24/7 monitoring & alerting
CloudWatch, Datadog or the customer's chosen stack — with the alert routing, on-call and escalation that actually catches issues before customers do.
Phoenix Managed Services runs cloud estates day-to-day so the customer team can focus on what only they can do. Monitoring, incident response, patching, backup, DR, security posture and continuous improvement — under SLAs that hold up in a real audit.
The engagement covers AWS-native workloads, SAP on AWS (including RISE), and the multi-cloud reality where Azure and Huawei Cloud sit alongside the AWS estate.
CloudWatch, Datadog or the customer's chosen stack — with the alert routing, on-call and escalation that actually catches issues before customers do.
P1/P2 response with defined SLAs, blameless post-mortems and the improvement loop back into runbooks and infrastructure.
OS patching, security updates, application-layer patching where in scope — scheduled, tested, reported.
Backup policy, off-site retention, DR runbooks and regular tested failover — not just documented, actually rehearsed.
GuardDuty, Security Hub, Config drift, IAM reviews — proactively managed, not just monitored.
Modernization backlog fed from what we see in run, delivered as small change requests — the estate gets better over time, not worse.
Runbook capture, tooling access, on-call rotation set up, SLA baseline agreed.
24/7 run under agreed SLAs — with monthly service review and quarterly business review.
Feedback from run turns into modernization and optimization work — the estate compounds in value over time.
Every engagement lands specific artefacts — not slides.
SLA-backed 24/7 monitoring and incident response.
Patching, backup and DR run as a service.
Security posture actively managed, not just reported.
Monthly and quarterly service reviews with the customer.
Continuous-improvement backlog fed from live operations.
Real Phoenix engagements — measured outcomes at MEA scale.
Whether you're scoping an SAP move to cloud, restarting a stalled programme, or just trying to figure out where data and AI fit — start with a conversation.