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Overview
Rose Rocket powers high-throughput transportation management software and has recently expanded its use of GenAI to support internal tools and customer-facing features. As their usage of foundation models increased, particularly across OpenAI and AWS Bedrock providers, they encountered new challenges: opaque billing, unpredictable performance, and difficulty evaluating which models were truly delivering the best value.
While the team had visibility into total spend, they lacked insight into token-level efficiency, latency across model providers, and the impact of different AI model choices on both cost and experience.
Challenge
GenAI adoption introduced a new layer of complexity in cost management. Rose Rocket needed to answer critical questions:
Which model providers are offering the best performance-to-cost ratio?
How do usage patterns impact efficiency?
- Are we overpaying for latency or underutilizing our budget?
Like many organizations, they were treating GenAI as a black box, without a clear way to assess whether their investments were justified.
Solution
nOps applied its AI Model Recommendation capability to:
- Benchmark model performance using industry-standard quality scores (like MMLU)
- Analyze input/output token behavior and model response latency
- Compare OpenAI and AWS Bedrock models across over 70 optimization factors
- Recommend model shifts to reduce costs while maintaining (or improving) latency and quality
To provide deeper visibility, nOps evaluated multiple GenAI providers, including OpenAI, AWS Bedrock Claude, DeepSeek, Llama, and Nova. Each model was assessed across cost, latency, and quality dimensions to ensure recommendations reflected the optimal trade-offs for Rose Rocket’s unique workload mix.
Results
Through detailed analysis, nOps identified opportunities for Rose Rocket to reduce GenAI costs and significantly boost performance.
The recommendations include:
- Switching to models that could lower costs by up to 30%
- Reducing latency by as much as 40% across key workloads
- Replacing guesswork with data-informed model selection and performance benchmarking
Business Impact
With nOps’ recommendations in hand, Rose Rocket is positioned to achieve meaningful improvements in both cost efficiency and response time as it moves toward implementation.
They now have a framework for comparing model providers using real performance and cost data, setting them up for measurable improvements in efficiency and scalability when they’re ready to act.
nOps turned uncertainty into a strategic advantage, empowering Rose Rocket to move from a reactive model selection to proactive, data-informed decision-making.
