Banana vs Scaleway
A detailed comparison to help you choose between Banana and Scaleway.
Quick Verdict
4.5/5
Banana
328 reviews
4.8/5
Scaleway
475 reviews
Scaleway is rated higher (4.8 vs 4.5). Banana is an ML model inference hosting platform. Deploy any model in a Docker container with fast warm-up. Pay-per-request pricing. Good for teams building AI product features. Scaleway provides bare-metal servers, VPS, and managed Kubernetes in European data centers. Designed for developers and small-to-medium businesses needing reliable cloud compute without vendor lock-in.
Banana Serverless GPU inference with built-in model serving | Scaleway European cloud infrastructure with competitive pricing and transparent billing | |
|---|---|---|
| Overview | ||
| Rating | 4.5 (328 reviews) | 4.8 (475 reviews)✓ |
| Pricing model | usage-based | freemium |
| Starting price | Free tier available | Free tier available |
| Best for | ML engineers and startups needing cost-effective serverless GPU inference without DevOps overhead | European startups and developers prioritizing cost efficiency, data sovereignty, and avoiding long-term contracts with established cloud providers. |
| Specifications (entry plan) | ||
| CPU cores | — | 2 vCPU |
| RAM | — | 2 GB |
| Storage | — | 20 GB |
| Bandwidth | — | 1 TB/mo |
| SLA uptime | — | 99.9% |
| Data-center count | — | 5 |
| €/vCPU/mo | — | €0.00 |
| €/GB RAM/mo | — | €0.00 |
| Features | ||
| IPv6 | ✓ | |
| DDoS protection | ✓ | |
| Automated backups | ||
| Snapshots | ✓ | |
| Managed option | ||
| Bare metal | ||
| GPU available | ||
| S3-compatible | ||
| Hourly billing | ✓ | |
| Free tier | ✓ | |
| Data-center locations | ||
| Regions | — | FranceNetherlandsPoland |
| Tags | ||
| Tags | gpu availableus datacenterapi access | free tierhourly billingipv6ddos protectionsnapshotsmanaged optionbare metalgpu availables3 compatibleeu datacentergdpr compliantterraform providerkubernetes supportapi access |
| Visit Banana → | Visit Scaleway → | |
Banana
Pros
- + Deploy ML models without managing servers or Kubernetes clusters
- + Access multiple GPU types (NVIDIA T4, A40, A100) for different performance needs
- + Use built-in model templates for common frameworks (PyTorch, TensorFlow, Hugging Face)
- + Scale automatically from zero to handle traffic spikes
Cons
- - Limited to inference workloads; not suitable for long-running batch jobs
- - Colder starts and potential latency compared to dedicated GPU instances
- - Smaller ecosystem and community compared to AWS or Google Cloud
Scaleway
Pros
- + Configure instances precisely with granular CPU, RAM, and storage options
- + Access bare-metal servers at lower price points than major competitors
- + Pay by the hour with no long-term contracts or setup fees
- + Manage infrastructure via API or intuitive dashboard
- + Comply with EU data residency requirements with multiple European regions
Cons
- - Smaller ecosystem and fewer third-party integrations compared to AWS or DigitalOcean
- - Support response times slower than premium enterprise providers
- - Limited US region availability; primarily Europe-focused infrastructure
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