Now Supporting Bedrock Claude in AI Model Cost Recommendations

Save up to 90 % on LLM spend while maintaining the same quality

nOps AI Model Provider Recommendations give you side-by-side guidance on when to switch AI models — such as from OpenAI to Amazon Bedrock — to reduce costs significantly while maintaining the same performance.

Starting today, recommendations now include the Claude family on Bedrock (Claude 3 Haiku, Claude 3.5 Haiku, Claude 3.5 Sonnet, and Claude 3.7 Sonnet). This allows you to cut costs — often by an order of magnitude — without sacrificing response quality.

In the example below, a conversational service running GPT-4o costs $112,456 per month. Our engine flagged that the same prompt mix fits Claude 3.5 Haiku on Bedrock just as well, bringing projected spend down to $14,721 — a savings of $97,736 (-86.9%).

What's New

nOps automatically detects your OpenAI models and assesses latency, context-window and function-calling needs. It calculates token economics using the latest model provider pricing from Amazon, OpenAI and now Anthropic, to select and recommend the most cost-efficient Bedrock Claude (or other supported model) tier that meets your requirements—no guesswork needed.

Each recommendation includes a clear explanation of the proposed model switch, including pricing, capabilities, and projected savings.

Potential savings from switching to Claude

How to Get Started

To access the new recommendations, log in to nOps. Navigate from Cost Optimization to the AI Model Provider Recommendations dashboard.

If you're already on nOps...

Have questions about AI Model Provider Recommendations? Need help getting started? Our dedicated support team is here for you. Simply reach out to your Customer Success Manager or visit our Help Center. If you’re not sure who your CSM is, send our Support Team an email.

If you’re new to nOps…

Ranked #1 on G2 for cloud cost management and trusted to optimise $2B+ in annual spend, nOps gives you automated GenAI savings with complete confidence. Book a demo to start saving on LLM cost without compromising on performance.