Quick facts
- Best for
- Deploy ML Models from Any Python Environment with Modelbit
- Pricing
- Paid
- Editor rating
- 5 / 5
- Community saves
- 6
About Modelbit
Images displaying a cutting-edge model deployment solution called 'Modelbit'. This innovative platform empowers users to train custom machine learning models using on-demand GPUs available instantly. The standout feature is the ability to deploy trained models from any Python environment, ensuring flexibility and convenience. Users can also infer models deployed in various environments such as Snowflake, Redshift, and dbt through REST APIs. Backed by robust version control, CI/CD, and code review mechanisms with git-based tools, Modelbit offers superior control and efficiency. Additionally, the platform boasts comprehensive logging and monitoring facilities, providing real-time insights, alerts, and observability.
Pros
- Deploy from any Python environment
- On-demand GPUs for training
- Infer from Snowflake, Redshift, dbt, REST APIs
- Backed by git repo for version control, CI/CD, code review
- Robust logging and monitoring
- Deploy in your cloud or Modelbit's
- Built-in tools for MLOps
- Support for custom and open-source models
- Automated CI/CD
- Comprehensive observability and alert systems
Cons
Pricing
On-Demand: XGBoost Fraud Detector
$380
- • CPU compute
- • 0.5 compute seconds per inference
- • 10,000 inferences per day
On-Demand: Segment Anything Model
$165
- • T4 GPUs
- • 1.0 GPU compute seconds per inference
- • 500 object detections per day
Private Cloud: Medical Information Extraction Model
$25000
- • Self-hosted deployment for PII privacy
- • Built with PyTorch
Enterprise: Custom TensorFlow Model
$833
- • A10 GPU
- • 25 compute seconds per inference
- • 100 inferences per day
- • Effective GPU rate of $0.32/ GPU Minute