Google Cloud Platform offers enterprise capabilities in data analytics, machine learning, and Kubernetes that are genuinely differentiated from AWS and Azure — but realizing that value requires architecture expertise and procurement strategy that RLM provides without a GCP sales agenda.
RLM advises enterprises on GCP architecture, BigQuery and analytics strategy, AI/ML platform design, multi-cloud integration, and GCP contract negotiation — independent of Google's commercial interests.
A structured advisory process — from discovery and market evaluation to negotiation and post-deployment optimization — tailored to your specific environment and objectives.
We evaluate which workloads — data analytics, ML training, containerized applications, or full-stack migration — are genuinely best served by GCP's architecture and differentiated capabilities, versus workloads better suited to AWS or Azure.
BigQuery is one of GCP's strongest differentiated capabilities. We design the data architecture — ingestion pipelines, Dataflow, BigQuery optimization, and Looker integration — that maximizes analytics value.
Vertex AI, TPUs, and pre-trained Google models offer genuine ML advantages for specific workloads. We evaluate these capabilities against your ML use cases and design the Vertex AI environment.
Committed Use Discounts (CUDs) for compute and spend-based CUDs for all services provide significant savings. We model CUD commitment levels and advise on contract structure.
These are the dimensions that consistently separate successful deployments from costly ones — and the questions RLM will help you answer before any commitment.
BigQuery pricing is based on query processing (on-demand) or capacity reservations. Evaluate the right pricing model against your query volume and optimization opportunities through materialized views and partitioning.
Many GCP deployments coexist with AWS or Azure. Evaluate Anthos, Cloud Interconnect, and the governance model for consistent security and cost management across providers.
GCP offers significant ML infrastructure advantages — TPUs, managed Vertex AI pipelines, and pre-trained models. Evaluate whether these capabilities justify workload placement on GCP vs. cloud-agnostic ML platforms.
GCP's global network is a genuine differentiator for latency-sensitive applications. Evaluate Premium vs. Standard network tier tradeoffs and egress cost exposure for your data volumes.
GKE is Google's most mature managed Kubernetes service. Evaluate Autopilot vs. Standard GKE and the operational savings of managed Kubernetes vs. self-managed alternatives.
Sustained use discounts apply automatically; CUDs require commitment. Evaluate your compute usage patterns and the discount available from each mechanism against your budget flexibility.
"RLM helped us rationalize our multi-cloud spend and identify over $1.2M in annual savings. Their approach was methodical and unbiased — exactly what we needed."
"Our migration was stalled for months. RLM came in, assessed the gaps, and helped us select a managed services partner that got us across the finish line in 60 days."
Start with a no-cost conversation with an RLM cloud advisor — vendor neutral, no agenda, just clarity on the right path forward.
Speak to a Cloud Advisor