VentureDive

Senior DevOps Engineer

QisstPay

DevOps Engineer

ShopDev

Khired Networks

DevOps Engineer | AWS

I am a dedicated DevOps engineer with over 3 years of experience in designing and implementing robust infrastructure solutions. My passion for automation, continuous improvement, and a strong work ethic drive me to streamline processes and deliver high-quality results. I thrive in dynamic environments, collaborating across teams to optimize workflows and enhance system reliability

LLM Cost Hack: We Cut Token Use by 62% in 4 Hours.

TL;DR: Stop paying for JSON punctuation. Our data extraction pipeline switched to TOON (Token-Oriented Object Notation) and immediately saved $1K/month, reduced tokens by 62%, and cut inference latency by 12%.

The Problem: Paying for Punctuation
LLM pricing is based on token count, and JSON formatting is the silent cost sink. Over half the tokens in structured output are often wasted on syntax like braces, quotes, and commas.

The Solution: TOON eliminates this syntactic overhead by declaring the schema once and using newlines/indentation instead of heavy JSON punctuation, resulting in a 62% token reduction for the same data payload.

If you run high-volume LLM pipelines, test TOON immediately.

What's the biggest cost sink in your current LLM pipeline? Have you tried non-JSON formats like TOON or YAML?

#Python #LLMOps #GenerativeAI #DataExtraction #TOON #MachineLearning #TokenOptimization #FinTech #TokenEconomy #AlyzomSolutions

Hasan Butt

Founder & CTO @Alyzom Solutions

Google just stepped into deeper document intelligence but how does it really compare to Azure’s long-established ecosystem?

From what I’ve explored so far:

	•	Azure still feels ahead in handling messy PDFs, legal files, scanned tables, forms that aren’t forms anymore. Its Document Intelligence stack extracts structure with surprising accuracy and lets you fine-tune almost everything.
	•	Google’s new search brings massive potential. With its foundation models and layout understanding, it’s clearly aiming for more semantic, “understand-the-document-like-a-human” search.

Where Azure gives you control and maturity, Google promises speed and intelligence out-of-the-box.

Two very different philosophies — both powerful, depending on what you’re building.

If you had to choose today for a real-world RAG or document search workload… which direction are you leaning?

#GoogleCloud #Azure

When high-powered GPUs sit idle, AWS bills can escalate quickly, halting experimentation. A recent challenge faced by a team highlighted this issue: AWS GPU utilization was stuck at 20% despite using powerful instances, leading to slow training and rising costs.

The solution involved not just acquiring more hardware, but implementing smarter orchestration and cost control.

Step 1: Identify hidden inefficiencies  
Using AWS CloudWatch, Compute Optimizer, and NVIDIA Nsight, the team uncovered bottlenecks including idle GPUs, slow data pipelines, and inefficient job scheduling. The GPUs weren't underperforming; the pipeline was the issue.

Step 2: Implement smarter Kubernetes orchestration  
The team migrated training to EKS with GPU-aware scheduling, utilizing Cluster Autoscaler and Karpenter for automatic node scaling. This approach allowed GPUs to spin up only when needed and scale down when idle.

Step 3: Reduce costs without compromising quality  
By switching interruptible jobs to EC2 G4dn and G5 Spot instances, significant savings were achieved without risk. They also enabled NVIDIA MIG to partition GPUs into smaller segments, enhancing multi-tenant efficiency.

Step 4: Accelerate model training  
The adoption of mixed precision training using AWS Deep Learning Containers reduced compute time while maintaining accuracy.

The results? GPU utilization increased to over 75%, training times improved, and AWS bills became manageable. All SLAs regarding latency, uptime, and reliability were met—without simply adding more hardware.

This serves as a reminder that in MLOps, performance is not solely about scale; it requires strategy, right-sizing, and intelligent scheduling.

What strategies do you use to balance performance and cost in GPU-heavy ML workloads? Let's share insights and learn from one another. 

#MLOps #AWS #GPUOptimization #Kubernetes #NVIDIA #SRE #SLAs #TechLeadership #MachineLearning #AI #CloudComputing #CostOptimization #EC2

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