Banana.dev vs Modal 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.
Modal
https://modal.com
Modal is a cloud infrastructure platform that allows developers and data scientists to run code in the cloud without managing servers or infrastructure. It provides a Python-native interface for running serverless functions, training machine learning models, and deploying AI applications with on-demand GPU and CPU compute. Modal handles scaling, containerization, and dependency management automatically, enabling teams to go from local code to production cloud workloads with minimal configuration.
Quick Comparison
| Detail | Banana.dev | Modal |
|---|---|---|
| Category | AI Cloud Infrastructure | AI Cloud Infrastructure |
| Starting Price | $20/mo | Free |
| Plans Available | 3 | 3 |
| Features Tracked | 15 | 20 |
| Founded | 2021 | 2021 |
| Headquarters | San Francisco, USA | New York, USA |
Features
Detailed feature-by-feature comparison
Feature Comparison
| Feature | | |
|---|---|---|
| api | ||
| API Endpoints | ||
| Open API & SDKs | ||
| core | ||
| Automatic Dependency Management | ||
| Autoscaling GPUs | ||
| Batch Job Processing | ||
| Built-in Observability | ||
| Container Deployments | ||
| Cron Jobs | ||
| Custom Container Runtime | ||
| GPU-Backed Notebooks | ||
| High-Throughput Storage System | ||
| Max Parallel GPUs | Add-on | |
| Model Training and Fine-tuning | ||
| Multi-Cloud GPU Pool | ||
| Pay-per-Use Pricing | ||
| Python-Native Code Definition | ||
| Request Analytics | ||
| Rolling Deploys | ||
| Scale to Zero Pricing | ||
| Serverless GPU Inference | ||
| Team Collaboration | ||
| Web Endpoints | ||
| custom | ||
| Custom GPU Types | ||
| integration | ||
| CLI Tool | ||
| Cloud Bucket Integration | ||
| External Database Connectivity | ||
| GitHub Integration | ||
| Key-Value Dictionaries | ||
| Networking Tools | ||
| Persistent Volumes | ||
| Task Queues | ||
| security | ||
| Sandboxes for Untrusted Code | ||
| support | ||
| Integrated Logging and Monitoring | ||
| 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.
Modal
From $0/mo
Price Components
- base_fee: $0/month (30 included)
- seats: $0/user (3 included)
- CPU: $0.0000131/core-second
- Memory: $0.00000222/GiB-second
- Nvidia B200: $0.001736/second
Best For
Python-focused ML teams and startups needing rapid GPU-accelerated model training and inference without managing Kubernetes, containers, or infrastructure scaling.
Integrations
See which third-party services are supported
Supported Integrations
Coming Soon
Integration comparison data for Banana.dev, Modal 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.
Modal
Python-focused ML teams and startups needing rapid GPU-accelerated model training and inference without managing Kubernetes, containers, or infrastructure scaling.
- Python-native serverless platform eliminates manual containerization and dependency management, reducing deployment friction for ML engineers and data scientists
- On-demand access to high-performance GPUs (A100, H100) with per-second billing removes upfront infrastructure costs and commitment lock-in common with traditional cloud providers
- Automatic horizontal scaling to thousands of parallel containers with zero-to-scale capability enables cost-efficient handling of bursty AI workloads without manual orchestration
- Limited to Python ecosystem, excluding teams using Go, Node.js, or other languages that dominate in serverless and edge computing markets
- Series B funding and 11-50 employee count signal smaller scale and fewer enterprise resources compared to hyperscalers (AWS, Google Cloud, Azure) controlling 65% of AIaaS market revenue
Company Info
Company details and background
Banana.dev
Modal
Comparison FAQ
Common questions about comparing Banana.dev and Modal
No FAQs available yet