Operational Fingerprints Reveal LLM Cloud Service Production Behavior

When selecting and planning for managed Large Language Model (LLM) services in production, organizations typically rely on capability benchmarks. However, these benchmarks offer limited insight into how LLMs perform operationally after deployment, failing to capture real-world issues like stability, latency, or error patterns.Researchers at Google Cloud have developed Operational Embedding (OpEmbed), a novel framework designed to learn "operational fingerprints" of LLM cloud services. OpEmbed analyzes structured, privacy-preserving support-case metadata, rather than case text, to create an eight-channel operational signature for model-time windows. It then uses temporal contrastive learning, cross-view reconstruction, and generational-ordinality regularization to derive a low-dimensional representation of these operational characteristics.Evaluated on over 33,000 production support cases across seven LLM families over 26 months, OpEmbed successfully identifies interpretable family- and version-level structures. It significantly improves operational forecasting compared to non-learned baselines, remains effective even with limited early data, and supports transferring fault-type knowledge across different models. This tool offers practical benefits for model onboarding, assessing support readiness, and continuous operational monitoring.