AWS vs Google Cloud: Pricing Comparison for Small Teams (2026)

AWS holds 31% of global cloud market share with over 200 services, while GCP holds 11% with roughly 150 services (Synergy Research, Q4 2025). AWS has the broadest catalog. GCP has the simplest pricing model. That distinction shapes nearly every cost comparison between the two.

AWS built its pricing around flexibility and control. You get the most instance types, the deepest discount programs (Reserved Instances, Savings Plans, Spot), and the widest regional coverage. But that flexibility means more decisions. A small team can easily leave 20-30% savings on the table just by missing a discount commitment.

GCP took the opposite approach. Sustained-use discounts kick in automatically when you run an instance more than 25% of a month — no upfront commitment, no reservations to manage. Per-second billing has been the default since launch. The pricing is flatter and more predictable, which suits teams that don't have a dedicated FinOps person.

So which is actually cheaper? It depends entirely on your workload profile. Let's break it down service by service.

Evaluating Microsoft Azure as well? See our AWS vs Azure pricing comparison and Azure vs GCP pricing comparison for a complete three-way view.

GCP Compute Engine is roughly 5-10% cheaper than AWS EC2 for steady workloads, primarily because sustained-use discounts apply automatically at 20-30% off on-demand rates (Google Cloud documentation). AWS requires active enrollment in Savings Plans or Reserved Instances to match those savings. For small teams that forget or skip commitments, GCP wins by default.

AWS counters with sheer variety. EC2 offers 750+ instance configurations across general purpose, compute-optimized, memory-optimized, GPU, and specialty families. GCP's catalog is smaller but includes custom machine types — you pick the exact vCPU and memory ratio you need instead of choosing the closest predefined size. That precision can reduce waste for workloads that don't fit standard shapes.

The spot market tells a different story. AWS Spot Instances can save up to 90% off on-demand prices, while GCP's equivalent — Spot VMs (formerly preemptible) — cap closer to 60-80% off. AWS also gives you more control over interruption handling and capacity pools. If your workload tolerates interruptions, AWS's spot ecosystem is significantly more mature.

Both providers bill per second with a one-minute minimum. AWS introduced per-second billing in 2017; GCP had it from the start. For most small teams running steady workloads without active discount management, GCP's automatic discounts make it the cheaper compute option out of the box. But teams willing to commit to one- or three-year Savings Plans on AWS can match or beat GCP pricing.

AWS RDS supports six database engines (MySQL, PostgreSQL, MariaDB, Oracle, SQL Server, and Aurora) while GCP Cloud SQL supports three: MySQL, PostgreSQL, and SQL Server. For teams running standard relational workloads, Cloud SQL is simpler to configure and roughly 10-15% less expensive for equivalent instance sizes based on public pricing calculators. The gap narrows significantly with AWS Reserved Instances.

Where GCP truly separates itself is analytics. BigQuery is a serverless data warehouse that charges per query ($6.25 per TB scanned) or offers flat-rate pricing for predictable workloads. There's no infrastructure to manage, no clusters to resize. AWS's closest equivalent, Redshift Serverless, charges $0.375 per RPU-hour and requires more configuration.

For a small team running ad-hoc analytics on a few hundred gigabytes, BigQuery's per-query model is hard to beat. You pay nothing when nobody's querying. On AWS, even Redshift Serverless has a minimum compute cost when active. If analytics is a core part of your product, this alone can tip the scales toward GCP.

What about managed NoSQL? AWS DynamoDB is the more mature option with predictable pricing and global tables. GCP's Firestore and Bigtable serve similar roles but with different trade-offs. DynamoDB's on-demand mode at $1.25 per million writes is straightforward. Firestore charges per document operation, which can surprise you at scale. For small teams, DynamoDB's pricing is easier to predict.

Before settling on a provider, our cloud cost estimation guide walks through how to model total infrastructure spend across compute, databases, and storage.

Standard-tier object storage pricing is nearly identical: AWS S3 Standard costs $0.023/GB/month and GCP Cloud Storage Standard costs $0.020/GB/month in US regions. For a small team storing 500 GB, that's a difference of $1.50 per month. The meaningful savings come from archival tiers and lifecycle policies, not the standard tier.

GCP Nearline storage ($0.010/GB/month) is about 20% cheaper than S3 Infrequent Access ($0.0125/GB/month) for data accessed less than once per month. For archival data accessed less than once per quarter, GCP Coldline ($0.004/GB/month) and S3 Glacier Instant Retrieval ($0.004/GB/month) are priced identically. The deeper you go into cold storage, the less the pricing difference matters.

AWS has more storage classes overall — six tiers from S3 Standard to Glacier Deep Archive. GCP keeps it simpler with four: Standard, Nearline, Coldline, and Archive. Both providers offer lifecycle rules that move objects between tiers automatically. AWS S3 Intelligent-Tiering adds an automated option that monitors access patterns and shifts objects for you, at a small monitoring fee per object.

Does the storage class difference matter for small teams? Honestly, not much. Pick either provider and configure lifecycle policies. The real cost driver in storage isn't the per-GB rate — it's the forgotten snapshots, unattached volumes, and orphaned backups that accumulate silently over months.

GCP provides 200 GB of free internet egress per month. AWS charges $0.09 per GB starting from the very first byte (GCP network pricing). For a small web application serving 500 GB of outbound data monthly, that's $45 on AWS versus roughly $36 on GCP — a $9/month difference that compounds as traffic grows.

Here's the nuance most comparisons miss: GCP's per-GB rate after the free tier is $0.12/GB — higher than AWS's $0.09/GB. The breakeven point is approximately 850 GB per month. Below that, GCP is cheaper thanks to the free tier. Above that, AWS's lower per-GB rate starts to win. Most small teams fall well below 850 GB.

Cross-zone data transfer adds another wrinkle. AWS charges $0.01/GB for traffic between Availability Zones — a cost that catches teams off guard when running multi-AZ deployments. GCP doesn't charge for cross-zone traffic within the same region in most configurations. For a microservices architecture with heavy inter-service communication, this adds up quietly.

What about ingress? Both providers offer free inbound data transfer. No surprises there. The real cost trap on AWS is internal traffic: NAT Gateway processing fees, cross-AZ charges, and VPC endpoint costs that don't exist in GCP's networking model. For small teams, GCP's simpler network pricing is genuinely easier to predict.

There's no single winner. AWS offers 200+ services compared to GCP's ~150, giving it the edge for teams that need niche managed services (AWS). GCP wins on pricing simplicity and AI-native tooling. The right choice depends on what you're building and what trade-offs you can tolerate.

Choose AWS if you need breadth and ecosystem maturity

AWS is the safer bet when your project requires obscure managed services, deep third-party integrations, or a large hiring pool of experienced engineers. Lambda's serverless ecosystem is more mature than Cloud Functions. The Marketplace has more pre-built AMIs and SaaS integrations. If you're building something that touches many AWS services, the interoperability advantage compounds over time.

Choose GCP if AI/ML or analytics is your core workload

GCP's strength in AI and machine learning is hard to argue against. TPU access for training, Vertex AI for model serving, and BigQuery ML for running models directly on your data warehouse — these are first-party services with no direct AWS equivalent at the same level of integration. If your product is AI-first, GCP's toolchain has less friction.

Choose GCP for Kubernetes-native teams

Google invented Kubernetes, and GKE shows it. GKE Autopilot manages the control plane and node infrastructure entirely, charges per pod rather than per node, and eliminates most cluster management overhead. EKS is solid but requires more hands-on node group management. For a small team running containers, GKE Autopilot reduces operational burden significantly.

Choose GCP for budget-conscious startups with steady traffic

If you're a startup watching every dollar, GCP's automatic discounts, free egress tier, and simpler pricing model means fewer surprises. You don't need someone managing Savings Plans. You don't need to worry about cross-AZ transfer fees. The bill is more predictable, and that matters when you're pre-revenue or early-revenue.

Choose AWS for enterprise complexity

If you're running a complex multi-account organization, need deep compliance tooling (GovCloud, HIPAA BAA across 100+ services), or your clients mandate AWS, the ecosystem maturity wins. AWS Organizations, Control Tower, and IAM Identity Center are more battle-tested than GCP's equivalents for large-scale governance.