The default assumption in 2026 is that everything lives in the cloud. AWS or GCP, pick your managed services, and ship. For a certain class of workload, that’s the right call. For others, it’s an expensive mistake that takes 18 months to fully recognize.
I’ve run infrastructure across all three environments — and often across combinations of them simultaneously, as we did at Figment where blockchain validator infrastructure required bare-metal performance at scale alongside cloud-based management tooling. The decision framework I use starts with workload characteristics, not vendor marketing.
Start With Workload Requirements, Not Platform Preferences
Platform selection before you understand the workload is how organizations end up locked into the wrong environment. Before evaluating any provider, answer these questions:
What are the latency requirements? Cloud compute adds network hops that bare-metal doesn’t. For most web applications, this is irrelevant. For low-latency trading systems, game servers, or real-time data processing, it matters enormously. At Cube Exchange, we ran crypto exchange infrastructure where single-digit millisecond differences in order execution mattered — that workload profile eliminates most managed cloud options before you start.
What’s the cost profile over 3 years? Cloud compute is almost always more expensive per compute-hour than equivalent bare-metal at sustained load. The economics flip when you factor in flexibility, management overhead, and utilization rates. If you’re running at 30% average utilization and spiky, cloud wins on cost. If you’re running at 80%+ sustained load, the math tilts toward bare-metal or reserved instances significantly ahead of on-demand pricing.
How much operational overhead can you absorb? Bare-metal gives you complete control and lowest cost at scale. It also gives you the full operational burden: hardware failure handling, firmware management, data center relationships, capacity planning. Cloud abstracts all of that. The abstraction has a price, and it’s worth paying until you have the operational team to justify not paying it.
AWS: The Default for Good Reason
AWS is where most organizations should start, and where many should stay. The service breadth is unmatched, the tooling ecosystem is mature, and the operational model is well-understood. More importantly, the hiring market is AWS-heavy — your future infrastructure engineers will likely know AWS better than any alternative.
AWS strengths that are genuinely differentiated:
- Managed services depth. RDS, EKS, Lambda, SQS — the managed service ecosystem reduces operational overhead for teams that want to focus on application development rather than infrastructure management.
- Global footprint. If you need regions across multiple continents, AWS has more options than any alternative.
- Enterprise features. Organizations with compliance requirements (HIPAA, FedRAMP, SOC 2) have well-trodden paths on AWS. The audit trails, access controls, and certification documentation exist and are battle-tested.
Where AWS consistently disappoints: raw compute cost at scale, egress fees (the tax you pay to get data out), and the complexity of cost management. AWS costs creep upward in ways that are hard to predict and harder to attribute. We’ve helped clients find 30-40% savings by auditing running AWS costs — savings that were invisible because the billing dashboard doesn’t make the waste obvious.
GCP: Better in Specific Scenarios
GCP is AWS’s main competition for cloud-first workloads, and in specific scenarios it genuinely wins. If your organization is already deep in Google Workspace, the identity and access model integrates more naturally. If you’re running ML/AI workloads, GCP’s TPU access and the ecosystem around Vertex AI and BigQuery is legitimately competitive with and often better than AWS.
GCP also has a reputation for cleaner networking — the private global backbone rather than the more complex VPC peering model of AWS — which matters at scale.
The challenge with GCP: smaller ecosystem, less tooling depth in operational areas, and a history of product discontinuation that makes some teams nervous about long-term commitment. If AWS had Google’s cancellation track record, the market would have punished it far more. The concern is real for niche services, but core compute and networking infrastructure is stable.
For data-heavy workloads — especially analytics — BigQuery is genuinely excellent and worth evaluating seriously even if your primary cloud is AWS.
Bare-Metal: When the Math Works
Bare-metal makes economic sense at sustained scale, for latency-sensitive workloads, and when you have the operational team to run it. The classic use case: a company spending $80k/month on AWS compute, running at 70%+ sustained utilization, with an infrastructure team capable of managing physical servers. Moving to bare-metal hardware at providers like Hetzner, OVHcloud, or Equinix often cuts that compute bill by 60-70%. The savings fund the operational overhead and then some.
At Figment, we ran Kubernetes across 1,300+ servers spread across 13 providers — a combination of bare-metal and cloud. The validator infrastructure ran on bare-metal because the cost economics at that scale made cloud unsustainable, and because the latency requirements for blockchain validation rewarded low-latency hardware. Management plane and orchestration ran on cloud because the operational flexibility was worth the cost there.
The model that works is hybrid: bare-metal for sustained, predictable load where you have operational capacity; cloud for bursty, variable, or geographically distributed load.
The Multi-Cloud Question
Running workloads across multiple cloud providers adds complexity that’s rarely worth it purely for redundancy — cloud providers rarely have correlated failures at the region level, and the operational overhead of maintaining consistent deployments across providers is significant.
Where multi-cloud genuinely makes sense: when different providers have non-overlapping strengths you actually need. Using GCP BigQuery for analytics while running primary compute on AWS is a legitimate pattern. Running production on AWS while using GCP for ML training pipelines is another. The anti-pattern is running the same workload across multiple providers “for redundancy” without the operational discipline to actually manage that complexity.
Our cloud infrastructure practice has migrated workloads across all three environments, and the honest answer is that the right platform depends entirely on factors that require a real assessment — not a vendor comparison matrix. The good news: the infrastructure patterns that work in one environment translate reasonably well. The Terraform and Kubernetes knowledge transfers. The specific service knowledge doesn’t.
If you’re evaluating platforms for a new workload or considering a migration, the place to start isn’t vendor pricing calculators — it’s a clear understanding of what the workload actually needs. Get that wrong, and no amount of optimization on the platform side will fix it.
Related: if cost management on an existing cloud environment is the immediate problem, our cloud migration and cost optimization practice focuses specifically on finding and eliminating cloud waste.