for separate training infrastructure or experiment tracking tools — most teams use MLflow, Weights & Biases, or custom scripts
vs alternatives: More integrated than MLflow because training is tied to dataset versions and annotation workflows; simpler than Kubeflow because it abstracts away infrastructure management
Provides search capabilities across images, annotations, and metadata using both keyword search (filename, class name) and semantic search (find similar images based on visual content). Supports filtering by annotation properties (class, confidence, annotator, date), metadata tags, and custom attributes. Search results can be exported as new datasets or used to create subsets for targeted annotation or analysis. Semantic search uses embeddings (model unknown) to find visually similar images.
Unique: Combines keyword, metadata, and semantic search in a single interface with the ability to export results as new datasets, enabling data exploration and quality analysis without leaving the platform — most annotation tools have basic filtering but lack semantic search or export capabilities
vs alternatives: More powerful than CVAT's filtering because it includes semantic search; more integrated than using Elasticsearch separately because search results can be directly exported as datasets
Enables multiple annotators to work on the same image simultaneously with real-time synchronization of changes. Detects conflicts when two annotators modify the same annotation and flags them for resolution. Supports undo/redo with conflict awareness (undo by one user doesn't affect another user's changes). Annotation state is persisted to the server after each change, ensuring no data loss. Latency and conflict resolution strategy are unknown.
Unique: Implements real-time collaborative annotation with automatic conflict detection and per-user undo/redo, allowing multiple annotators to work on the same image without stepping on each other's changes — most annotation tools are single-user or require manual conflict resolution
vs alternatives: More collaborative than CVAT because it supports simultaneous editing with conflict detection; more user-friendly than Google Docs-style conflict resolution because it's domain-specific to annotation conflicts
Enables annotation of 3D point clouds (LiDAR, RADAR, depth sensors) with cuboid, cylinder, and segmentation primitives, with synchronized 2D image context from camera feeds to resolve ambiguities. The platform fuses multi-sensor data (e.g., LiDAR + camera + radar) into a unified 3D scene, allowing annotators to label objects in 3D space while referencing 2D projections. Includes automatic ground segmentation and AI-assisted cuboid generation (requires Cloud Points Max add-on at €399/month).
Unique: Fuses LiDAR, camera, and RADAR data into a unified 3D annotation canvas with synchronized 2D projections, allowing annotators to resolve 3D ambiguities using 2D context — most competitors require separate 2D and 3D annotation passes or lack RADAR integration
vs alternatives: More cost-effective than Waymo's internal annotation infrastructure because it's cloud-based and subscription-priced; supports more sensor modalities (RADAR + LiDAR + camera) than Scalabel or Kitti-based tools which focus on LiDAR-only or camera-only workflows
Provides specialized annotation tools for DICOM medical imagery including multi-planar reconstruction (MPR), 3D perspective views, and slice-by-slice segmentation with automatic 3D tracking across slices. Includes anonymization tools to strip PHI (patient identifiers, dates) and enforce HIPAA compliance. Medical Max add-on (€149/month) unlocks 50,000+ file limit, 3D tracking, and anonymization features. Supports CT, MRI, X-ray, and ultrasound modalities.
Unique: Combines DICOM-native annotation (multi-planar reconstruction, Hounsfield unit windowing) with automatic 3D tracking across slices and built-in anonymization, eliminating the need for separate DICOM viewers, segmentation tools, and de-identification pipelines that most medical AI teams cobble together
vs alternatives: More specialized than general-purpose annotation tools (Labelbox, Scale) because it understands DICOM metadata, Hounsfield units, and multi-planar reconstruction; cheaper than dedicated medical annotation platforms (Nuance, Agfa) because it's cloud-based and modular
+6 more capabilities
Construct data transformations through a visual, step-by-step interface without writing code. Users click through operations like filtering, sorting, and reshaping data, with each step automatically generating M language code in the background.
Automatically detect and assign appropriate data types (text, number, date, boolean) to columns based on content analysis. Reduces manual type-setting and catches data quality issues early.
Stack multiple datasets vertically to combine rows from different sources. Automatically aligns columns by name and handles mismatched schemas.
Split a single column into multiple columns based on delimiters, fixed widths, or patterns. Extracts structured data from unstructured text fields.
Convert data between wide and long formats. Pivot transforms rows into columns (aggregating values), while unpivot transforms columns into rows.
Identify and remove duplicate rows based on all columns or specific key columns. Keeps first or last occurrence based on user preference.
Detect, replace, and manage null or missing values in datasets. Options include removing rows, filling with defaults, or using formulas to impute values.
Supervisely scores higher at 43/100 vs Power Query at 32/100. Supervisely leads on adoption, while Power Query is stronger on quality and ecosystem. Supervisely also has a free tier, making it more accessible.
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Apply text operations like case conversion (upper, lower, proper), trimming whitespace, and text replacement. Standardizes text data for consistent analysis.
+10 more capabilities