(continued from previous article)

4.  

4. GenAI-heavy Databricks + Langfuse + remote model calls: 

1. Recommended posture: active-passive for the Databricks and observability plane, plus agreed-upon resilience for the model dependency because the model endpoint is often outside my  Azure regional control. Azure’s DR guidance says to automate recovery safely and add safeguards like retries and circuit breakers, which is especially important in GenAI systems where external inference services can fail independently of my client’s own platform. 

2. A good service mapping is: Databricks for prompt orchestration and feature processing, replicated workspace artifacts and jobs, Langfuse deployed with durable storage or replicated telemetry storage, Key Vault for API credentials, and a resilient egress path to the remote model API. Because the application speaks an OpenAI-style chat interface, I preserve prompt templates, tool definitions, model selection rules, moderation logic, and traceability, so that behavioral differences are explainable after failover. If the remote model is degraded or unavailable, the system should have fallbacks such as a cached-response mode, a lower-cost backup model, queued processing, or a limited read-only experience rather than a hard outage. 

3. Recommended RTO/RPO bands: RTO 15–90 minutes and RPO 1–15 minutes for the control-plane and telemetry plane, with the model-call path handled by graceful degradation instead of strict regional failover because the provider may not be under my regional control. If Langfuse data is central to compliance or incident review, I keep its RPO exceptionally low and ensure logs, traces, and prompt versions are not lost during a cutover. 

4. Failover checklist: I verify secondary Databricks workspace and job artifacts; confirm secrets, identities, and storage; validate Langfuse availability or telemetry sink continuity; test outbound network access to the model endpoint; run a small prompt canary, compare outputs, and trace capture; validate fallback behavior for rate limits and outages; and only then re-enable production traffic and scheduled GenAI workflows. 

As usual for all four stamps/workloads, I keep the same architectural discipline: infrastructure as code, identical identity and policy posture in both regions, clear dependency mapping, and written failover/failback criteria that are tested on a schedule. Azure’s DR guidance emphasizes automated recovery, safe orchestration, replication monitoring, and validation of data consistency and service dependency activation sequences, which is exactly the pattern I want across a high-security environment.