I'm Aishwarya, co-founder of Inferless with@nilesh_agarwal22 . We're thrilled to officially launch Inferless today!
Background Story: Two years ago, while running an AI-powered app startup, we hit a big wall: deploying AI models was expensive, complicated, and involved lots of idle GPU costs. The process simply didn’t make sense, so we decided to fix it ourselves.
Inferless is a Serverless GPU inference platform that helps developers deploy AI models effortlessly:
✅ Instant Deployments: Deploy any ML model within minutes—no hassle of managing infrastructure. ✅ Ultra-Low Cold Starts: Optimized for instant model loading ✅ Auto-Scaling & Cost-Efficient: Scale instantly from one to millions and only pay for what you actually use. ✅ Flexible Deployment: Use our UI, CLI, or run models remotely—however you prefer.
Since our private beta, we've processed millions of API requests and helped customers like Cleanlab, Spoofsense, Omi, Ushur etc move their production workloads to us.
And now, Inferless is open for everyone—no waitlists, just sign up and deploy instantly!
Feel free to ask me anything in the comments or provide any feedback. Your feedback and support mean the world. 🙌
Inferless looks game-changing, Aishwarya! Instant deployments and low cold starts are huge for devs. Excited to see it scale—great job!
Instantly, GPU infrastructure stands out as the obvious future in the AI world. I've been following Nilesh and Aishwarya's progress updates on LinkedIn, and it's clear you're all working on something truly significant.
With GPUs driving faster computations and supporting scalable, efficient AI models, Inferless seems poised for a major impact. Team’s passion shines through. Good luck—let’s go 🚀
This product sounds incredibly promising! The ability to deploy machine learning models with ultra-low cold starts is a game changer, especially for developers looking to scale efficiently. I love the pay-as-you-go model, which makes it accessible for everyone. Just curious, what specific ML frameworks do you support for deployment?
This could revolutionize how teams manage ML workflows – truly a game-changer in the industry! Congrats!
As someone with a startup background, I completely understand the challenge of managing GPU infrastructure. This is such an interesting problem to solve—making AI deployments seamless, and eliminating wasted time or costs on idle GPUs. The instant deployment and auto-scaling features are a game-changer. Huge props to the Inferless team for simplifying this process! I’ll definitely be recommending it. 🙌
It's amazing to see how effortless GPU deployment has become—what used to be a complex process is now streamlined and accessible! 🚀 And the cost savings? An absolute game-changer. This is a huge step forward for developers and businesses alike. Wishing you all the success with the launch! 🎉
GPU deployment has become easy and cost-effective. Good luck with the launch!
Congratulations on the launch of Inferless! This platform effectively addresses the complexities of deploying machine learning models quickly and efficiently.
What strategies does Inferless employ to maintain performance and scalability while ensuring ultra-low cold start times for different machine learning models?
@nilesh_agarwal22@aishwaryagoel_08 Congrats on the launch, Aishwarya and team! 🚀 Inferless looks like a much-needed step forward in AI infra.
how would you compare Inferless with alternatives like Modal, Banana, or Replicate? Particularly around cold starts, GPU utilization, and pricing transparency? 🔍
Also wondering: any plans to support fine-tuning or model training in the future, or is the focus staying purely on inference?
Excited to see how you reshape AI deployment! ⚡️
This is a brilliant solution! The ultra - low cold starts and pay - as - use model are great. I'm intrigued. Can it handle complex, large - scale models? And how easy is it to switch between different machine learning frameworks during deployment? Looking forward to trying it.
Huge congrats Aishwarya & team! 🎉 As someone who battled GPU provisioning headaches before, your sub-second cold starts + pay-per-millisecond model is a game-changer! ⚡ The Cleanlab integration case speaks volumes.
Have you considered adding granular model monitoring (like token cost breakdowns per API call)? That could take cost optimization to the next level. Any plans for live model swapping without downtime? So excited to see what's next! 🔥
ML deployment made easy - no cold starts, scalable, and PAYG. Perfect for devs who want to focus on building, not infrastructure headaches. Kudos to the team.
Inferless looks like a promising solution for deploying machine learning models with ease. The focus on ultra-low cold starts and serverless GPUs makes it ideal for scaling from small applications to large-scale deployments. The fact that it offers stress-free production deployment and a pay-as-you-go model is a major plus for developers and businesses looking to optimize costs.
It would be interesting to see how Inferless compares to existing solutions in terms of speed, cost efficiency, and integration with various ML frameworks. If it delivers on its promises, it could be a game-changer for AI deployment! 🚀
