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- Now Supporting Bedrock DeepSeek in AI Model Cost Recommendations
Now Supporting Bedrock DeepSeek 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 DeepSeek on Bedrock (DeepSeek R1). 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 $5,270.90 per month. Our engine flagged that the same prompt mix fits DeepSeek R1 on Bedrock just as well, bringing projected spend down to $1,582.77 — a savings of $3,688.33 (-70.00%) with an increase of 13.5% in quality.
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, Claude, Llama and now DeepSeek, to select and recommend the most cost-efficient Bedrock DeepSeek (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.
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 optimize $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.
