Sold by: Baseten
Deployed on AWS
Machine learning infrastructure that just works
4.3
Overview
At Baseten, we provide all the infrastructure you need to deploy and serve ML models performantly, scalably, and cost-efficiently.
With Baseten, you can:
- Deploy your proprietary ML models with optimized serving engines.
- Deploy open-source models on dedicated instances.
- Handle massive traffic spikes with autoscaling model deployments.
- Save on infra costs with scale to zero and lighting fast cold starts.
- Manage deployments, metrics, and spending with role-based access control.
Connect with us to discuss your ML infrastructure needs and learn more about our available live engineering support, custom POCs, volume discounts, and self-hosted options.
Highlights
- Highly performant autoscaling infrastructure that goes from prototype to production seamlessly.
- Reliable logging and visibility across deployments, health, metrics, and spend in your Baseten workspace.
- Enterprise-grade security and reliability with SOC 2 Type II, HIPAA compliance, and custom SLAs.
Details
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Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Trust Center
Access real-time vendor security and compliance information through their Trust Center powered by Drata or Vanta. Review certifications and security standards before purchase.
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on the duration and terms of your contract with the vendor, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/month |
|---|---|---|
Baseten Base Package | Listed pricing is indicative only. All purchases are completed via AWS Marketplace private offers tailored to your requirements. Reach out to request a custom quote and private offer | $10,000.00 |
Dimension | Description | Cost/unit |
|---|---|---|
additional_usage | Additional usage | $1.00 |
Dimensions summary
This listing uses a contract structure with two dimensions. The Baseten Base Package sets your committed starting purchase. Additional usage covers consumption beyond that base amount, billed as you use it. You pay only for the compute your models actually use, not idle time. Listed prices are indicative only. You finalize the actual terms through an AWS Marketplace private offer built around your needs. Contact the vendor to request a custom quote and private offer.
Top-of-mind questions for buyers
You pay for the compute your models use, measured to the minute. This includes time spent deploying, scaling up or down, and making predictions. You are not charged for idle time. The Base Package covers your committed amount; additional usage covers consumption beyond it.
No. You pay only for the time your model uses compute. That includes active deployment, scaling, and prediction time. Idle time does not accrue charges. You control how your model scales up and down, so you decide when compute runs.
The Base Package sets your committed starting purchase. Additional usage bills for consumption beyond that base, charged as you use compute. Both appear on the same invoice. If your workloads stay within the base amount, additional usage charges stay low; heavy use drives the additional usage portion up.
www.baseten.co
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Vendor refund policy
All fees are non-refundable and non-cancellable except as required by law.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Resources
Vendor resources
Support
Vendor support
Our standard email support is available Monday through Friday during business hours (Pacific time).
We offer substantial additional support options, including Slack connect, live engineering support, custom POCs, and custom response SLAs.
support@baseten.co
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Updated weekly
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
Mixed reviews
Negative reviews
AI generated from product descriptions
Model Deployment and Serving
Supports deployment of proprietary ML models with optimized serving engines and open-source models on dedicated instances.
Autoscaling Infrastructure
Handles massive traffic spikes with autoscaling model deployments and supports scale to zero functionality with fast cold starts.
Monitoring and Observability
Provides logging and visibility across deployments, health metrics, and spending through a centralized workspace.
Access Control and Management
Implements role-based access control for managing deployments, metrics, and spending.
Security and Compliance
Maintains SOC 2 Type II certification, HIPAA compliance, and offers custom SLAs for enterprise-grade reliability.
GPU Container Provisioning
Spin up GPU-enabled containers in as little as one second with custom infrastructure for rapid iteration and scaling.
Autoscaling Capability
Automatically scale resources from zero to hundreds of GPUs and back down based on workload demands without manual infrastructure management.
Infrastructure as Code Deployment
Deploy Python functions to the cloud using infrastructure-as-code to define custom container images and hardware requirements.
Serverless Compute Architecture
Serverless compute platform that abstracts infrastructure management for ML inference, fine-tuning, and batch data processing workloads.
Pay-Per-Use Resource Billing
Resource-based billing model that charges only for the actual compute time consumed during workload execution.
Model Deployment Infrastructure
Inference Endpoints enable deployment of models as secure, production-ready APIs with fast inference capabilities
Application Hosting Platform
Spaces provides hosting for machine learning applications with integrated GPU resources and pre-configured dependencies
Enterprise Security and Access Management
Enterprise Hub includes Single Sign-On, Resource Groups, Audit Logs, and Storage Regions for advanced security and access controls
Model and Dataset Repository
Access to over 1 million pre-trained models, datasets, and AI applications for text, image, audio, and video processing
Standard contract
Customer reviews
Jeni J.
Deploying AI Models Is Surprisingly Easy with Baseten
Reviewed on Jul 29, 2026
Review provided by G2
What do you like best about the product?
I really like how Baseten simplifies deploying AI models while giving production-grade performance. The developer experience is excellent with straightforward deployment workflows and reliable autoscaling. It also offers GPU optimization and built-in monitoring, which makes transitioning from experimentation to a scalable production API really easy without much infrastructure overhead. I appreciate the flexibility to deploy both open-source and custom models with minimal configuration. The built-in features like logging and performance insights are invaluable for troubleshooting and optimizing models in production. Plus, the documentation is easy to follow, and the initial setup was very easy.
What do you dislike about the product?
One area I'd like to see improved is pricing transparency and cost optimization guidance, especially for teams scaling GPU workloads, since estimating inference costs can become difficult as usage grows. I also think the platform could offer more built-in deployment templates, debugging tools, and finer-grained performance analytics to make it even easier to optimize latency, troubleshoot production issues, and onboard new users.
What problems is the product solving and how is that benefiting you?
I use Baseten to deploy AI models in production without managing GPU infrastructure. It simplifies turning models into scalable APIs with autoscaling and monitoring, saving me from DevOps hassles. I focus on building applications while Baseten manages deployment complexity and offers smooth performance.
LOKESH G.
Fast, Reliable Model Deployment with Autoscaling and a Smooth Developer Experience
Reviewed on Jul 23, 2026
Review provided by G2
What do you like best about the product?
It makes it easy to deploy and serve AI/ML models in production. The platform provides a straightforward deployment workflow, fast inference performance, autoscaling, and reliable infrastructure without requiring extensive DevOps effort. It also integrates smoothly with modern AI frameworks and APIs, so moving models from development to production feels simple and consistent. Monitoring, version management, and the overall developer experience help streamline the entire model lifecycle from deployment through ongoing updates.
What do you dislike about the product?
Baseten is generally easy to use, but some of the more advanced configuration options and deployment settings come with a learning curve. The documentation for complex use cases could be more detailed and easier to follow, and pricing can become expensive as inference volume grows. Expanding the built-in analytics and adding stronger cost-optimization tools would also make the platform even more valuable.
What problems is the product solving and how is that benefiting you?
Baseten makes it easier to deploy, scale, and manage machine learning models in production. It cuts down the operational overhead of maintaining inference infrastructure, so I can focus more on developing and improving models rather than managing servers. As a result, deployment has been faster, reliability has improved, and it’s become simpler to deliver AI-powered applications with consistent performance.