Baseten: Simplifying ML Deployment to Unlock Peak Performance
From healthcare and finance to startups and consumer tech, artificial intelligence (AI) is reshaping how industries operate— powering everything from clinical trial analysis and fraud detection to customer support automation and personalized recommendations. Yet, while building AI and machine learning (ML) models has become more accessible, deploying them into production remains a significant hurdle. Many teams face challenges with complex infrastructure, scalability, and the intricacies of integrating AI models into real-world applications.
Baseten bridges this gap by providing a robust developer platform for deploying and serving ML models with minimal infrastructure effort. Purpose-built for modern AI workflows, Baseten supports a wide range of applications—including transcription, large language models (LLM), image generation, text-to-speech and embeddings.
With Baseten, organizations can accelerate time-toproduction by focusing on model performance and product development rather than DevOps. The platform offers essential capabilities such as scalable model inference, cloud-native infrastructure, versioning, experiment tracking and flexible deployment strategies—allowing teams to build, iterate and ship ML products with confidence.
“In this market, your number one differentiation is how fast you can move. That is the core benefit for our customers,” says Tuhin Srivastava, co-founder and CEO. “You can go to production without worrying about reliability, security and performance.”
Baseten bridges this gap by providing a robust developer platform for deploying and serving ML models with minimal infrastructure effort. Purpose-built for modern AI workflows, Baseten supports a wide range of applications—including transcription, large language models (LLM), image generation, text-to-speech and embeddings.
With Baseten, organizations can accelerate time-toproduction by focusing on model performance and product development rather than DevOps. The platform offers essential capabilities such as scalable model inference, cloud-native infrastructure, versioning, experiment tracking and flexible deployment strategies—allowing teams to build, iterate and ship ML products with confidence.
“In this market, your number one differentiation is how fast you can move. That is the core benefit for our customers,” says Tuhin Srivastava, co-founder and CEO. “You can go to production without worrying about reliability, security and performance.”
At the heart of Baseten’s platform is its robust model performance monitoring (MPM) system. Designed for real-time visibility, MPM uses advanced techniques like speculative decoding and ultra-low-latency Compound AI to deliver deep insights into model behavior, accuracy and drift. It visualizes key metrics such as prediction volume, response times and resource usage, enabling teams to proactively detect and address issues. By monitoring latency across percentiles and tracking CPU and memory consumption across replicas, organizations can ensure efficient resource allocation and maintain reliable, highperforming AI applications.
Complementing MPM is Baseten’s model management platform, which supports the entire lifecycle of machine learning models. Built-in autoscaling and intelligent resource management ensure consistent performance under fluctuating workloads—whether models run on Baseten’s managed cloud or internal infrastructure. The platform also supports control and experiment tracking, logging each model iteration for easy comparison and optimization.
In this market, your number one differentiation is how fast you can move. That is the core benefit for our customers. You can go to production without worrying about reliability, security and performance
Complementing MPM is Baseten’s model management platform, which supports the entire lifecycle of machine learning models. Built-in autoscaling and intelligent resource management ensure consistent performance under fluctuating workloads—whether models run on Baseten’s managed cloud or internal infrastructure. The platform also supports control and experiment tracking, logging each model iteration for easy comparison and optimization.
To streamline deployment, Baseten enables advanced strategies such as A/B testing and canary releases, helping teams fine-tune performance and reduce risk. Powering this process is Truss, Baseten’s open-source Python library that simplifies deploying models built with frameworks like Transformers, TensorRT or Triton. By eliminating the need for complex DevOps or infrastructure configurations, Truss enables fast, reliable and scalable AI deployments.
Built on a cloud-native architecture, Baseten empowers organizations to scale models seamlessly across multi-cloud and multi-region environments. Its optimized autoscaling automatically aligns computing resources with real-time traffic, helping teams maintain ultra-low latency without overspending. Backed by a commitment to five-nines (99.999 percent) availability, Baseten ensures AI products remain consistently accessible and responsive—even under heavy load.
A dedicated team of engineers works closely with clients to deploy low-latency, high-efficiency models using advanced techniques such as speculative decoding and TensorRT optimization. They continuously monitor model behavior, dynamically adjust resources with elastic autoscaling,and manage the full infrastructure stack to keep performance high and costs low.
Integrating Baseten into existing systems, developers can offload the burdens of infrastructure and operations. This frees them to focus on building great products, refining their models, and leveraging their data—without getting tangled in the complexities of production environments.
Built on a cloud-native architecture, Baseten empowers organizations to scale models seamlessly across multi-cloud and multi-region environments. Its optimized autoscaling automatically aligns computing resources with real-time traffic, helping teams maintain ultra-low latency without overspending. Backed by a commitment to five-nines (99.999 percent) availability, Baseten ensures AI products remain consistently accessible and responsive—even under heavy load.
A dedicated team of engineers works closely with clients to deploy low-latency, high-efficiency models using advanced techniques such as speculative decoding and TensorRT optimization. They continuously monitor model behavior, dynamically adjust resources with elastic autoscaling,and manage the full infrastructure stack to keep performance high and costs low.
Integrating Baseten into existing systems, developers can offload the burdens of infrastructure and operations. This frees them to focus on building great products, refining their models, and leveraging their data—without getting tangled in the complexities of production environments.
