BetterPic Reduces GPU Costs By 50% and Accelerates AI Inference Speed

Table of Contents

BetterPic company logo

Our GPU pipeline was one of the biggest cost drivers in our stack. Automat-it helped us optimize it, improving latency, reliability, and consolidating everything under one infrastructure.

About BetterPic

 

BetterPic is a leading generative AI platform for professional headshots. Users upload casual photos, and within minutes, receive high-quality, studio-grade portraits, ready for resumes, LinkedIn, or corporate directories. Behind the scenes, BetterPic runs a multi-step AI pipeline combining input evaluation, preprocessing, model training, inference, and postprocessing.

The Challenge

 

As demand for AI headshots and real-time editing tools grew, so did the pressure on BetterPic’s GPU infrastructure, especially during model training and inference, the two most GPU-intensive steps.

BetterPic’s key bottlenecks came down to three GPU-related issues:

  • High GPU costs: Training and inference were the most expensive parts of BetterPic’s pipeline, representing the biggest share of its COGS. The startup needed to optimize them to free up budget for growth and key hires, critical at the stage of the company.
  • Cold start delays: On-demand GPU providers had boot times ranging from 2-10 minutes. This was unacceptable for real-time tools like BetterPic’s AI Editor, where users expect results in milliseconds.
  • Limited availability: Peak-hour usage sometimes meant GPUs were not available, causing delivery delays on customer orders.

 

To keep scaling efficiently without compromising performance or UX, BetterPic needed a more reliable, scalable GPU strategy.

The Solution

 

To address these challenges, BetterPic partnered with Automat-it’s DevOps team and AWS to revamp its GPU infrastructure, focusing on cost efficiency, performance, and scalability.

Key components of the solution included:

  • Amazon SageMaker HyperPod to provide stable, dedicated GPU capacity with no cold-boot times. This powered both short model training jobs and real-time inference.
  • Flexible GPU scaling during peak demand periods allowed BetterPic to spin up additional capacity when needed, without overpaying during idle times for a truly cost-efficient solution.
  • Full observability through centralized monitoring tools to track GPU utilization, job durations, and cost drivers, enabling BetterPic to make smarter infrastructure decisions.

 

The Results

 

The result was a lean, scalable setup that keeps latency low and performance high, while optimizing one of BetterPic’s largest cost centres.
Ultimately, BetterPic was able to adapt to shifting GPU availability while keeping growth on track.

Standout results:

  • GPU costs were cut by 50%
  • 0 latency for AI editing and inference pipelines, even at scale
  • Improved GPU availability, eliminating user-facing delays during peak usage
  • Seamless scalability to support growing user demand
  • Simpler infrastructure and more predictable costs

 

Customer Testimonial

 

Miguel Rasero, co-founder & CTO at BetterPic, explained:

« Their DevOps team follows best practices by default, which made it easier to align with SOC2 requirements and scale cleanly. The weekly reports and quick standups gave us full visibility without the overhead. A high-trust partnership that delivers.”

 

Get in Touch

 

Ready to cut costs, boost performance, and scale your AI workloads like BetterPic? Talk to the Automat-it team today and start transforming your infrastructure.

Get started