Banana
GPU inference hosting with autoscaling, deployment tooling, and built-in operational analytics.
About Banana
Banana is an inference-hosting platform for AI teams running high-throughput model workloads on GPUs. It automatically adjusts GPU replicas as demand changes and uses pass-through compute pricing, pairing infrastructure costs with a flat platform fee. The platform brings deployment and operations into one workflow through GitHub integration, CI/CD, a CLI, rolling deployments, tracing, searchable logs, request analytics, and business analytics. Teams can inspect traffic, latency, errors, utilization, endpoint usage, and spend, while an open API, SDKs, and CLI support deployment automation. Banana also uses the open-source Potassium HTTP framework for custom containerized backends, allowing developers to bring libraries such as PyTorch, TensorFlow, and Hugging Face Transformers. The listed Team plan supports up to 10 members, 5 projects, and 50 parallel GPUs; organizations needing SAML SSO, larger GPU limits, customizable queues, build-pipeline GPUs, or dedicated support are directed to Enterprise.
Best for
Best use cases
Serving variable-demand model inference workloads
Managing GPU endpoint releases through GitHub and CI/CD
Tracking request performance, usage, and infrastructure spend
Compare before choosing
Pass-through compute pricing reduces provider markup on GPU time
The Team plan combines a fixed monthly fee with compute at cost
Enterprise adds SAML SSO, higher limits, queues, and dedicated support
Features
Scale model inference
Run high-throughput inference services while automatically adjusting GPU replicas as request demand changes.
Operate production endpoints
Monitor traffic, latency, errors, utilization, endpoint usage, and spend from the platform’s operational views.
Automate deployment workflows
Use the open API, SDKs, CLI, GitHub integration, CI/CD, branch deployments, and environments to manage releases.
Build custom model backends
Package Potassium applications in containers with libraries such as PyTorch, TensorFlow, and Hugging Face Transformers.
Pros
- Automatic GPU replica scaling can align capacity with changing request demand.
- GitHub, CI/CD, CLI, rolling deploys, logs, and tracing reduce workflow fragmentation.
- Request and business analytics connect endpoint activity with performance and spend.
Cons
- The Team plan costs $1200 per month before compute charges are added.
- Team is capped at 10 members, 5 projects, and 50 parallel GPUs.
- SAML SSO, higher GPU limits, customizable queues, and dedicated support require Enterprise.
Pricing Plans
Team
- 10 team members
- 5 projects
- 50 maximum parallel GPUs
- Custom GPU types
- Logging and search
- Percent-utilization autoscaling
- Request analytics
- Business analytics
- Branch deployments
- Environments
Enterprise
- Everything in Team
- SAML SSO
- Automation API
- Higher parallel GPU limits
- Customizable inference queues
- Build-pipeline GPUs
- Dedicated support
* Pricing information is for reference only. Please refer to the latest announcement on Banana's official website.