Cut Anthropic model spend by switching to equivalent Nova, Llama, OpenAI OSS, or DeepSeek models—now with projected cost savings, speed (TPS), latency, and quality (MMLU) deltas shown side-by-side.

Modern AI teams juggle accuracy, speed, and cost—usually across multiple providers with evolving SKUs and price sheets. Knowing what to switch to and what you gain or lose (beyond price) is hard, especially at scale.

We built Anthropic Migration Recommendations to make this simple and safe: nOps analyzes your Anthropic usage and recommends the best-fit alternative on AWS Bedrock Nova, Bedrock Llama, OpenAI OSS, or DeepSeek—complete with estimated monthly savings and expected changes in throughput, latency, quality, and use case recommendations.

What's New

We’re excited to unveil Anthropic Migration Recommendations in nOps (under Inform → Cost Saving Recommendations → AI Model Provider Recommendations). 

Starting today, you can access migration suggestions that translate your Anthropic usage into concrete actions—without spreadsheets, guesswork, or manual price lookups.

One-click, price-aware model mapping

For each Anthropic model you use (e.g., claude-4.5-sonnet, claude-3.7-sonnet, claude-3.5-haiku), nOps proposes the best alternatives across AWS Bedrock Nova, Llama, OpenAI OSS, and DeepSeek. Compare monthly cost, total savings, and percentage saved side-by-side, based on your real token consumption.

Speed, latency, and quality tradeoff visibility

Every recommendation includes expected TPS (throughput), latency (s), and quality (MMLU) deltas versus your current Anthropic model.

  • Speed (TPS): Estimated tokens/sec output speed relative to Anthropic usage.
  • Latency (s): Typical end-to-end response latency comparison.
  • Quality: Relative score from the popular MMLU benchmark.
  • Use Case: Relative score for the selected use case benchmark from ProLLM.
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Who Benefits Most

FinOps Leaders

AI/ML Platform & MLOps Engineers

Engineering Managers / Data Science

Prove ROI quickly with clear estimated monthly savings per model.  Prioritize changes that meet your savings targets while respecting quality/SLA.

Confidently plan migrations with TPS and latency projections.  Reduce integration thrash by targeting providers/models that match your workloads.

Understand quality tradeoffs up front; choose options that preserve evaluation thresholds.  Iterate faster—tests focus on the best candidates, not a long list.

How It Works

  1. Ingest Anthropic usage (Link to documentation for Anthropic integration).
  2. Savings and totals are computed from your actual input/output token usage.
  3. Cross-map to candidate models on AWS Bedrock: Nova, Llama, OpenAI OSS, and DeepSeek.
  4. Generate estimated pricing using the recommended model cost and current token usage.
  5. Recommend the best-fit alternative with estimated cost, savings, and performance/quality changes.

How to Get Started

To start using Anthropic Migration Recommendations, navigate to Inform → Cost Saving Recommendations → AI Model Provider Optimization. 

  1. Review your automatically generated recommendations.
  2. Sort by Cost Optimized, Quality Optimized, or AWS Bedrock Model Provider.

Requirements: Anthropic integration must be enabled for your workspace.

If you're already on nOps...

Have questions about the new feature? Need help getting started? Ping your Customer Success Manager or visit our Help Center. If you’re not sure who your CSM is, send our Support Team a message.

If you’re new to nOps…

nOps was recently ranked #1 with five stars in G2’s cloud cost management category, and we optimize $2+ billion in cloud spend for our customers.

Join our customers using nOps to understand your cloud costs and leverage automation with complete confidence by booking a demo with one of our AWS experts.