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GenAI Model Assessment — Migrate from 3rd Party Model Providers to Amazon Bedrock
Quickly assess your current LLM usage and get recommendations for the most effective foundation models on Amazon Bedrock with nOps’ migration evaluator
Supported models
100% Visibility into AI Spending
Save up to 80% on GenAI
Compare quality and performance impact of switching LLMs
See GenAI Model Assessment in Action
How To Get Started
Deliverables
Identify candidates & business cases for model migration
Provide recommendations, guidance & benchmarking for GenAI savings
Deliver a migration roadmap to cut AI costs while ensuring quality & performance
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Frequently Asked Questions
What is the GenAI Model Assessment?
The GenAI Model Assessment is an nOps service that evaluates your current large language model (LLM) usage via third party provders and provides cost, quality, and performance comparisons with Amazon Bedrock models. It delivers a clear migration roadmap to cut costs and maintain quality.
How does the GenAI Cost Optimization assessment work?
We connect to your existing LLM usage data, analyze input and output tokens, batch vs. cached usage, and cost by project or API. Using this data, nOps benchmarks model quality with MMLU scores and recommends the most cost-effective Bedrock alternatives for your workloads.
What do I get from the GenAI Cost Optimization assessment?
You’ll receive:
- A full breakdown of GenAI spend and usage patterns
- Benchmarking of cost, speed, and accuracy across supported models
- Recommendations for migrating to Amazon Bedrock, including savings potential
- A step-by-step migration roadmap tailored to your workloads
Which models are supported?
Currently, the assessment supports usage data from OpenAI, Llama, DeepSeek, and Amazon Bedrock, with additional model providers rapidly being added.
How much can I save by migrating?
Organizations typically save up to 80% on GenAI costs when switching to the most cost-efficient Bedrock models, depending on their workloads and usage patterns.
How does nOps compare model quality?
We leverage industry-standard benchmarks such as MMLU to measure model accuracy and performance. This ensures recommendations balance cost efficiency with model quality, speed, and latency.
Do I need to commit to migration after the assessment?
No. The assessment is designed to give you full visibility and actionable recommendations. You can decide whether and when to migrate based on the results.
Who should use the GenAI Model Assessment?
- FinOps teams that need cost accountability and allocation
- Engineering leaders looking for performance-per-dollar efficiency
- Finance & executives seeking predictable, transparent AI spend
- AWS customers looking to get additional value out of their AI spend.
What are the benefits of cost optimization for GenAI on AWS?
Optimize costs of generative AI on AWS helps with reducing AWS GenAI inference costs, tying spending to the right teams or projects, forecasting future demand, and ensuring AWS GenAI billing optimization—cutting costs while maintaining performance.
How long does it take to complete the assessment?
Most assessments can be completed quickly—often within a few days—depending on the size and complexity of your AI workloads. All the lifting is done on the nOps end, with a quick ~5 minute setup and brief meeting to review results on your end.
How do I get started?
Simply schedule a demo with our AWS experts. We’ll guide you through connecting your usage data and provide your migration assessment and roadmap.
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