Baseten vs Modal
Compare Baseten and Modal by workflow, pricing, privacy, model support, and best use cases.
Baseten
Choose Baseten when your main problem is deploying and operating AI models in production, especially when custom model code, GPU autoscaling, observability, and enterprise controls matter more than having a bundled coding assistant.
Modal
Choose Modal when you need Python-first serverless compute for AI, data, GPU, inference, batch jobs, queues, notebooks, or backend services. Choose E2B or Daytona for dedicated AI sandbox infrastructure, Vercel Sandbox for Vercel-native code execution, RunPod or Baseten for alternative GPU hosting, and GitHub Codespaces or Coder for full developer workspaces.
Key Differences
Workflow
Baseten is a production AI inference platform for teams that need to deploy, scale, and operate custom or open-source models as APIs.
Modal is a serverless compute platform for AI, data, Python, GPU, batch, sandbox, notebook, and inference workloads that need elastic cloud execution without infrastructure management.
Editor base
Standalone
CLI
Feature Comparison
| Feature | Baseten | Modal |
|---|---|---|
| Primary workflow | Baseten is a production AI inference platform for teams that need to deploy, scale, and operate custom or open-source models as APIs. | Modal is a serverless compute platform for AI, data, Python, GPU, batch, sandbox, notebook, and inference workloads that need elastic cloud execution without infrastructure management. |
| Type | resource | resource |
| Editor base | Standalone | CLI |
| Pricing model | freemium | freemium |
| Starting price | Unknown | $0 |
| Free plan | Yes | Yes |
| Open source | No | No |
| Local models | No | No |
| BYOK | No | No |
| Platforms | Web, API, Python, CLI, Docker-compatible model packaging, OpenAI-compatible clients | Python SDK, CLI, Web dashboard, Serverless functions, GPU containers, Web endpoints, Cron jobs, Job queues, Modal Sandboxes, Modal Notebooks, Persistent volumes, Cloud-hosted Linux containers |
| Models | DeepSeek, Qwen, GLM, Kimi, GPT OSS 120B, NVIDIA Nemotron, Llama, Mistral, embedding models, reranking models, classification models, image generation models | Unknown |
| Enterprise features | SOC 2 Type II certification, HIPAA compliance, Teams and granular access control, SSO and SCIM, Audit logs, Secrets management, Restricted environments, OIDC, Regional environments, Single-tenant deployments, Self-hosted deployments, Hybrid deployments, Private networking and compliance policies, Forward-deployed engineering support | Team workspace, Enterprise contracts, Custom support, Production workload governance, Usage visibility, Secrets management, Environment separation, Persistent volumes, Web endpoints, Custom containers and images, Autoscaling controls, GPU access, Modal Sandboxes, Modal Notebooks, Dashboard observability, Security and compliance review through enterprise sales |
| Best for | Teams deploying custom AI models to production APIs, AI applications that need GPU-backed inference with autoscaling, Engineering teams moving from Hugging Face checkpoints to production endpoints, LLM, embedding, reranking, image, and multimodal model serving, ML teams that need observability, secrets, access control, and deployment environments | Serverless GPU inference, LLM serving, Image and video generation workloads, Speech and audio processing, Batch data processing, Fine-tuning jobs, Parallel Python jobs, Scheduled compute, AI backend services, Model APIs, Data science workloads, Code execution backends, Agent infrastructure that needs scalable compute, Teams that want cloud GPUs without managing Kubernetes |
| Not best for | Users looking for a complete AI code editor, Developers who only need a chat-based coding assistant, Small prototypes where a simple hosted LLM API is enough, Teams without ML deployment experience who do not want to manage model behavior, Projects that require fully local inference without a managed cloud service | Developers looking for a browser IDE, Users looking for AI autocomplete or code chat, Non-technical users looking for prompt-to-app builders, Teams needing a GitHub-native cloud development environment, Workloads that require fully self-hosted or on-prem execution, Projects needing fixed monthly compute pricing with no usage variability, Simple frontend demos better served by StackBlitz or CodeSandbox |
Use Case Winners
Both Baseten and Modal have comparable signals here.
Both Baseten and Modal have comparable signals here.
Modal lists more team or enterprise controls.
Modal has stronger frontend or web workflow signals.
Baseten supports more model/provider options or BYOK-style workflows.
Neither tool shows a strong signal for this use case in the current structured data.
Pricing Comparison
Baseten
- Model APIsUsage-based / per 1M tokens
Instant access to pre-optimized hosted models through OpenAI-compatible APIs.
- Dedicated DeploymentsFrom $0.01052 / per GPU minute
Metered GPU deployments with per-minute billing; listed GPU options include T4, L4, A10G, A100, H100, and B200.
- CPU DeploymentsFrom $0.00058 / per minute
Lower-cost CPU instances for non-GPU workloads and supporting services.
- TrainingUsage-based / per compute minute
On-demand compute for training jobs, including GPU options similar to deployment pricing.
- Pro / VolumeCustom
Volume discounts and higher-touch support can be negotiated for larger workloads.
Modal
- Starter$0 / month
Free workspace plan with usage-based compute billing for serverless CPU, memory, GPU, sandbox, storage, and related resources.
- Team$250 / month
Team workspace plan plus compute usage, designed for shared production workloads, collaboration, and higher team needs.
- EnterpriseCustom
Custom pricing and support for larger organizations with security, compliance, governance, scaling, and procurement needs.
- CPU and MemoryUsage-based / second
Serverless functions and workloads are billed by requested compute resources and execution time.
- GPUUsage-based / second
GPU instances such as T4, L4, A10G, L40S, A100, H100, H200, and B200 are priced by GPU type and runtime.
Privacy & Security
Baseten
Baseten documentation states that it does not store model inputs, outputs, or weights by default, while async inference inputs may be temporarily stored until processed. Baseten also documents SOC 2 Type II certification, HIPAA compliance, workload isolation, and self-hosted or single-tenant options for customers with stricter requirements.
Modal
Modal workloads can process application code, container images, environment variables, secrets, model files, datasets, logs, notebooks, sandbox contents, volumes, and runtime outputs. Teams should configure Modal Secrets, control data copied into images or volumes, limit public endpoints, review logs for sensitive output, and design retention, access, and network policies before running proprietary models, private data, or generated-code execution workloads.
Choose Baseten if...
- Teams deploying custom AI models to production APIs
- AI applications that need GPU-backed inference with autoscaling
- Engineering teams moving from Hugging Face checkpoints to production endpoints
- LLM, embedding, reranking, image, and multimodal model serving
- ML teams that need observability, secrets, access control, and deployment environments
Choose Modal if...
- Serverless GPU inference
- LLM serving
- Image and video generation workloads
- Speech and audio processing
- Batch data processing
Avoid Baseten if...
- Users looking for a complete AI code editor
- Developers who only need a chat-based coding assistant
- Small prototypes where a simple hosted LLM API is enough
- Teams without ML deployment experience who do not want to manage model behavior
- Projects that require fully local inference without a managed cloud service
Avoid Modal if...
- Developers looking for a browser IDE
- Users looking for AI autocomplete or code chat
- Non-technical users looking for prompt-to-app builders
- Teams needing a GitHub-native cloud development environment
- Workloads that require fully self-hosted or on-prem execution