Unlock Google Cloud's Unique Capabilities — With Independent Guidance
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.
What RLM Delivers on Google Cloud Platform
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.
How We Approach Google Cloud Platform
We work Google Cloud Platform the same way each time: establish the baseline, test the market properly, negotiate on evidence, and stay involved through implementation.
GCP Workload Fit Assessment
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.
Data & Analytics Architecture
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.
AI/ML Platform Design
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.
Contract & CUD Optimization
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.
Google Cloud Platform Evaluation Criteria
These are the dimensions we have seen separate a Google Cloud Platform deployment that works from one that quietly becomes shelfware.
BigQuery Architecture & Cost
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.
Multi-Cloud Integration
Many GCP deployments coexist with AWS or Azure. Evaluate Anthos, Cloud Interconnect, and the governance model for consistent security and cost management across providers.
Vertex AI vs. Third-Party ML Platforms
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.
Networking & Egress Costs
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.
Kubernetes & GKE Autopilot
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.
CUD & Sustained Use Discounts
Sustained use discounts apply automatically; CUDs require commitment. Evaluate your compute usage patterns and the discount available from each mechanism against your budget flexibility.
"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."
Independent means we will tell you when the answer is to keep what you have.
Where This Matters Most
Sector-specific considerations we see repeatedly in cloud and managed services engagements.
A Sample of the Cloud & Managed Services Providers We Evaluate
RLM is vendor neutral. These are among 600+ providers in our evaluation set — inclusion here is not an endorsement, and we are paid by the provider you choose, not by any provider in particular. How that works →
Where Do You Want to Start With Google Cloud Platform?
Start with a no-cost conversation with an RLM cloud advisor — vendor neutral, no agenda, just clarity on the right path forward.
Talk to a Cloud Advisor