Baseten provides a platform for deploying and serving machine learning models with optimized inference speed and autoscaling capabilities, enabling seamless transition from development to production. The solution addresses the complexities of model infrastructure management, allowing teams to focus on building and iterating on their AI applications without incurring excessive costs.
Funding
$60M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
+6Founders
Product
Problem
Deploying and scaling machine learning models in production environments involves significant complexities related to infrastructure management, performance optimization, and cost control. Teams often struggle to efficiently transition models from development to production due to the challenges of autoscaling, inference speed, and security.
Solution
Baseten provides a platform for deploying and serving machine learning models with a focus on optimized inference speed and autoscaling capabilities. The platform simplifies the transition from development to production by abstracting away the complexities of model infrastructure management. Baseten offers tools for packaging models built in any framework, such as PyTorch, Tensorflow, TensorRT, and Triton, into a standard format for deployment in any environment. The platform also provides features for resource management, cost tracking, and real-time observability, enabling teams to focus on building and iterating on their AI applications. Baseten supports deployment in various environments, including Baseten Cloud, self-hosted, and hybrid configurations, allowing users to leverage existing cloud commitments while benefiting from the platform's performance optimizations.
Target Audience
Baseten targets engineering and machine learning teams that require a scalable and secure platform for deploying and serving AI models in production, including those in health-tech, and enterprises requiring high performance and reliability.
Features
- Open-source Truss model packaging for framework-agnostic deployment
- High model throughput with optimized serving engines and lower memory footprint
- Blazing fast cold starts through optimized image building, container starting, and resource provisioning
- Low latency inference with authentication and routing service, achieving up to 1,500 tokens per second
- Effortless GPU autoscaling that automatically adjusts resources based on traffic
- Instant API generation, automatically wrapping deployed models in an endpoint
- Comprehensive observability tools for real-time tracking of inference counts, response times, and GPU uptime
- HIPAA compliance for handling protected health information