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Picking a cloud provider feels like it should be a five-minute decision. It is not. You are committing your data, your team’s muscle memory, your vendor contracts, your compliance posture, and a meaningful chunk of your engineering roadmap to a platform that charges more per hour if you make the wrong call — and charges you to leave if you later change your mind.
AWS, Azure, and GCP together control 68% of global enterprise cloud spending as of Q1 2026. That concentration exists for real reasons: depth of services, global infrastructure, enterprise support, and a decade of compounding integrations that are genuinely hard to replicate elsewhere. But it also means that the ‘right choice’ for your specific team is buried inside a three-way comparison that most guides flatten into a feature checklist.
This guide is built for the decision you are actually making: whether to commit to a provider, migrate away from one, add a second cloud to an existing footprint, or simply understand why your bill looks the way it does.
The Market in 2026: Where Each Provider Actually Stands
The market share numbers tell a story that raw percentages obscure. AWS, Azure, and GCP are not three similar products competing on the same dimensions. They are three different bets on what enterprise IT should look like — and they have been diverging, not converging, over the last three years.
AWS holds 30% of global cloud infrastructure spend in Q1 2026. Azure is at 25%. Google Cloud is at 13%. Source: Synergy Research Group Q1 2026, confirmed across quantumrun.com, businesstats.com. The combined 68% share has been stable for two years, but the growth rates underneath are dramatically different: Azure grew cloud revenue 40% year-over-year in its latest quarter. Google Cloud grew 63%. AWS grew 19%. AWS is the biggest and the slowest-growing. GCP is the smallest and the fastest-growing. Azure is gaining ground on AWS in absolute revenue faster than at any point in its history.
AI is driving all of it. AI-related cloud spending hit 19% of total cloud spend in 2026, up from 8% in 2023. Every major provider is repositioning around AI infrastructure — which means their pricing, service depth, and competitive advantages are shifting faster in 2025-2026 than in any prior period.
Also read: The Complete Guide to Compute Savings Plans (AWS and Azure)
AWS in 2026: Still the Default — and the Most Expensive
AWS has 240+ managed services across 33 regions and 105 availability zones as of 2026. No other provider comes close on either service breadth or global footprint. If a cloud pattern exists — message queues, managed Kubernetes, serverless, vector databases, GPU instances — AWS shipped it first and has the most mature managed version.
That maturity comes with a cost premium. AWS is consistently 10-20% more expensive than GCP for equivalent compute on a list-price basis, and more expensive than Azure on storage for most configurations. The premium is real, and it is not a coincidence — AWS charges more because its ecosystem maturity, support infrastructure, and partner network are genuinely more developed. Whether that premium is worth it is a workload-specific question.
What AWS Is Best For
AWS wins on breadth of services, scale of ecosystem, and the startup-to-enterprise pipeline. If you are a startup in Y Combinator or Techstars, you are almost certainly on AWS credits. If you are a large enterprise with a diverse workload spanning 20 different service types, AWS probably already has a managed version of all 20. If your team’s hiring pool is weighted toward engineers with cloud experience, statistically more of them have AWS certifications than Azure or GCP.
AWS also has the best Reserved Instance and Savings Plans infrastructure of the three providers. The combination of EC2 Instance Savings Plans, Compute Savings Plans, and Standard Reserved Instances — all with different flexibility and discount characteristics — gives sophisticated FinOps teams more levers to optimize spend than Azure or GCP offer. The discount ceiling on 3-year RIs (up to 69% for some instance families) is higher than either competitor.
Also read: AWS Savings Plans: A Complete Guide
Real question: What if I move from GCP to AWS — will my costs go up?
Almost certainly yes, on compute list price. AWS is 10-20% more expensive than GCP for equivalent vCPU and memory configurations at on-demand rates. However, AWS’s Reserved Instance discounts are deeper (up to 69% vs GCP’s 52% CUD), so the gap narrows significantly with commitment coverage.
If you are moving from GCP on-demand to AWS with full RI coverage, you may end up paying similar or less than GCP on-demand. The real cost increase from a GCP-to-AWS migration is usually not compute — it is the migration itself (engineering time, data transfer, service re-architecture) and the period of parallel running both environments during cutover.
A 100 TB data migration from GCP to AWS at $0.09/GB in egress would have previously cost $9,200. All three major clouds now waive egress fees for full migrations to another provider when you contact them directly — but the fees are waived as credits, not automatic, and approval processes apply. Source: Forrester analysis of AWS and Google Cloud egress policy changes, TechRadar.
Azure in 2026: The Enterprise Deal Machine
Azure’s growth is not primarily an infrastructure story. It is a deals story. Microsoft has relationships with every major enterprise on Earth through Office 365, Windows, SQL Server, Teams, and Active Directory. When those enterprises evaluate cloud strategy, Azure is not just a cloud provider — it is part of a Microsoft Enterprise Agreement that most of them already have. The bundling creates a procurement advantage that AWS cannot match and GCP is still learning to replicate.
In 2026, that bundling has expanded to AI. Microsoft’s exclusive partnership with OpenAI means Azure is the only cloud where you can run GPT-4o and GPT-5 natively within enterprise services — a significant advantage for enterprises already standardized on Microsoft 365. Azure integrated GPT-5 natively into all enterprise services in Q1 2026, according to Tech Insider (April 2026).
What Azure Is Best For
Azure wins for enterprises that are already deep in the Microsoft stack. If your team runs Active Directory, SQL Server on-premises, SharePoint, Teams, or Power BI, the Azure integrations are native and genuinely better than competing services on AWS or GCP for those specific tools. Azure Arc extends Azure management and policy to on-premises and other clouds, which makes Azure the strongest hybrid cloud story of the three providers for Microsoft-native environments.
Azure also has the best compliance and sovereignty posture for regulated industries in Europe. The combination of Azure Government, Azure Germany (operated by T-Systems as a data trustee), and Azure Europe-specific data residency commitments gives financial services and healthcare companies operating under GDPR and local data residency regulations more out-of-the-box coverage than AWS or GCP.
Also read: AWS Savings Plans vs Reserved Instances: Which Saves More?
Real question: What if I move from AWS to Azure — what actually changes?
Three things change materially. First, your commitment discount structure: AWS uses Reserved Instances and Savings Plans (up to 69% off). Azure uses Reserved VM Instances and Azure Savings Plans for Compute (up to 65% off on 3-year). The mechanics are similar but the specific discount percentages differ by workload. Second, your managed service names and APIs all change. An AWS Lambda function does not migrate to Azure Functions with a copy-paste. The logic migrates; the deployment configuration, IAM model, and event trigger syntax all need to be rewritten. Third, your support and account team changes, which is actually significant for enterprise accounts where the AWS enterprise support relationship is tightly integrated with procurement. Budget 6-18 months of parallel operation and re-architecture time for a non-trivial AWS-to-Azure migration.
GCP in 2026: The AI-First Underdog With a Price Advantage
Google Cloud is the smallest of the three by market share but the fastest-growing by percentage. It has also made the most aggressive pricing moves in 2026: GCP cut compute pricing by 8% across all regions in Q1 2026, according to Tech Insider (April 2026). That cut continues a pattern of GCP pricing below AWS and Azure on compute as a deliberate strategy to close the market share gap.
For AI workloads specifically, GCP remains 5-10% cheaper than AWS and Azure, according to the Tech Insider Q1 2026 analysis. Google’s custom TPU (Tensor Processing Unit) infrastructure and its native TensorFlow and Vertex AI tooling give it a genuine cost-performance edge for AI training and inference workloads that AWS has not fully matched even with its Trainium and Inferentia custom silicon.
What GCP Is Best For
GCP wins on data analytics, AI/ML infrastructure, and teams already invested in Google’s ecosystem (Google Workspace, BigQuery, YouTube, Google Ads). BigQuery is the strongest managed data warehouse of the three providers on a cost-per-query basis for large-scale analytics. If your data strategy centers on BigQuery, GCP is the natural home for the rest of your infrastructure. Splitting your data warehouse and your compute across two clouds creates data transfer costs and operational complexity that typically outweigh any compute savings on the alternative cloud.
For AI-heavy companies building on top of foundation models: Google’s Gemini model access is native within GCP, and the Vertex AI platform has the most integrated MLOps tooling of any cloud. Teams that need GPU-backed training at scale and are cost-sensitive should benchmark GCP’s A3 (H100-backed) instances against AWS p4d and Azure NDv5 before committing to a provider.
Also read: GCP Committed Use Discounts vs AWS and Azure commitments
Real question: What if I am on AWS but want GCP just for AI workloads?
This is the fastest-growing multi-cloud pattern in 2026. AWS for production infrastructure, GCP for AI training and Vertex AI. The main cost to account for is cross-cloud data transfer. If your AI training data lives in S3, moving it to GCS (Google Cloud Storage) for each training run incurs data transfer costs from AWS.
At $0.09/GB for the first 10 TB of S3 egress, a 10 TB training dataset moved monthly costs $900/month in data transfer before any GCP compute. Three options: copy the data to GCS once and keep a replicated version there (ongoing storage cost but eliminates per-run transfer), run inference only on GCP with model artifacts (smaller data volume), or use AWS SageMaker for training and only move to GCP for services that are genuinely better there.
The multi-cloud AI pattern works well when the data volume is modest relative to the compute savings.
Cost Comparison: What You Actually Pay for Equivalent Workloads
Head-to-head pricing comparisons between cloud providers are notoriously unreliable when done at the instance-type level, because no two providers offer identical configurations. What follows is a comparison at the workload level, based on verified rates and real usage patterns.
General-Purpose Compute (Web Servers, Application Backends)
For a 4-vCPU, 16 GB RAM web server instance running on-demand in a US region: AWS m7i.xlarge is approximately $0.2016/hr (~$147/month). Azure D4s v5 equivalent is approximately $0.192/hr (~$140/month). GCP n2-standard-4 is approximately $0.1906/hr (~$139/month). All three are within 5% at list price. With 1-year reservations: AWS 1-yr No Upfront RI saves approximately 33%; Azure 1-yr Reserved VM saves approximately 40%; GCP 1-yr CUD saves 25%. Azure’s deeper 1-year discount makes it cheapest for committed compute in this class. Verify at each provider’s official pricing calculator — rates change.
Database (Managed MySQL)
AWS RDS db.r8g.xlarge Single-AZ on-demand: $0.478/hr ($349/month). GCP Cloud SQL 4-vCPU 15 GB MySQL on-demand: approximately $0.386/hr ($282/month). Azure Database for MySQL Flexible 4-vCPU 20 GB on-demand: verify at azure.microsoft.com/pricing/details/mysql — rates change. GCP Cloud SQL is notably cheaper on database compute for MySQL at list price, but GCP SSD storage at $0.22/GB-month is nearly double AWS RDS gp3 at $0.115/GB-month. For storage-heavy databases, AWS is typically less expensive overall. For compute-heavy databases with modest storage, GCP Cloud SQL often wins on total cost.
AI Training and Inference
GCP A3 instances (NVIDIA H100 80GB) are designed for large-scale AI training and are Google’s flagship AI compute. AWS p4d.24xlarge (8 x A100 80GB) and Azure NDv5 (8 x H100 80GB) are the comparable options. GPU pricing is opaque — most serious training workloads are negotiated through enterprise agreements. At list price, GCP is generally 5-10% cheaper for equivalent GPU-hours. For inference at high throughput, AWS Inferentia2 and GCP’s TPU v5 are significantly cheaper than GPU instances for models compatible with their architectures.
Which cloud is cheapest for my specific workload?
There is no universal answer, and anyone who gives you one without knowing your workload is guessing. The correct approach: use each provider’s official pricing calculator to build an equivalent estimate, include storage, egress, and support tier costs (not just compute), and model your commitment discount at both 1-year and 3-year terms.
The list-price comparison almost always shows GCP and Azure within 5-10% of each other with AWS slightly higher. The committed-rate comparison often inverts depending on the commitment depth you can commit to. A useful shortcut: if your primary concern is AI/ML cost, benchmark GCP first.
If your primary concern is database and storage cost, benchmark AWS first. If your primary concern is reducing the total Microsoft spend across your organization, benchmark Azure first.
Commitment Discounts: How the Three Providers Compare
This is where your real multi-year savings are — and where the three providers have meaningfully different structures.
| Dimension | AWS | Azure | GCP |
| 1-year commitment discount | ~33% (RI) / ~40% (SP on 1-yr) | ~40% Reserved VM Instances | 25% CUD |
| 3-year max discount | Up to 69% (3-yr All Upfront RI) | ~65% Reserved VM Instances | 52% CUD |
| Commitment type | Instance-specific (RI) or spend-based (SP) | Instance-specific (Reserved VM) or spend-based (Azure SP) | Spend-based, regional (CUD) |
| Cross-service flexibility | Compute SP covers EC2 + Fargate + Lambda | Azure SP covers most compute | CUD covers all dedicated-core instances in region |
| Storage covered by commitment? | No | No | No |
| Covers multiple engines/DBs? | Yes (Compute SP) / No (RDS RI) | Varies by service | Yes (all DB engines in region) |
Sources: AWS Savings Plans documentation, Azure Reserved VM Instances documentation, Google Cloud CUD documentation (docs.cloud.google.com/sql/cud, April 2026). Rates approximate and subject to change. Verify at each provider’s pricing and commitment documentation.
The GCP 3-year CUD at 52% is the simplest commitment structure: one spend commitment covers all dedicated-core database and compute instances in a region. AWS offers the deepest discounts (up to 69%) but the most complex commitment structure. Azure sits in the middle on both dimensions.
GCP Committed Use Discounts vs AWS Savings Plans: multi-cloud comparison
Switching Providers: What Actually Happens to Your Stack
Cloud migrations are one of those decisions that look cleaner on a whiteboard than they are in production. The cost of switching is rarely the egress fee — all three major providers now waive egress fees for full migrations when you contact them directly. The actual switching cost is almost always the re-architecture work, the parallel running period, and the productivity loss during the transition.
What happens to my AWS Reserved Instances if I migrate to GCP?
They continue billing until they expire. An RI is a billing commitment, not a running resource. If you migrate your EC2 instances to GCP but your AWS RIs still have 18 months remaining, AWS continues charging the committed rate for the full 18 months regardless.
There are two partial remedies: list Standard RIs on the AWS Reserved Instance Marketplace (EC2 RIs can be sold; RDS RIs cannot), or contact AWS to negotiate early termination credits. Neither is guaranteed. The practical lesson: plan cloud migrations around RI expiration dates. If your RIs expire in Q3, do not start a major migration in Q1. Or budget the remaining RI cost explicitly into the migration total cost calculation so finance is not surprised.
What happens to my data if I switch cloud providers?
Your data does not disappear — it just gets expensive to move unless you plan carefully. AWS, Azure, and GCP have all waived egress fees for full migrations (you move all data out and wind down the account), but the process requires contacting the provider, credit approval, and a specific exit timeline.
Partial migrations — where you move some workloads but keep others — do not qualify for waived egress and still incur standard data transfer charges. For a 50 TB database migration: at $0.09/GB standard egress from AWS, the transfer itself costs $4,608 before credits. With credits approved for a full migration, it is $0.
The bigger concern is usually not the egress cost but the data format compatibility — your S3-specific data lifecycle policies, your RDS snapshots, your CloudFront distributions all need equivalent configuration on the destination cloud. Budget 2x the compute migration time for data and configuration migration. Source: Forrester analysis of cloud egress policy changes, TechRadar.
Why is my cloud bill so much higher than I expected when we started?
Four reasons account for the majority of bill surprises across all three clouds. First: storage costs. Object storage (S3, GCS, Azure Blob) and managed database storage are often budgeted at headline rates but the bill includes additional charges for requests, retrieval, cross-region replication, and access tier transitions that are not on the pricing page front page. Second: data transfer.
Moving data between services, between regions, and out to the internet incurs charges that no amount of compute optimization reduces. Third: commitment mismatch. Teams that purchased Reserved Instances or CUDs for a workload that then changed — right-sized, migrated, or shut down — continue paying committed rates on unused reservations. Fourth: idle resources. Stopped instances that retain IP addresses, snapshots from deleted volumes, and load balancers with no active traffic all generate charges with no corresponding value.
Security, Compliance, and Data Residency: The Non-Cost Factors That Drive Provider Decisions
Cost is rarely the only — or even the primary — driver of cloud provider selection for regulated industries. Security certifications, compliance frameworks, and data residency commitments often narrow the decision before pricing enters the conversation.
AWS Compliance Posture
AWS has the broadest compliance certification portfolio of the three providers: SOC 1/2/3, ISO 27001, PCI DSS Level 1, FedRAMP (US government), HIPAA, and many country-specific frameworks. AWS GovCloud (US-East and US-West) provides dedicated infrastructure for US government and defense workloads with stringent isolation requirements. The breadth of certifications reflects AWS’s decade-long head start in compliance.
Azure Compliance Posture
Azure has the strongest compliance story for European enterprises. Azure Germany (operated by T-Systems as a separate data trustee entity) and Azure’s EU Data Boundary commitment — which keeps EU customer data within EU borders — are the most explicit sovereignty commitments of any major cloud. For organizations operating under GDPR with strict data residency requirements, Azure’s compliance documentation and Microsoft’s legal support team are the most mature in the industry.
GCP Compliance Posture
GCP’s compliance posture is strong but its differentiated compliance story is Assured Workloads, which lets you configure data residency and access restrictions within standard GCP infrastructure without requiring physically separate government cloud regions. For teams that need compliance without the operational complexity of a separate cloud environment, Assured Workloads is genuinely innovative. GCP also has the strongest default encryption model: Google encrypts all data at rest by default with AES-256, and customer-managed keys via Cloud KMS are well-integrated.
The Decision Framework: Who Should Be on Which Cloud
No cloud is universally best. The following framework is based on the workload and organizational patterns where each provider demonstrably wins:
Choose AWS if: you are a startup that expects to use 15+ distinct cloud services and needs mature managed versions of all of them. You have a multi-geography footprint that needs consistent service availability across 30+ countries. Your engineering team is predominantly AWS-certified and switching would require significant retraining. Your commitment strategy can support the complex RI/SP optimization that extracts the maximum discount from AWS’s pricing architecture.
Choose Azure if: your organization already has a Microsoft Enterprise Agreement and you can convert existing software spend to Azure credits. You are deep in the Microsoft 365 ecosystem and your AI strategy involves native OpenAI/GPT integration. Your compliance requirements center on EU data residency or US government cloud. You are running hybrid cloud with significant on-premises Windows Server or SQL Server infrastructure that Azure Arc manages most cleanly.
Choose GCP if: your data strategy is built on BigQuery and you want your compute collocated with your analytics. You are an AI-first company training models at scale and need the best price-performance on GPU and TPU instances. You are already in the Google Workspace ecosystem and your team uses Google-native tools for collaboration and identity. You want the simplest commitment discount structure (one CUD covers all compute and database in a region) without the complexity of AWS’s RI/SP architecture.
Multi-Cloud in 2026: 87% of Enterprises Are Already There
Multi-cloud is no longer a strategy decision for most large enterprises — it is a description of the current state. 87% of organizations run a multi-cloud strategy, and 73% operate hybrid cloud estates as of 2026, according to quantumrun.com citing Synergy Research Group and Gartner data. The question has shifted from ‘should we use multiple clouds’ to ‘how do we manage the cost and complexity of the multiple clouds we already have.’
The most common multi-cloud patterns in 2026: AWS as primary with GCP for AI/BigQuery analytics (the most common new pattern), AWS as primary with Azure for Office 365 and SharePoint integration (established enterprise pattern), GCP as primary with AWS for specific services where AWS has no GCP equivalent. Pure single-cloud shops still exist — primarily startups in early growth phase and government entities with strict sovereignty requirements — but they are the minority among organizations above $10M/year in cloud spend.
What if I am on two clouds and want to consolidate to one — is it worth it?
Rarely, at scale. The cost of consolidation (migration engineering, parallel running, service re-architecture for workloads that depend on cloud-specific features, retraining teams on new tooling) almost always exceeds the management overhead savings from single-cloud operation, unless you are under $500K/year in total cloud spend. Above that threshold, the architectural and personnel lock-in on each cloud is deep enough that consolidation typically triggers a multi-year disruption that pays back slowly.
A better target is standardizing your management layer across clouds — using a multi-cloud cost management platform, a unified identity and access management layer, and consistent tagging and allocation practices — while letting each cloud serve the workloads it actually does best.
How Usage.ai Works Across All Three Clouds
Commitment discounts — AWS Reserved Instances and Savings Plans, Azure Reserved VM Instances and Savings Plans, GCP Committed Use Discounts — are where the largest cloud cost savings live for organizations running steady production workloads. They are also the category with the most complexity, the most risk of over-commitment, and the highest penalty for getting the sizing wrong.
Usage.ai automates commitment purchasing across AWS, GCP, and Azure. The platform analyzes your actual usage patterns across all three clouds with a 24-hour refresh cycle, calculates the optimal commitment level for each provider and each service, and purchases commitments automatically within parameters you approve. Savings are tracked per cloud, per service, and per commitment type.
The differentiation from buying commitments manually: Usage.ai’s Insured Flex Commitments carry a buyback guarantee. If a commitment becomes underutilized because a workload is scaled down, migrated, or shut down — the common source of stranded commitment waste — Usage.ai provides cashback on the unused portion in real money, not credits. This changes the risk calculation for organizations that have been under-committed out of fear of waste. Usage.ai has delivered $91M+ in verified savings across 300+ enterprise customers across AWS, GCP, and Azure. Fee: percentage of realized savings only.
See how Usage.ai optimizes commitments across AWS, Azure, and GCP
Frequently Asked Questions
1. Which cloud service provider is best in 2026?
Depends on your workload. AWS leads on service breadth and ecosystem (30% market share, 240+ services). Azure leads on enterprise deals and Microsoft integration (25% market share, fastest absolute revenue growth). GCP leads on AI/ML cost and analytics (13% market share, fastest percentage growth, 5-10% cheaper for AI workloads). There is no single best provider — the right answer is workload-specific. Source: Synergy Research Group Q1 2026.
2. What is the market share of AWS vs Azure vs GCP in 2026?
AWS holds 30% of global cloud infrastructure spend, Azure 25%, and Google Cloud 13% as of Q1 2026 per Synergy Research Group. Together they control 68% of enterprise cloud spending. Growth rates: Azure +40% YoY, GCP +63% YoY, AWS +19% YoY. The market gap between AWS and Azure has narrowed by 5 percentage points over the past 3 years. Source: quantumrun.com citing Synergy Research Group Q1 2026.
3. Is Google Cloud cheaper than AWS?
On compute list price, GCP is generally 5-10% cheaper than AWS for equivalent configurations. GCP database compute (Cloud SQL) is often 15-20% cheaper than AWS RDS for MySQL at equivalent specs. However, GCP SSD storage at $0.22/GB-month is nearly double AWS gp3 at $0.115/GB-month. For storage-heavy workloads, AWS is typically cheaper overall. For compute-intensive AI workloads with modest storage, GCP often wins on total cost.
4. Can I switch cloud providers for free?
Data transfer fees are now waived for full migrations by all three major providers (AWS, Azure, GCP) when you contact the provider directly and request migration credits. However: this applies only to full migrations (you move all data and wind down the account), not partial moves. It requires provider approval and is not automatic. The bigger cost of switching is engineering time and parallel operation, not egress. Budget 6-18 months of transition time and parallel running for a non-trivial workload migration. Source: Forrester, TechRadar, TechCrunch reporting on egress fee changes.
5. What happens to my Reserved Instances if I leave AWS?
They continue billing until expiration. RIs are billing commitments, not physical resources. If you migrate to GCP or Azure before your RIs expire, AWS continues charging the committed rate for the remaining term. EC2 Standard RIs can be listed on the AWS Reserved Instance Marketplace for early exit. RDS RIs cannot be sold. The practical advice: plan migration timelines around RI expiration dates. If that is not possible, budget the remaining committed term explicitly as a migration cost.
6. Which cloud is best for AI workloads?
GCP has the strongest AI-specific infrastructure: TPU v5 for large model training, Vertex AI for MLOps, native Gemini model access, and A3 H100-backed instances approximately 5-10% cheaper than AWS and Azure equivalents for GPU compute. AWS has the broadest AI service catalog (SageMaker, Bedrock, Trainium3 for training, Inferentia2 for inference) and the largest partner ecosystem. Azure has exclusive access to OpenAI GPT-5 within enterprise services and the strongest enterprise AI integration via Microsoft Copilot. For cost-sensitive AI training: benchmark GCP first. For enterprise AI applications built on GPT models: Azure. For broad AI service coverage: AWS.
7. How do I reduce my cloud bill across multiple providers?
Three levers apply across all three clouds: right-sizing (match instance specs to actual workload requirements using CloudWatch, Azure Monitor, or Cloud Monitoring data), commitment discounts (AWS RIs and Savings Plans, Azure Reserved VMs, GCP CUDs — each saves 25-69% on compute depending on term and provider), and idle resource elimination (unattached storage, stopped instances with IPs, unused load balancers). For teams spending $50,000+/month across multiple clouds, automated commitment management platforms that purchase and monitor commitments continuously deliver the most consistent savings without requiring manual monthly optimization work.
