Inference Platform built for speed and control. Deploy any model anywhere, with tailored inference optimization, efficient scaling, and streamlined op
BentoML is recognized for its strong capabilities in facilitating AI model deployment with user-friendly features that streamline the process. Users appreciate its flexibility and integration options which are seen as beneficial for various machine learning workflows. However, there is limited feedback on pricing, making it difficult to gauge user sentiment in this area. Overall, BentoML maintains a positive reputation in the developer community, particularly for those focused on deploying machine learning models efficiently.
Mentions (30d)
0
Reviews
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Platforms
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GitHub Stars
8,550
943 forks
BentoML is recognized for its strong capabilities in facilitating AI model deployment with user-friendly features that streamline the process. Users appreciate its flexibility and integration options which are seen as beneficial for various machine learning workflows. However, there is limited feedback on pricing, making it difficult to gauge user sentiment in this area. Overall, BentoML maintains a positive reputation in the developer community, particularly for those focused on deploying machine learning models efficiently.
Features
Use Cases
Industry
information technology & services
Employees
11
Funding Stage
Seed
Total Funding
$9.6M
1,393
GitHub followers
117
GitHub repos
8,550
GitHub stars
2
npm packages
5
HuggingFace models
Pricing found: $0.51 / hr, $0.80 / hr, $2.65 / hr, $2.90 / hr, $4.20 / hr
Repository Audit Available
Deep analysis of bentoml/BentoML — architecture, costs, security, dependencies & more
Yes, BentoML offers a free tier. Pricing found: $0.51 / hr, $0.80 / hr, $2.65 / hr, $2.90 / hr, $4.20 / hr
Key features include: Deploy Any Model, Open Model Catalog, Custom Models, Manage Inference, Scale Efficiently, Orchestrate Compute, Your Cloud, Open Source Model Launcher.
BentoML is commonly used for: Deploying machine learning models for real-time predictions in web applications., Serving custom deep learning models for image recognition tasks., Scaling inference workloads for large-scale data processing in cloud environments., Integrating with CI/CD pipelines for continuous deployment of AI models., Optimizing model performance for edge devices and IoT applications., Facilitating A/B testing of different model versions in production..
BentoML integrates with: TensorFlow, PyTorch, Scikit-learn, Keras, Docker, Kubernetes, AWS Lambda, Google Cloud Functions, Azure Machine Learning, MLflow.
BentoML has a public GitHub repository with 8,550 stars.
