Baseten vs Paperspace Comparison
Detailed comparison of features, pricing, and capabilities
Last updated May 1, 2026
Overview
Compare key metrics and features at a glance
Baseten
https://www.baseten.co
Baseten is a machine learning infrastructure platform that enables developers and ML engineers to deploy, serve, and scale AI models in production. It provides tools for building model pipelines, creating model-backed applications, and managing inference workloads with support for popular frameworks like PyTorch, TensorFlow, and Hugging Face. Baseten focuses on simplifying the MLOps workflow by offering features such as autoscaling, GPU support, and a Python-native SDK called Truss for packaging and deploying models.
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 | Baseten | Paperspace |
|---|---|---|
| Category | AI Cloud Infrastructure | AI Cloud Infrastructure |
| Starting Price | Free | Free |
| Plans Available | 3 | 8 |
| Features Tracked | 14 | 15 |
| Founded | 2020 | 2014 |
| Headquarters | San Francisco, USA | New York, USA |
Features
Detailed feature-by-feature comparison
Feature Comparison
| Feature | | |
|---|---|---|
| api | ||
| Full API Access | ||
| REST API Endpoints | ||
| compliance | ||
| SOC 2 Type II | ||
| core | ||
| Autoscaling | ||
| Collaboration Tools | ||
| GPU Instances | ||
| GPU/CPU Infrastructure | ||
| Global Scaling | ||
| High-Speed Networking | ||
| Inference Optimization | ||
| Instant Provisioning | ||
| Jupyter Notebooks | ||
| ML Monitoring | ||
| Model Deployment | ||
| Model Deployments | ||
| Monitoring & Logging | ||
| Multi-Model Workflows | ||
| Per-Second Billing | ||
| Persistent Storage | ||
| Pre-configured Frameworks | ||
| Truss Deployment | ||
| Windows Machines | ||
| Workflows | ||
| custom | ||
| Custom Environments | ||
| Hybrid Deployments | ||
| integration | ||
| Kubernetes Support | ||
| SDK Integration | ||
| security | ||
| API Key Access Control | ||
| support | ||
| Hands-on Support | ||
Pricing
Compare pricing plans and value for money
Baseten
From $0/mo
Price Components
- Monthly Subscription: $0/month
- DeepSeek V4 Input: $0.00000174/token
- DeepSeek V4 Output: $0.00000348/token
- GPU Compute T4: $0.01052/minute
- GPU Compute A100: $0.06667/minute
Best For
ML engineers and AI teams deploying production-scale open-source or custom models needing fast autoscaling, GPU optimization, and compliance without managing infrastructure.
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 Baseten, Paperspace is being collected and will be available soon.
Strengths & Limitations
Key strengths and limitations of each service
Baseten
ML engineers and AI teams deploying production-scale open-source or custom models needing fast autoscaling, GPU optimization, and compliance without managing infrastructure.
- Truss SDK enables Python-native packaging and deployment of models from PyTorch, TensorFlow, and Hugging Face, simplifying MLOps beyond general cloud ML services.
- Autoscaling to zero with global multi-cloud GPU capacity supports massive inference scale and cost efficiency unmatched by broader hyperscalers.
- OpenAI-compatible APIs and Baseten Chains optimize latency/throughput 2x+ faster than competitors like Fireworks or Modal.
- SOC 2 Type II, HIPAA/GDPR compliance with no input/output storage and hybrid self-host options for secure enterprise AI.
- Smaller scale (51-200 employees, Series B) limits global infra compared to hyperscalers like AWS SageMaker or GCP Vertex AI.
- Pro and Enterprise tiers require volume commitments for discounts and custom SLAs, less ideal for tiny teams on strict budgets.
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
Baseten
Paperspace
Comparison FAQ
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