AWS AI Consulting · AWS Advanced Tier Partner

Master Your LLM Stack

Not sure if Claude, Amazon Nova, or OpenAI is right for your workload? We run the numbers. DataMax benchmarks every major LLM on AWS Bedrock across latency, accuracy, output quality, and cost and hands you a migration roadmap you can act on in days, not quarters.

Book a Free Discovery Call

Trusted By

Your GenAI Costs Are Climbing. Your Model Choice Is Holding You Back.

Most enterprises are running the wrong LLM for at least one of their use cases — and paying a premium for it. With dozens of models now available on AWS Bedrock alone (Amazon Nova, Claude, Llama, Mistral) plus external options like OpenAI GPT5, the model selection decision has become one of the most consequential, and least data-driven, choices in your AI strategy.

No Objective Data Teams pick LLMs based on hype, demos, or familiarity, not measured performance on their actual workloads.

Runaway Spend Premium models like GPT-5 or Claude Sonnet are often invoked where Nova Micro or Haiku would deliver identical quality at a fraction of the cost.

No Clear Path Forward Even teams that suspect they're overpaying have no structured process to validate alternatives and migrate with confidence.

Introducing the DataMax LLM Benchmarking & Cost Optimization Suite

A 6-week, evidence-based engagement that tells you exactly which LLM to use, why, and how to get there.

Batch & Runtime Evaluation

We run your real prompt datasets, not generic benchmarks, through every model on your shortlist. Batch mode covers your full dataset at scale. Runtime mode mirrors production traffic to capture true latency under real conditions. Models evaluated include Amazon Nova (Micro, Lite, Pro), Anthropic Claude (Haiku, Sonnet), Amazon Titan, Meta Llama 3, Mistral, and optionally OpenAI GPT.

Comparison Across What Matters

Every model is scored on four dimensions your business cares about: latency (p50/p90/p99 response times), accuracy (ROUGE, BERTScore, task-specific metrics), output quality (coherence, faithfulness, instruction-following), and cost (per-token pricing mapped to your actual usage volumes). Results are visualized in a live Amazon QuickSight or Grafana dashboard.

An Executable Migration Roadmap

The engagement closes with custom developed, prioritized, and effort-estimated roadmap: defining which models to swap, in which order, with projected ROI for each substitution. Staged rollout guidance (shadow mode and A/B testing) ensures you migrate without disrupting production. Your team walks away knowing exactly what to do next.

How it works.

Week 1–2: Discovery & Setup

We assess your AWS environment, run a use case definition workshop, curate representative prompt datasets (PII-free, verified with Amazon Macie), and deploy the benchmarking infrastructure on your own AWS account using CDK or Terraform. Your data never leaves your environment.

Week 3–5: Benchmarking & Analysis

The pipelines run. Every model is evaluated against your datasets. We score latency, accuracy, and output quality, run the cost analysis, and build your dashboard. An interim review at Day 20 keeps you aligned before we finalize.

Week 6: Roadmap & Enablement

We deliver your migration roadmap, present findings to your executive stakeholders, hand over the infrastructure with full documentation, and run a knowledge transfer workshop so your team can re-run benchmarks independently as new models launch on AWS Bedrock.