Cloud computing has transformed how modern companies build and deliver applications. Rather than maintaining costly on-premises data centers, businesses now leverage on-demand infrastructure from cloud providers like Amazon Web Services (AWS) and Google Cloud Platform (GCP). This guide offers an in-depth, side-by-side analysis to help your organization evaluate AWS vs GCP.
Understanding Cloud Infrastructure Services
Before diving into the platforms, let‘s briefly explain Infrastructure-as-a-Service (IaaS) cloud computing:
- IaaS providers offer fundamental building blocks – networking, storage, servers – that companies use to host IT systems and applications without setting up hardware.
- Resources are delivered over the internet on a pay-as-you-go basis, scaling up and down dynamically based on usage.
- Companies access IaaS provider resources directly or provision virtual machines and environments customized to their needs.
To bring this concept to life…
Imagine your company needs additional capacity to support a new product launch. Without cloud IaaS, you‘d have to purchase more servers and data center resources – a slow, expensive process.
But with AWS or GCP, you can instantly spin up virtual machines, databases, object storage, and other infrastructure tailored to your temporary surge needs. That‘s the essence of the public cloud and what platforms like AWS and Google Cloud Platform enable.
Now let‘s take a deep dive comparing GCP vs AWS capabilities specifically…
Cloud Computing Market Leaders: AWS vs Google Cloud Platform (GCP)
IDC market research shows Amazon AWS currently leading in market share with 33% of worldwide spend. Microsoft Azure is second at 22%. Meanwhile, Google Cloud Platform trails at 9%, but is growing over 40% annually.
As cloud adoption accelerates, companies are deciding between these dual market leaders for primary or secondary IaaS providers:
- AWS Pros – Broadest, deepest feature set. Leading ecosystem. Enterprise expertise.
- GCP Pros – Engineering excellence. ML and analytics specialization. Open source commitment.
Let‘s analyze how they compare across key decision factors…
Comparing Core IaaS Building Blocks
The foundation of cloud infrastructure revolves around compute power, data storage, and networking connectivity. Here‘s how the two providers stack up across these fundamental areas:
Compute Options
| Service | AWS EC2 | Google Compute Engine |
|---|---|---|
| Description | Resizeable VMs | VMs + autoscaling containers |
| Instance Types | 400+ | 96+ predefined |
| OS Support | All major | All major |
| Language Support | Broad | Broad |
| GPU instances | Yes | Yes |
- AWS EC2 offers maximum configurability to customize VMs for diverse apps.
- GCP Compute emphasizes autoscaling, preemptibility, and containers via Kubernetes.
Compute advantage goes to AWS for breadth and depth of instance types. GCP counters with leading price-performance, especially for high capacity workloads.
Storage Services
| Service | AWS | Google Cloud |
|---|---|---|
| Object storage | S3 | Cloud Storage |
| Block storage | EBS volumes | Persistent Disks |
| Data archival | Glacier | Coldline, Archive |
| Caching | ElastiCache | Memorystore |
AWS and GCP present essentially parity in foundational storage capabilities for objects, files, archival, and caching.
Differences show up at extreme scale – AWS can provision very high IOPS EBS volumes exceeding anything available on GCP. Coldline and Glacier also vary in accessibility SLAs and cost structure.
Networking and Content Delivery
Both platforms offer robust global networking with availability zones and edge locations to reduce latency:
- AWS has slightly more metro areas today (84 zones vs. 35)
- GCP uses own private fiber backbone interconnect for speed
For edge networks and content delivery to users, AWS CloudFront is more mature. But GCP Cloud CDN capabilities are rapidly developing – recently adding 15 more locations.
Comparing Higher Level Managed Services
Now that we‘ve covered the IaaS basics, let‘s explore how AWS and Google Cloud compare on critical application infrastructure and middleware:
Database Capabilities
Complimenting core compute and storage, databases are vital for most applications:
| Purpose | AWS Services | Google Cloud Services |
|---|---|---|
| Relational, OLTP | RDS, Aurora | Cloud SQL, Spanner |
| Data warehousing | Redshift | BigQuery |
| Key-value store | DynamoDB | Bigtable |
| Search | OpenSearch, ElasticSearch Service | None |
| Graphs | Neptune | None |
For common SQL databases, AWS RDS and Aurora and mature and fully featured. GCP provides excellent support for PostgreSQL/MySQL as a service with Cloud SQL.
When it comes to NoSQL data like key-value stores and document databases, AWS DynamoDB is a top choice balancing performance with operational ease. For analytics against petabyte datasets, BigQuery leads.
Overall advantage here tilts to AWS for straddling both transactional and analytical use cases.
Analytics and Large Scale Processing
Both platforms offer fully managed platforms for SQL, ETL, data warehousing and big data processing:
| Purpose | AWS Services | Google Cloud Services |
|---|---|---|
| SQL querying | Athena | BigQuery |
| Data pipeline/ETL | Glue | Dataflow |
| Batch processing | EMR | Dataproc |
| Streaming analysis | Kinesis | PubSub/Dataflow |
| Data warehouse | Redshift | BigQuery |
AWS Redshift remains popular for managed data warehousing given its SQL interface familiarity. Google BigQuery runs on internal Google infrastructure optimized for enormous scale analytics at low cost.
For streaming pipelines and data integration, AWS Glue/EMR competes well with Google PubSub and Dataproc serverless options. Neither has an unequivocal edge.
Machine Learning Capabilities
Modern applications increasingly incorporate AI, so cloud machine learning services can accelerate development:
| Purpose | AWS Services | Google Cloud Services |
|---|---|---|
| ML model building | SageMaker | AI Platform |
| AutoML | Forecast | AutoML suite |
| Speech/Video AI | Rekognition | Video/Speech APIs |
| Text analytics | Comprehend | Natural Language |
| Translation | Translate | Translation |
AWS and GCP offer similar capabilities for training ML models against data warehouses and for scaling inferencing production workloads.
Google Cloud‘s AutoML impresses by automating tedious model development tasks for non-experts. While competitive on base ML building blocks, AWS leads in AI applications areas like image recognition and language.
Comparing Key Premium Features
Beyond the above services, additional differentiating functionality should guide your cloud platform evaluation process.
Development Tools and Environments
For programmers building cloud-native applications, the integrated tooling experience matters greatly:
- AWS Cloud9 delivers browser-based IDE tightly integrated to underlying AWS services.
- Google Cloud Shell offers browser IDE tailored for Linux/Python console dev.
GCP also provides Git integration and Cloud Source Repositories applying Google‘s internal engineering tools externally.
Overall GCP rates an advantage here applying learnings from Google‘s industry leading software practices.
Management and Governance
Admin capabilities for provisioning resources, applying policies, managing costs:
- AWS provides granular IAM identity and access control.
- Google Cloud IAM roles are similarly comprehensive.
GCP builds monitoring and alerting into Cloud Console for unified Admin visibility. For compliance AWS Trusted Advisor and Config have more extensive policy guardrails.
Hybrid Deployments and Migration
Most enterprises run a mix of private data centers and public cloud:
- AWS Outposts offers AWS environment hosted on-premises.
- Google Anthos promises consistency across hybrid/multi-cloud.
AWS aligns better to VMware stacks. Google Cloud spins Kubernetes container approach consistently on-prem and in cloud.
Pricing and Support
Cost and vendor relationships dictate long term value:
- Both AWS and GCP pricing depend on usage of specific services. Published list rates are similar.
- GCP strategically undercuts AWS on benchmark workloads as the challenger.
- AWS support offers tiered plans from developer to enterprise technical teams.
- GCP Cloud Support matches on technical severity levels.
Ultimately for sizable projects, negotiated enterprise discounts and reserved capacity bring overall TCO into closer parity between the two rivals.
Comparing Strategic Platform Differences
Now that we‘ve detailed area-by-area service comparisons, let‘s summarize the overarching strategic differentiators between the two cloud titans:
AWS Key Advantages
- Broadest, deepest IaaS portfolio
- Leading ecosystem of ISV partners
- Enterprise legacy workload expertise
- Largest global region footprint
Google Cloud Key Differentiators
- Engineering culture focused on UX
- Specialization in analytics and ML
- Leading price-performance
- Open source commitment
- Modern container-based architecture
AWS pioneered public cloud, has sustained rapid pace of innovation for 15+ years, and offers unmatched capabilities catering from legacy SQL to modern Kubernetes based applications.
Google Cloud has closed the gap considerably leveraging its technical prowess and data science leadership honed from search, ads, and video workloads. GCP feels simpler for those with modest Linux Admin skills.
Both have proven enterprise credibility, robust networking and leading compliance coverage, and world class operations.
Choosing the Best Platform for Your Workloads
Now that we‘ve thoroughly reviewed IaaS and PaaS capabilities across the clouds, here‘s guidance mapping common use cases to optimal platform:
If you have…
- Traditional SQL transactional systems → Pick AWS for broader database compatibility
- Java or Windows legacy applications → Pick AWS for seamless lift-and-shift
- High performance video/graphics intensive computing → Pick AWS for specialized instance types
- Advanced machine learning model development → Pick GCP for AutoML and deep learning foundation
- Large scale batch data analytics → Pick GCP for BigQuery cost-effectiveness at scale
- Open source preference → Pick GCP for Kubernetes, BigQuery OSS, friendly licensing
- Global zone density priorities → Pick AWS for more regions and lower latency
Of course hybrid and multi-cloud deployments that leverage each platform‘s strengths are popular for large enterprises. But the above guidelines serve you well identifying likely tech stack alignment.
Making Your Platform Picks
Choosing between AWS and Google Cloud impacts architecture choices and teams for years. How do you approach this pivotal decision?
You must balance technical needs with business considerations:
- Technical fit – Which platform has services fine-tuned to my problem space? Which will maximize developer productivity with best tools?
- Business considerations – Am I better served aligning with the open, innovative challenger or the market leader? How will the partnership evolve given my growth plans? Which sales team demonstrates commitment my success?
Rather than simply comparing spreadsheet feature checklists, I strongly urge you to run trials of real projects on both platforms.
- Build prototype workloads on each option leveraging free trials and credits. Observe how your developers respond.
- Ask pointed architectural questions to vendor solution architects based on your roadmap.
- Negotiate pilot pricing for staging meaningful proof of concepts.
AWS and GCP want to earn your business – so take advantage of their readiness to support controlled evaluations. Use these trials to make an informed choice measuring real metrics versus being swayed by sales and marketing propaganda alone.
While cloud platform evaluations require internal effort upfront, you‘re making a multi-year bet on the foundational building blocks fueling business growth. This is no time for snap judgments or guessing – do your homework!
Only hands on experience will reveal which choice aligns both rationally and intuitively to your needs today while offering the adaptability to meet future needs tomorrow. Let me know if I can offer any advice or introduce you to helpful contacts at either platform.
I wish you clarity and confidence selecting the ideal infrastructure for your next groundbreaking application!
