Banana.dev vs Paperspace Comparison
Detailed comparison of features, pricing, and capabilities
Last updated May 13, 2026
Overview
Compare key metrics and features at a glance
Banana.dev
https://www.banana.dev
Banana.dev was a cloud platform that enabled developers to deploy and scale machine learning models on serverless GPU infrastructure with minimal configuration. It provided a simple API-based interface for running inference workloads, allowing teams to avoid managing their own GPU servers. The service shut down in 2023 as the team pivoted or wound down operations.
Paperspace
https://www.paperspace.com
Paperspace is a cloud computing platform specializing in GPU-accelerated virtual machines and machine learning infrastructure, enabling developers and data scientists to build, train, and deploy AI/ML models at scale. It offers products including Gradient, a MLOps platform for running Jupyter notebooks and ML pipelines, and Core, which provides on-demand GPU cloud instances. Paperspace was acquired by DigitalOcean in 2023, integrating its GPU cloud capabilities into DigitalOcean's broader cloud services portfolio.
Quick Comparison
| Detail | Banana.dev | Paperspace |
|---|---|---|
| Category | AI Cloud Infrastructure | AI Cloud Infrastructure |
| Starting Price | $20/mo | Free |
| Plans Available | 3 | 8 |
| Features Tracked | 15 | 15 |
| Founded | 2021 | 2014 |
| Headquarters | San Francisco, USA | New York, USA |
Features
Detailed feature-by-feature comparison
Feature Comparison
| Feature | | |
|---|---|---|
| api | ||
| API Endpoints | ||
| Full API Access | ||
| Open API & SDKs | ||
| core | ||
| Autoscaling GPUs | ||
| Built-in Observability | ||
| Collaboration Tools | ||
| Container Deployments | ||
| GPU Instances | ||
| High-Speed Networking | ||
| Instant Provisioning | ||
| Jupyter Notebooks | ||
| ML Monitoring | ||
| Max Parallel GPUs | Add-on | |
| Model Deployments | ||
| Pay-per-Use Pricing | ||
| Per-Second Billing | ||
| Persistent Storage | ||
| Pre-configured Frameworks | ||
| Request Analytics | ||
| Rolling Deploys | ||
| Serverless GPU Inference | ||
| Team Collaboration | ||
| Windows Machines | ||
| Workflows | ||
| custom | ||
| Custom GPU Types | ||
| integration | ||
| CLI Tool | ||
| GitHub Integration | ||
| Kubernetes Support | ||
| support | ||
| Hands-on Support | ||
| Performance Monitoring | ||
Pricing
Compare pricing plans and value for money
Banana.dev
From $20/mo
Price Components
- base_fee: $1200/month
- compute: $0/at-cost compute
- team_members: $0/member (10 included)
- base_fee: $0/month
- compute: $0/at-cost compute
Best For
Small dev teams prototyping ML inference APIs who previously used Banana.dev and now seek similar serverless GPU options.
Paperspace
From $0/mo
Price Components
- base_fee: $0/month
- storage: $0/GB (5 included)
- base_fee: $8/month
- storage: $0/GB (15 included)
- base_fee: $39/month
Best For
ML engineers and data scientists needing cost-efficient, GPU-accelerated development environments with integrated MLOps tools and flexible per-second billing.
Integrations
See which third-party services are supported
Supported Integrations
Coming Soon
Integration comparison data for Banana.dev, Paperspace is being collected and will be available soon.
Strengths & Limitations
Key strengths and limitations of each service
Banana.dev
Small dev teams prototyping ML inference APIs who previously used Banana.dev and now seek similar serverless GPU options.
- Serverless GPU inference with autoscaling from zero eliminates node management, unlike managed clusters from hyperscalers.
- Pay-per-use pricing passes through at-cost GPU compute, minimizing waste compared to fixed instance competitors.
- Built-in observability and request analytics provide real-time insights without extra tooling integrations.
- GitHub integration and CLI enable seamless CI/CD for ML model deployments.
- Service shut down in 2023, making it unavailable for new deployments or ongoing use.
- Small team size (1-10 employees) limited enterprise-grade support and feature depth.
- Seed funding stage restricted scalability for massive production workloads.
Paperspace
ML engineers and data scientists needing cost-efficient, GPU-accelerated development environments with integrated MLOps tools and flexible per-second billing.
- Per-second billing with no hourly minimums enables precise cost control for variable GPU workloads compared to competitors' hourly models
- Integrated MLOps platform (Gradient) combines managed Jupyter notebooks, automated pipelines, and model deployment in one interface without switching tools
- Access to enterprise-grade GPUs (H100, A100) with 10 Gbps backend networking optimized specifically for AI/ML training at scale
- Limited market presence and brand recognition post-DigitalOcean acquisition compared to established competitors like AWS SageMaker or Google Colab
- Smaller global data center footprint than hyperscalers, potentially limiting geographic redundancy and latency optimization for distributed teams
Company Info
Company details and background
Banana.dev
Paperspace
Comparison FAQ
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