How might one include the cost of operations (CPU, memory usage, or even monetary cost for cloud services) into the evaluation, rather than just raw speed and accuracy metrics?

To integrate operational costs like CPU, memory, or cloud expenses into system evaluation, you need to measure resource consumption during execution and map it to tangible costs. Start by instrumenting your code or infrastructure to track metrics such as CPU time, memory allocation, network bandwidth, and storage I/O. For cloud services, use provider-specific billing APIs or monitoring tools (like AWS CloudWatch or Google Cloud Monitoring) to capture usage data tied to pricing models (e.g., per vCPU-hour or GB of memory). Combine these metrics with your system’s performance data (latency, throughput) to create a cost-performance profile. For example, a machine learning model might process 1,000 inferences per hour at $0.10 per inference on a cloud VM—tracking both accuracy and cost per inference gives a clearer picture of tradeoffs.

Next, establish a framework to convert raw metrics into monetary terms. Cloud costs are often tiered or usage-based, so calculate expenses using formulas like (compute hours × instance price) + (memory GB × memory cost) + data transfer fees. For on-prem systems, estimate hardware depreciation, power consumption, or maintenance costs. Tools like Prometheus or OpenTelemetry can collect resource data, which you can then feed into a cost calculator. For example, a serverless function using AWS Lambda might cost $0.00001667 per GB-s of memory used, plus execution time—tracking peak memory and execution duration lets you model total cost. Similarly, a high-memory algorithm that reduces runtime by 50% but doubles memory usage might not save money if memory costs outweigh time savings.

Finally, balance cost against other metrics by defining key performance indicators (KPIs) that reflect both efficiency and economics. For instance, a “cost per transaction” metric could combine cloud expenses and processing time, helping compare architectures. If a caching system reduces database calls by 30% but requires expensive Redis instances, calculate whether the savings on database operations justify the added cache costs. Automated tools like Kubernetes’ Vertical Pod Autoscaler can optimize resource allocation in real time, while load testing with tools like Locust can simulate traffic to predict scaling costs. By iteratively measuring, modeling, and adjusting, you can prioritize optimizations that reduce costs without compromising performance—for example, choosing a cheaper region for non-latency-sensitive tasks or switching to spot instances for batch processing.