Banana

Overview

Banana provides serverless GPU infrastructure optimized for ML inference. The platform focuses on production ML inference workloads, offering auto-scaling, low latency, and cost efficiency. Banana allows developers to deploy models as APIs without managing servers or GPUs.

The platform is designed for teams running inference at scale, with features like A/B testing, multi-region deployment, and detailed analytics. Banana positions itself as production-grade infrastructure for ML teams.

Key Features

**Serverless GPUs**: On-demand GPU access

**Auto-Scaling**: Scale to zero when idle

**Multi-Region**: Deploy across regions

**A/B Testing**: Test model versions

**Analytics**: Detailed inference metrics

**Fast Cold Starts**: Quick model loading

**Custom Models**: Deploy any framework

**Production-Ready**: Built for scale

When to Use Banana

Banana is ideal for:

Production ML inference at scale

Applications requiring low latency

Teams wanting managed GPU infrastructure

Multi-model deployments

A/B testing model versions

Global inference needs

Pros

Good for production workloads

Fast inference times

Multi-region deployment

A/B testing built-in

Scales automatically

Good analytics

Production-focused

Competitive pricing

Cons

Less model library than Replicate

Smaller platform than major clouds

Requires some DevOps knowledge

Limited free tier

Vendor lock-in concerns

Smaller community

Documentation could be better

Less suitable for quick prototyping

Pricing

**Pay Per Use**: Starts at $0.0001 per second

**GPU Types**: A10, A100, H100 available

**No Free Tier**: Paid plans only

**Enterprise**: Custom pricing for scale