Tensorfuse
Run serverless GPUs on your own cloud
About the Company
The future of AI is inference
With the rise of agentic workflows and reasoning models, enterprises now need 100x more compute and 10x more throughput to run state-of-the-art AI models. Building robust, scalable inference systems has become a top priority—but it's also a major bottleneck, requiring deep expertise in low-level systems, snapshotters, Kubernetes, and more.
Tensorfuse removes this complexity by helping teams run serverless GPUs in their own AWS account. Just bring:
- Your code and env as Dockerfile
- Your AWS account with GPU capacity
We handle the rest—deploying, managing, and autoscaling your GPU containers on production-grade infrastructure. Teams use Tensorfuse for:
- Developer Experience: 10x better than current solutions, helping you move fast and save months of engineering time.
- Seamless Autoscaling: Spin up 100s of GPUs in real time with startup times as low as 30 seconds (vs. 8–10 mins with custom setups).
- Security: All workloads run inside your own AWS VPC—your data never leaves your account.
- Production Features: Native CI/CD integration, custom domain support, volume mounting, and more—out of the box.
We’re building the runtime layer for AI-native companies. Join us.
Founders
Samagra is the Co-Founder and CEO of Tensorfuse. Samagra has deep expertise in deploying production Machine Learning systems owing to his work on Multimodal Content Generation at Adobe Research and ML systems for network telemetry at UCSB. Samagra is a published AI researcher and holds a patent on Multimodal Content Generation. Additionally, Samagra authored the Java implementation of "AI: A Modern Approach," a widely used AI textbook in over 1,500 universities around the globe.
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