AI Cost Visibility & Optimization Understand, allocate & reduce your AI costs - Learn More
Quickly assess your current LLM usage and get recommendations for the most effective foundation models on Amazon Bedrock with nOps’ migration evaluator
View cost data, cost by project, cost by API, and other key business metrics. Get instant visibility into input tokens, output tokens, batch vs cached usage, and other relevant dimensions.
Recommendations for the most cost-effective Bedrock model based on your usage data
Quality scores based on well-recognized MMLU benchmarks make it easy to compare models based on cost, accuracy, speed & latency
See where your AI spend goes and how much you could save on Amazon Bedrock.
“Partnering with nOps made it easy and seamless to optimize our AWS costs. The platform's automated cost optimization and commitment management reduced our AWS spend significantly, allowing us the time to focus on building and innovating. The flexibility nOps offers means we can stay agile and efficient without the risk of long-term commitments.”
Matt Morgan
Head of Engineering & Product, US, CommentSold
“nOps has transformed how we manage our AWS infrastructure. We considered several solutions, including another Reserved Instance solution, and then decided to work with nOps because it was a more complete solution. It gave us automated commitment management and an effortless way to track and monitor our costs with Business Contexts. We have seen a significant reduction in costs and highly recommend nOps to any organization looking to optimize their cloud costs and improve operational efficiency.”
Herman Lotter
Technology Operations Manager, Kurtosys
“As a FinOps lead at Arlo, I view cost transparency as non-negotiable, and nOps has delivered exactly that. The platform gave us end-to-end visibility into our cloud spend, eliminating guesswork and surfacing the true cost drivers across our environment. What stands out is how quickly the insights translate into action. nOps doesn’t just present data, it highlights the specific optimizations, anomalies, and inefficiencies we can address immediately. Those recommendations have driven real cost reductions for us while strengthening accountability across engineering teams.”
Alex Kuan
FinOps Lead, Arlo
“nOps provided the visibility and control we needed to optimize our AWS costs without long-term commitments. I highly recommend nOps for cloud optimization and managing cloud costs effectively.”
Andy Kwan
Senior Director of Infrastructure, BENlabs
“nOps has been invaluable in helping manage the intricacies of optimizing AWS environments and costs in today’s dynamic environment. The nOps Commitment Manager program provides us with the agility to progress and innovate, without the typical constraints associated with long-term commitments. The platform also gives us a clear and transparent picture of our costs, so we can plan and rightsize our resources accordingly.”
Khaled BEN JANNET
Datacenter & Cloud Team Leader, Vermeg
Schedule a demo with our AWS experts to see how to optimize your AWS cloud
Advanced technology partner AWS, G2 4.8 rating, FinOps Foundation member and many more
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.
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.
You'll receive:
Currently, the assessment supports usage data from OpenAI, Llama, DeepSeek, and Amazon Bedrock, with additional model providers rapidly being added.
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.
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.
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.
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.
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.
Simply schedule a demo with our AWS experts. We'll guide you through connecting your usage data and provide your migration assessment and roadmap.
Schedule a demo with one of our AWS experts to get 100% visibility into GenAI costs
