Pipeshift is the "Vercel for open-source LLMs", offering a platform for finetuning, distilling, and inferencing open-source LLMs for engineering teams to get to production with their LLMs 10x faster. With Pipeshift, companies making >1000 calls/day on frontier LLMs can use their data and logs to replace GPT/Claude in production with specialized LLMs that offer higher accuracy, lower latencies, and model ownership.

We are experts in LLMs, having scaled LLMs to 1000s of users in 2023. That's when we saw the massive drawbacks of closed-source LLMs in production, making us start Pipeshift AI. We met 6 years back as roommates during undergrad, and before starting Pipeshift AI, we led a defense robotics non-profit backed by NVIDIA and built a health-tech startup.

The shift to AI is like the shift to the cloud, every company is going to implement AI. And, open-source AI will be as good as closed-source AI. Meta's Llama 3.1 models prove that. But, the open-source AI stack is a complete mess, with companies needing a team of engineers to set up 10+ different tools just to get started and every optimization needs 1000s of engineering hours.

Pipeshift offers this stack out of the box. With our fine-tuning + distilling platform and one-click deployment stack for hosting these LLMs, we ensure 10x faster experimentation cycles and time-to-production. Think "Vercel for open-source LLMs".
Platform for Fine-tuning, Distilling and Inferencing Open-source LLMs.
Title: Pipeshift: Platform for Fine-tuning and Inferencing Open-source LLMs. | Y Combinator

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### Platform for Fine-tuning and Inferencing Open-source LLMs.

Pipeshift is the "Vercel for open-source LLMs", offering a platform for finetuning, distilling, and inferencing open-source LLMs for engineering teams to get to production with their LLMs 10x faster. With Pipeshift, companies making \>1000 calls/day on frontier LLMs can use their data and logs to replace GPT/Claude in production with specialized LLMs that offer higher accuracy, lower latencies, and model ownership. We are experts in LLMs, having scaled LLMs to 1000s of users in 2023. That's when we saw the massive drawbacks of closed-source LLMs in production, making us start Pipeshift. We met 6 years back as roommates during undergrad, and before starting Pipeshift, we led a defense robotics non-profit backed by NVIDIA and built a health-tech startup. The shift to AI is like the shift to the cloud, every company is going to implement AI. And, open-source AI will be as good as closed-source AI. Meta's Llama 3.1 models prove that. But, the open-source AI stack is a complete mess, with companies needing a team of engineers to set up 10+ different tools just to get started and every optimization needs countless engineering hours. Pipeshift offers this stack out of the box. With our fine-tuning + distilling platform and one-click deployment stack for hosting these LLMs, we ensure 10x faster experimentation cycles and time-to-production. Think "Vercel for open-source LLMs".



Pipeshift

Founded:2024

Team Size:8

Location:San Francisco

### Active Founders

### Arko C, Founder

CEO @ Pipeshift AI. Helping developers fine-tune and deploy LLMs 10x faster.



### Enrique Ferrao, Founder

CTO @ Pipeshift. Focused on squeezing out max LLM performance from GPUs



### Pranav Reddy, Founder

Making LLMs go brrrr at Pipeshift



### Company Launches

[### Pipeshift AI - Fine-tuning and inference for open-source LLMs](

**_TL;DR:_** Pipeshift is the cloud platform for finetuning and inferencing open-source LLMs, helping teams get to production with their LLMs faster than ever. With Pipeshift, companies making \>1000 calls/day on frontier LLMs can use their data and logs to replace GPT/Claude with specialized LLMs that offer higher accuracy, lower latencies, and model ownership. [_Connect with us._](



**🧨 The Problem: Building with Open-source LLMs is hard!**
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The open-source AI stack is missing, forcing most teams to experiment by **duct-taping things like TGI/vLLM but having nothing ready for production.** As you scale, it requires expensive ML talent, long build cycles, and constant optimizations.

The **gap between open-source and closed-source models is shrinking** (Meta's Llama 3.1 405B is a testament to that)! And open-source LLMs offer multiple benefits over their closed-source counterparts:

🔏 Model ownership and IP control  
🎯 Verticalization and customizability  
🏎️ Improved inference speeds and latency  
💰 Reduction of API costs at scale

**🎉 The Solution: Heroku/Vercel for Open-source LLMs**
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Pipeshift is the **cloud platform for fine-tuning and inferencing open-source LLMs**, helping developers get to production with their LLMs faster than ever.

**🎯 Fine-tune Specialized LLMs**  
Run multiple LoRA-based fine-tuning jobs to build specialized LLMs.

**⚡️ Serverless APIs of Base and Fine-tuned LLMs**  
Run inference for your fine-tuned LLMs and pay as per your token usage.

**🏎️ Dedicated Instances for High Speed and Low Latency**  
Use our optimised inference stack to get max throughputs and utilisation on GPUs.

[Product Demo: 

_Our inference stack is one of the best globally, hitting **150+ tokens/sec on 70B parameter LLMs** without any model quantization. And, since our private beta access was opened (<2 weeks back), we have already seen **25+ LLMs being fine-tuned with over 1.8B tokens in training data** across 15+ companies._



**👋 Ask: How you can help**
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If you’re building an AI co-pilot/agent/SaaS product and **are looking to move to open-source LLMs** or know someone who’s looking to do that same, then [book a call]( or mail us at [_founders@pipeshift.ai_](mailto:founders@pipeshift.ai) \- whichever you’d like!
