Sold by: Perimattic ManageStacks
Perimattic reduces AI inference costs by up to 80% through model optimization on AWS. We tune LLMs, ML pipelines, and foundation models using SageMaker and Bedrock.
Overview
Reduce AI Inference Costs by Up to 80% on AWS
Perimattic helps organizations maximize the performance and efficiency of AI and machine learning models running on AWS. Our AI Model Optimization Services reduce inference costs by up to 80% without sacrificing accuracy, while improving latency, infrastructure utilization, and operational reliability for production AI applications built with Amazon Bedrock, Amazon SageMaker, and other AWS AI services.
We apply proven techniques including quantization, pruning, and knowledge distillation to compress and accelerate models across the full AI lifecycle - from data preparation and training through deployment, monitoring, and continuous improvement.
Who We Serve
Our services are designed for ML engineering teams, data science leaders, and platform engineers at organizations already running production inference workloads at scale. Whether you are deploying large language models (LLMs), computer vision systems, recommendation engines, or predictive analytics models, our engineers optimize every stage to deliver measurable cost and performance gains.
Phased Engagement Process
Phase 1: Assessment and Benchmarking (1-2 weeks) We evaluate your current model architecture, inference pipeline, and infrastructure utilization. Deliverables include a comprehensive benchmarking report with baseline metrics and an optimization roadmap with projected cost and latency improvements.
Phase 2: Optimization Implementation (2-6 weeks) Our engineers execute targeted optimizations including model compression, quantization, hyperparameter tuning, prompt engineering, inference acceleration, and GPU resource optimization. Deliverables include optimized model artifacts, updated deployment configurations, and performance validation results.
Phase 3: Validation, Handoff, and Monitoring (1-2 weeks) We validate improvements against baseline benchmarks, configure continuous monitoring dashboards, and hand off documentation and runbooks to your team. Deliverables include a final optimization report, monitoring setup, and knowledge transfer sessions.
Core Services LLM Optimization and Prompt Engineering - Reduce token costs and improve response quality for generative AI applications on Amazon Bedrock Model Compression and Quantization - Shrink model size and accelerate inference through pruning, quantization, and knowledge distillation Inference Acceleration - Optimize endpoint configurations, batching strategies, and hardware utilization on Amazon SageMaker GPU and Infrastructure Optimization - Right-size compute resources and reduce idle capacity costs MLOps Pipeline Optimization - Streamline training, deployment, and retraining workflows for faster iteration Continuous Model Monitoring - Detect drift, latency degradation, and cost anomalies in production
Prerequisites and Scope
To engage with our optimization services, buyers should have: An active AWS account with existing AI/ML workloads in production or pre-production At least one deployed model or inference endpoint (SageMaker, Bedrock, or custom) A designated technical point of contact on the buyer's team
Out of scope: raw data labeling, custom model training from scratch (without an existing baseline), and non-AWS cloud environments.
Why Perimattic
Perimattic enables organizations to deploy faster, more accurate, and cost-efficient AI solutions on AWS while maintaining reliability, scalability, and governance throughout the model lifecycle. Our optimization approach has delivered up to 80% inference cost reduction for production workloads through targeted application of quantization, pruning, and knowledge distillation techniques.
Get started with a free AI optimization assessment to identify your highest-impact optimization opportunities.
Highlights
- Reduce AI inference costs by up to 80% without sacrificing model accuracy. Perimattic applies quantization, pruning, and knowledge distillation techniques to compress and accelerate production models on Amazon SageMaker and Amazon Bedrock. Our optimization approach targets the highest-cost components of your inference pipeline first, delivering measurable savings within weeks rather than months.
- Structured three-phase engagement with clear deliverables: Phase 1 benchmarks your current models and produces an optimization roadmap with projected savings. Phase 2 implements targeted optimizations including model compression, prompt engineering, and GPU right-sizing. Phase 3 validates results against baselines, configures monitoring dashboards, and hands off documentation to your team.
- Deep AWS AI and ML specialization across Amazon SageMaker endpoint optimization, Amazon Bedrock performance tuning, and production MLOps pipelines. We serve ML engineering teams and data science leaders at organizations running inference workloads at scale, helping them reduce latency, eliminate idle compute costs, and maintain model quality through continuous monitoring and drift detection.
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Engagement and Support
Perimattic provides consulting, implementation, optimization, and managed support for AI Model Optimization projects.
Getting Started
Contact our team at sales@perimattic.com to schedule an initial AI optimization assessment. During this assessment, we evaluate your current model architecture, identify high-impact optimization opportunities, and provide a preliminary cost savings estimate.
What You Need to Provide AWS account access (via federated roles or temporary credentials as agreed during scoping) Access to model artifacts and inference endpoints to be optimized A designated technical point of contact from your team Baseline performance metrics if available
Engagement Deliverables
Depending on engagement scope, deliverables include: benchmarking reports, optimized model artifacts, updated deployment configurations, monitoring dashboards, performance validation results, optimization documentation, and knowledge transfer sessions.
Ongoing Support
Support Hours: Monday through Friday (Business Hours) with optional 24x7 enterprise support.
Support covers: AI architecture consulting, model performance optimization, LLM tuning, SageMaker implementation, Amazon Bedrock optimization, inference optimization, MLOps consulting, production monitoring, performance troubleshooting, and managed AI operations.
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