Banana
Scale AI inference workloads with autoscaling GPU infrastructure
Target Audience
- AI/ML engineering teams
- DevOps engineers managing ML infrastructure
- Enterprises scaling AI applications
Hashtags
Social Media
Overview
Banana provides managed GPU hosting specifically designed for AI inference at scale. It automatically adjusts GPU resources to match demand, helping teams maintain performance while controlling costs. The platform includes essential DevOps tools and charges only for actual compute usage without hidden markups.
Key Features
Autoscaling GPUs
Dynamically adjusts resources to balance cost and performance
Pass-through pricing
Pay only for compute time without vendor markup
DevOps integration
GitHub, CI/CD, and CLI tools built-in
Observability
Real-time monitoring of traffic and system performance
Business analytics
Track spending and endpoint usage patterns
Use Cases
Deploy production AI models
Monitor inference performance
Automate ML deployments
Optimize GPU costs
Manage team access controls
Pros & Cons
Pros
- True pay-as-you-go GPU pricing
- Automatic scaling eliminates manual provisioning
- Full-stack DevOps capabilities included
- Real-time performance diagnostics
Cons
- $1200/month base price may be high for small teams
- Limited to San Francisco for physical banana delivery
- Requires Kubernetes knowledge for advanced customization
Pricing Plans
Team
monthlyFeatures
- 50 max parallel GPUs
- Percent utilization autoscaling
- Branch deployments
- 10 team members
Enterprise
monthlyFeatures
- SAML SSO
- Dedicated support
- Customizable inference queues
- Build pipeline GPUs
Pricing may have changed
For the most up-to-date pricing information, please visit the official website.
Visit websiteFrequently Asked Questions
How does Banana's pricing compare to traditional cloud providers?
Banana charges base subscription + at-cost compute without markup, unlike providers that add GPU usage premiums
What AI frameworks does Banana support?
Supports any framework through Kubernetes, with native integration for popular Python-based tools
Can I handle sudden traffic spikes?
Autoscaling automatically provisions additional GPUs based on percent utilization metrics
Integrations
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