Picsellia
A computer vision MLOps workspace for visual data, annotation, model training, deployment, and production monitoring.
About Picsellia
Picsellia is an MLOps platform designed for computer vision teams. It brings visual data storage, dataset versioning, annotation, training, deployment, and production monitoring into one workspace, giving data scientists, ML engineers, and operations teams a shared system. Data Engine is the required base module. VisionAI Factory adds experiment tracking, training pipelines, GPU allocation, a model registry, and deployment. Reliability Engine adds real-time monitoring, anomaly and drift detection, feedback loops, continuous training, and shadow deployments. Pricing combines module subscriptions with usage charges. Each core module includes three user seats; extra users, labelers, GPU use, and data processing increase the total. The 14-day trial includes full platform access, 10,000 image uploads, and 100 GPU training hours. The platform fits teams that want computer-vision-specific workflows across development and production. For a small pilot, the €1,000 monthly Data Engine starting price may be hard to justify once optional modules and usage costs are added.
Best for
Best use cases
Manage and curate large image or video datasets before model training
Train, register, deploy, and iterate on computer vision models
Monitor production models for anomalies and drift, then feed results into retraining
Compare before choosing
Better aligned with visual data than a generic MLOps stack, but focused on computer vision rather than broader ML workloads
A unified workflow can replace several disconnected tools, but Data Engine creates a €1,000/month entry cost
Modular add-ons let teams expand into deployment and monitoring, while usage and extra-seat charges make total cost variable
Features
Visual data management
Store large image and video datasets in one workspace, create traceable dataset versions, curate training samples, search by visual similarity with CLIP embeddings, and prepare several media formats for downstream model work.
Model development and deployment
Track experiments and model versions, use pre-built or custom training pipelines, allocate GPU capacity when needed, register successful models, and move computer vision models into deployment from the same platform.
Production reliability
Watch deployed models in real time, detect anomalies and performance drift, collect production feedback, feed useful examples into continuous training, and evaluate new versions through shadow deployments.
Pros
- A single computer-vision workspace covers data, annotation, training, deployment, and monitoring
- Visual data features include dataset versions, similarity search, CLIP embeddings, and multi-format support
- Modules can be added as a team moves from data preparation into deployment and production monitoring
Cons
- Data Engine starts at €1,000 per month before optional modules or usage charges
- VisionAI Factory and Reliability Engine both require Data Engine, which limits low-cost standalone adoption
- Extra users, labelers, GPU allocation, and processing can make the final bill higher and less predictable
Pricing Plans
Data Engine
- Unlimited data storage
- Dataset versioning
- Smart data curation
- Visual similarity search
- CLIP embeddings
- Multi-format support
- 3 user seats included
- Extra users cost €500/month each
VisionAI Factory
- Requires Data Engine
- Experiment tracking
- Model registry
- Pre-built pipelines
- Custom training pipelines
- One-click GPU allocation
- Model deployment
- 3 user seats included
- Extra users cost €75/month each
Reliability Engine
- Requires Data Engine
- Real-time monitoring
- Anomaly detection
- Drift detection
- Feedback loops
- Continuous training
- Shadow deployments
- 3 user seats included
- Extra users cost €150/month each
Labeler Seats
- Annotation workforce access
- Unlimited seats available
- Annual price is €120 per labeler
* Pricing information is for reference only. Please refer to the latest announcement on Picsellia's official website.