Managed Services · Cloud & Data

Managed operations for cloud, data & AI.

24/7 operations across your AWS estate, data platforms and production ML: one accountable partner, three specialised delivery tracks.

Landing on AWS, standing up a lakehouse, and shipping a first ML model are all worth celebrating, and none of them include the 24/7 team that keeps them running through weekends, cost spikes, drift events and audit windows. Phoenix runs that operating layer across three specialised tracks: CloudOps for the AWS estate, DataOps for the lakehouse and pipelines, and MLOps for production models.

The service stays honest through a monthly service review, quarterly platform-health checks (Well-Architected, data-platform audit, MLOps model-health), a continuous-improvement backlog, and a living runbook repository owned jointly with the client.

On-call coverage across CloudOps, DataOps and MLOps: 24/7; On-call coverage across CloudOps, DataOps and MLOps
Specialised tracks: engage independently or together: 3; Specialised tracks: engage independently or together
Advanced Tier Partner: Well-Architected reviews included: AWS; Advanced Tier Partner: Well-Architected reviews included
Accountable delivery lead across every track you engage: 1; Accountable delivery lead across every track you engage

One accountable partner, three specialised delivery tracks.

Tracks are modular. Engage the ones you need today; extend as your estate matures. Each track has its own pager, its own runbooks, and its own SLA matrix, all under a single service lead.

CloudOps · AWS estate operations

24/7 platform ops, cost, resilience and security across your AWS accounts: account & landing-zone operations, incident response with clear P1/P2/P3 SLAs, FinOps, backup & DR drills, security operations (GuardDuty, Security Hub, Config) and Well-Architected reviews on a defined cadence.

DataOps · Lakehouse & pipeline ops

The data platform stays reliable, governed and fast: pipeline reliability (freshness, quality, retry and backfill playbooks), catalog & lineage upkeep (AWS Glue, Lake Formation), data-quality checks, PII masking and row/column ACLs, Redshift/EMR/Athena tuning, and new-source onboarding under a repeatable pattern.

MLOps · Production ML operations

Models in production behave: model registry and versioning, automated retraining (schedule- and drift-triggered), drift monitoring, evaluation against the anchor KPI, GenAI guardrails, and Amazon SageMaker & Bedrock endpoint operations.

How Phoenix structures the engagement.

Same operating team behind three commercial shapes. Actual pricing is confirmed in the scoping workshop against your specific estate.

Tiered flat monthly

Fixed monthly fee per track (CloudOps / DataOps / MLOps), scaled by estate size: accounts, pipelines and models under management. Best for steady-state estates that value budget predictability.

Named-FTE model

Named Phoenix engineers (onshore, offshore or hybrid) embedded per track against agreed roles and rotations. Best for complex or fast-moving estates that need continuous dedicated coverage.

Ops + build blocks

Base run-fee for pager and SLA coverage, plus pre-purchased engineering-hour blocks for backlog work and enhancement. Best for estates in build-out mode where evolution work outpaces steady-state.

Six to eight weeks: pager cover from day one.

Structured transition per engaged track. Day-one pager coverage is maintained throughout via the incumbent-Phoenix overlap.

01

Weeks 1 to 2 · Discovery & inventory

AWS accounts, pipelines, models and SLAs mapped. Runbooks and monitoring gaps identified. Scope and coverage windows fixed per track.
02

Weeks 3 to 4 · Shadow ops

Phoenix engineers shadow the incumbent team on live incidents and changes; alerting and paging integrated to the Phoenix tooling.
03

Weeks 5 to 6 · Parallel run

Phoenix takes primary on incidents under incumbent oversight. SLA dashboards live; escalation paths tested end-to-end.
04

Week 7+ · Full cutover

Phoenix owns the pager per track. Monthly service reviews, quarterly Well-Architected and platform-health checks embedded.

Priority-based response & resolution commitments.

Grouped by criticality, not by track. Coverage windows and RTOs are tuned per contract against your platform posture, operating model and time zones across MEA and the Caspian.

P1 critical (24/7), CloudOps: response under 15 minutes, resolution under 4 hours. Tier-1 AWS workload outage, region-level impairment, security incident.
P1 critical (24/7), DataOps / MLOps: response under 30 minutes, resolution under 8 hours. Broken pipeline blocking a business-critical dashboard, model down for a live decision surface.
P2 high (business hours), CloudOps: response under 2 hours, resolution under 24 hours.
P2 high (business hours), DataOps / MLOps: response under 4 hours, resolution under 48 hours.
P3 medium (business hours), all tracks: response under 8 hours, resolution within 5 business days.
Change requests: response within 2 business days, resolution per change window under CAB.
Evolution backlog: sprint-cadence response for new sources, model iterations, cost optimisations and platform hardening.

The wider managed-services practice.

SAP Application Management (AMS)

Helpdesk (L1) through Managed Operations (L4) for the SAP estate.

Managed services overview

Two tracks, one run team: how the managed-services practice is structured.

Data & AI practice

Enterprise data platforms, document AI, GenAI copilots and applied industry AI.

Cloud & Data AMS · Frequently asked

Do we have to engage all three tracks?

No. CloudOps, DataOps and MLOps are independently engageable: start with the one that carries the most operational risk today and extend as your estate matures. Coverage windows and commercial model are set per track.

How does this differ from SAP AMS?

SAP AMS is application-management around the SAP estate: helpdesk, functional support, technical Basis/HANA, and (optionally) L4 Managed Operations. Cloud & Data AMS is platform-and-model operations around AWS, the lakehouse and production ML. Same 24/7 delivery model, different domains.

What does MLOps actually cover?

Everything after a model is in production: registry and versioning, automated retraining, drift monitoring, evaluation against the anchor KPI, roll-back procedures, GenAI guardrails, and Amazon SageMaker / Bedrock endpoint operations. Model build stays in the Data & AI practice; this is the runtime team.

Is coverage AWS-only or does it include other clouds?

CloudOps is AWS-first: that's where the deep partnership sits. Azure or GCP workloads can be covered under a scoped agreement where a customer standard requires it, but the runbooks and accelerators are AWS-native.

How long is onboarding?

6 to 8 weeks per engaged track: discovery, shadow, parallel run, cutover. Pager coverage handed over from day one of parallel run so there's no gap in operational cover.

The earliest conversations are usually the most useful.

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.

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