Baseten vs Modal 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.
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 | Baseten | Modal |
|---|---|---|
| Category | AI Cloud Infrastructure | AI Cloud Infrastructure |
| Starting Price | Free | Free |
| Plans Available | 3 | 3 |
| Features Tracked | 14 | 20 |
| Founded | 2020 | 2021 |
| Headquarters | San Francisco, USA | New York, USA |
Features
Detailed feature-by-feature comparison
Feature Comparison
| Feature | | |
|---|---|---|
| api | ||
| REST API Endpoints | ||
| compliance | ||
| SOC 2 Type II | ||
| core | ||
| Automatic Dependency Management | ||
| Autoscaling | ||
| Batch Job Processing | ||
| Cron Jobs | ||
| Custom Container Runtime | ||
| GPU-Backed Notebooks | ||
| GPU/CPU Infrastructure | ||
| Global Scaling | ||
| High-Throughput Storage System | ||
| Inference Optimization | ||
| Model Deployment | ||
| Model Training and Fine-tuning | ||
| Monitoring & Logging | ||
| Multi-Cloud GPU Pool | ||
| Multi-Model Workflows | ||
| Python-Native Code Definition | ||
| Scale to Zero Pricing | ||
| Serverless GPU Inference | ||
| Truss Deployment | ||
| Web Endpoints | ||
| custom | ||
| Custom Environments | ||
| Hybrid Deployments | ||
| integration | ||
| Cloud Bucket Integration | ||
| External Database Connectivity | ||
| Key-Value Dictionaries | ||
| Networking Tools | ||
| Persistent Volumes | ||
| SDK Integration | ||
| Task Queues | ||
| security | ||
| API Key Access Control | ||
| Sandboxes for Untrusted Code | ||
| support | ||
| Integrated Logging and Monitoring | ||
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.
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 Baseten, Modal 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.
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
Baseten
Modal
Comparison FAQ
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