Google Cloud Platform - After 3 Years, I Still Don't Hate It

GCP is Google's cloud platform. Started in 2008 with App Engine (which was pretty ahead of its time), it's grown into a legitimate competitor to AWS and Azure. The big selling point? It runs on Google's actual infrastructure - the same stuff that keeps Search working when everyone's googling "is the internet down."

Why GCP Doesn't Completely Suck

Every cloud provider says they're amazing. Here's why GCP doesn't completely suck:

It actually runs on Google's network: This isn't marketing bullshit. GCP uses the same private fiber network that keeps YouTube streaming smooth when half the planet is watching cat videos. I switched our API from AWS us-east-1 to GCP europe-west1 in March 2024 and P95 response times dropped from 180ms to 95ms. The Premium Network Tier costs 50% more but when you're serving traffic to 47 countries, that extra $127/month saved us 6 hours of customer complaints about slow API responses.

AI that doesn't suck: Google's AI tools are legitimately good, not just overhyped garbage. Vertex AI gives you access to models that actually work, including the latest Gemini stuff. If you're doing ML work, this is where GCP genuinely beats AWS and Azure.

Kubernetes without the pain: Google invented Kubernetes, so GKE (Google Kubernetes Engine) is the least painful way to run containers at scale. GKE Autopilot handles all the cluster management bullshit for you - it's what Kubernetes should have been from the start.

The Stuff You Actually Use

GCP has the usual cloud services, but here's what matters:

Compute (The Boring But Important Stuff)

• Compute Engine: VMs that actually stay up. Unlike AWS where I lost 12 hours of training data because a spot instance died during a Terraform deployment, GCP's preemptible instances give you 30 seconds warning. The sustained use discounts kick in automatically after you hit 25% usage - no stupid reserved instance gambling where AWS makes you predict your usage 3 years in advance.
• TPUs: Google's custom chips for ML workloads. Actually fast, but you'll pay for it. Only useful if you're training huge models.
• Premium network tier: Costs like 50% more or something, but your packets go through Google's private network instead of the shitty public internet. Worth it if you're seeing latency issues - cut our API response times by maybe 40% when we switched last year.

Data Stuff

• BigQuery: The one service that makes everyone jealous of GCP. Query petabytes of data without babysitting clusters. Just write SQL and it works. Billing is $6.25/TB scanned as of September 2025 but can fuck you over - I saw a $12K bill when someone forgot to add WHERE clauses to SELECT * FROM bigquery-public-data.github_repos.commits. That specific query scanned 1.9TB before I killed it with "Error: Query exceeded limit of 1000 seconds" after maxing out our daily quota. Always use the query validator before hitting run on anything larger than your dev dataset.
• Cloud Storage: Object storage that works like S3 but with better egress pricing. The multi-regional replication is solid.
• Pub/Sub: Message queuing that actually scales. Better than SQS for high-throughput scenarios.

App Platform Stuff

• Cloud Run: Serverless containers done right. Deploy a Docker container and it scales from zero automatically. Way better than Lambda for anything that doesn't fit the function model.
• App Engine: Google's old serverless platform. Still works but Cloud Run is probably what you want now.
• Cloud Functions: Google's answer to Lambda. Cold starts average 89ms for Node.js 18 vs Lambda's 180ms, but you'll still get fucked by the 9 minute execution limit. Learned that when our PDF generation function died mid-process with "Function timeout" after exactly 540 seconds.

AI Stuff

• Vertex AI: Where GCP actually shines. Pre-trained models that work out of the box and APIs that don't require a PhD in ML.
• AutoML: Point-and-click machine learning. Surprisingly good for common use cases like image classification.

New Stuff in 2025

Google's been busy this year. Some of it's actually useful:

Firestore now speaks MongoDB: About fucking time. You can migrate from MongoDB without rewriting all your code. The serverless scaling is nice, but query patterns don't always translate cleanly - the docs are vague about performance gotchas which will bite you later.

Gemini embeddings that actually work: The new embedding model beats OpenAI's on most benchmarks. Good for RAG applications if you're tired of paying OpenAI's ridiculous API fees. Just remember Google sees all your data when using hosted models.

Cloud Run with GPUs: Serverless GPU compute is finally here. Great for AI inference workloads, but cold start times are brutal for GPU instances - I've seen 15-45 seconds for the first request after sitting idle. Learned that the hard way when our image classification API went down during a demo because it hadn't been hit in 20 minutes.

Security Stuff

Google's security is solid, but Cloud IAM is confusing as hell:

Everything's encrypted: At rest, in transit, doesn't matter - it's encrypted. Google handles the keys unless you want to manage your own (spoiler: you don't). The compliance certifications are real and comprehensive.

Cloud IAM will make you want to quit tech: Over 3,000 predefined roles sounds great until you're debugging why roles/run.developer can't deploy a container but roles/editor can. Spent 8 hours getting "Error: User does not have permission to access service account" before figuring out you need roles/iam.serviceAccountUser too. The error message doesn't tell you this. The docs are written by lawyers, not engineers. Budget a long weekend and strong coffee to figure this shit out, or just give everyone roles/editor and pray your security team doesn't find out.

DDoS protection: Cloud Armor handled that massive 2.54 Tbps attack, so it's legit. VPC Service Controls are like security groups but more confusing - useful once you figure them out.

The Reality Check

GCP is still third place with around 11% market share (maybe 10-12%, depends who's counting) behind AWS's massive chunk and Azure. But it's growing way faster - like 28% year-over-year last quarter according to Alphabet's earnings. Still, the ecosystem feels smaller when you're hunting for third-party integrations.

Big names like PayPal, Deutsche Bank, and Spotify use GCP for production workloads. That said, the ecosystem is still smaller than AWS - you'll find fewer third-party tools and integrations.

GCP rolled out a bunch of updates this year. Some are genuinely useful, others are just rebranded existing stuff. Here's what you need to know:

Compute Stuff That Actually Matters

New VM Types

The C4 instances with Intel Xeon 6980P are legitimately fast - I'm seeing 35% better performance on our CPU-heavy ETL jobs compared to the n2-standard-32 VMs we were using. Our nightly data pipeline went from 4.2 hours to 2.8 hours on the same data volume. Great for number-crunching, but they cost 40% more and are only in 8 regions as of September 2025. Classic Google - release something useful then make it impossible to actually get. Took 3 weeks and 2 escalations to get quota approval for 64 vCPUs.

License switching: You can finally change Windows licenses on running VMs without rebuilding the instance. Took them long enough - AWS has had this forever.

Local SSDs are faster: The new Titanium storage hits 2.5M IOPS compared to 1.6M on the old NVMe drives. But performance tanks during peak hours - learned this the hard way when our batch jobs went from 45 minutes to 2.5 hours every afternoon. Local storage is risky as fuck - had a disk failure destroy 6 hours of ML training data last month.

Kubernetes Updates

GKE got more knobs to turn: 130+ new configuration options in GKE 1.29.7 sounds great until you realize most people just want Kubernetes to work without reading 500 pages of docs. The new topology manager helps with NUMA alignment for ML workloads, but it's overkill for most apps and will probably break something unexpected. Enabled it on our cluster and got "Pod failed to schedule: No available nodes with topology affinity" for 3 days before realizing it broke our regular web app pods.

Cloud Run for background jobs: Worker pools let you run non-HTTP workloads on Cloud Run. Finally. It's still in preview so expect it to break in production at the worst possible moment, but it's useful for queue processing and batch jobs.

Database Updates

Firestore Speaks MongoDB Now

The MongoDB compatibility is real - you can actually use your existing MongoDB 5.0+ drivers and most queries work. But watch out for:

• Complex aggregation pipelines break in subtle ways that'll take you hours to debug
• Performance is totally different from real MongoDB - what's fast there might be slow here
• Pricing is pay-per-operation vs. fixed costs, so your bill might explode unexpectedly

It's decent for migrations but test everything twice. We had an app that worked perfectly in dev with our user collection (12K docs), then blew up in prod because aggregation pipelines with $lookup took 15 seconds vs 1.2 seconds on MongoDB Atlas. The exact error was "Query timeout exceeded" after 30 seconds, which caused our user profile API to return 503s for 6 hours until we reverted. Cost us 6 hours of downtime, 847 support tickets, and a very angry VP.

SQL Server Migration Tools

Google finally built decent SQL Server to PostgreSQL migration tools. The Gemini-powered code conversion actually works for simple cases, but you'll still need to manually fix stored procedures and complex queries. Takes way longer than Google says it will. Shocking, I know.

Storage Improvements

Better backups: Backup vaults now handle disk backups across multiple regions. Useful for compliance, but adds complexity to your backup strategy.

Data Analytics Updates

BigQuery Gets Spark

Serverless Spark in BigQuery is actually useful - you can run Spark jobs without the cluster management headache. Google claims 3.6x faster, but when I tested it last month on our ETL pipeline, it was more like 2x faster. Still worth it to avoid babysitting Dataproc clusters.

BigQuery Studio is basically a Jupyter notebook that connects to BigQuery. It's fine, nothing revolutionary.

Satellite data in SQL: Earth Engine integration lets you query satellite imagery with SQL. Cool for GIS work, useless for most people.

Messaging Updates

Pub/Sub transforms: You can transform messages in Pub/Sub with JavaScript functions now. Saves you from running a separate service for simple data transformations.

MongoDB to BigQuery: Datastream finally supports MongoDB, so you can pipe MongoDB changes directly into BigQuery. Works with Atlas too, which is nice.

AI Updates

Vertex AI Gets Better

Gemini embeddings are solid: The new embedding model beats OpenAI's on most benchmarks. You can now batch 250 texts per request instead of one-at-a-time, which helps with API costs.

DeepSeek R1 is available: This 671B parameter model shows its reasoning process, which is actually useful for debugging. Still in preview though, so expect API changes.

AI Agent Tools

Agent Builder: Multi-agent workflows are possible now. The Schroders case study is impressive, but most companies aren't doing anything that complex yet.

Developer Tools

Location Finder

Cloud Location Finder tells you which regions to use across different cloud providers. Useful if you're doing multi-cloud, but most people aren't.

HPC Stuff

New HPC VM images with Slurm support for scientific computing. Only relevant if you're running supercomputer-style workloads.

Security Updates

AI Security

New AI security features help protect against prompt injection and model tampering. Useful if you're running AI workloads in production, but the tooling is still immature.

IAM Improvements

Cloud IAM got more roles and better enterprise integration. Still confusing as hell, but at least there's better SSO support now.

That's the 2025 update summary. Some genuinely useful improvements mixed with typical Google over-engineering.

GCP wants to be taken seriously by big companies. Here's where they actually deliver and where they fall short:

Security That Actually Works (But Is Confusing)

The Good

Encryption everywhere: Everything is encrypted by default - at rest, in transit, doesn't matter. Google's Titan chips handle billions of encryption operations per second, so performance isn't an issue.

DDoS protection is legit: Cloud Armor successfully defended against that 2.54 Tbps attack (biggest on record). We got hit with a 400 Gbps attack last year and didn't even notice until I checked the logs. Zero downtime, zero manual intervention.

The Painful

Cloud IAM is a nightmare: Sure, thousands of roles sounds great until you spend 8 hours trying to figure out why your service can't read from a bucket. The conditional access policies are powerful but I needed 3 cups of coffee and a weekend to understand how they work.

Compliance Stuff

GCP has 100+ certifications including all the ones enterprises care about: SOC 2, ISO 27001, HIPAA, FedRAMP High. The certifications are real and comprehensive.

Assured Workloads guarantees data residency and limits personnel access for government work. Expensive but necessary if you're dealing with classified data.

Where GCP Actually Beats AWS and Azure

AI That Doesn't Suck

This is where GCP shines. The AI tools actually work instead of just being marketing hype:

Foundation models that work:

• Gemini: Multimodal model that scores 90% on MMLU. Actually understands images and text together.
• PaLM 2: Powers Bard and comes in different sizes. The small ones (Gecko) run fast, the big ones (Unicorn) are smart.
• Imagen 2: Text-to-image that's competitive with DALL-E but includes watermarking.

AutoML for non-experts: Point-and-click machine learning that actually produces decent models:

• AutoML Vision works well for image classification if you have enough training data
• AutoML Tables handles structured data better than most data scientists

ML Infrastructure

Model serving: Vertex AI scales from zero to millions of requests automatically. I'm seeing 95ms P95 latency on our image classification API, but it spikes to 800ms when traffic jumps 10x (like when TechCrunch links to us). Auto-scaling takes 30-60 seconds to kick in, so plan for that.

Custom training hardware:

• TPU v5: Way faster than v4 for training huge models - Google claims 4x but I got 3.2x speedup training a BERT-large model (340M parameters) on our financial text dataset. Training time dropped from 14 hours to 4.4 hours per epoch. Costs $8.38/hour per chip vs $2.40 for v4, so budget accordingly. Good luck getting quota - I've been waiting 8 weeks for TPU v5 allocation.
• GPU instances: H100 and A100 clusters with 1.6TB/s node interconnects. Good luck getting quota - been waiting 6 weeks for H100 allocation. They vanish during ML conference season.
• Edge TPU: For on-device inference. Good performance but limited model support.

Hybrid Cloud (Anthos)

Multi-Cloud Reality Check

Anthos is Google's attempt at hybrid/multi-cloud management. It works, but:

The good: Consistent Kubernetes experience across clouds. GitOps configuration management is solid. Service mesh (Istio) handles microservices complexity well.

The reality: Multi-cloud is expensive and complex. Most companies think they want it but end up standardizing on one provider anyway. Anthos is overkill unless you're actually running workloads across multiple clouds.

Google Workspace Integration

If you're already using Google Workspace, the integration is seamless:

SSO just works: Identity management between Cloud and Workspace is painless compared to Azure/Office 365 or AWS/everything else.

AI features: Gmail's Smart Compose, Docs' grammar suggestions, and Meet's transcription all use Google Cloud AI. They actually work, unlike most AI productivity features.

Industry Solutions (Mostly Marketing)

Financial Services

GCP has specialized fintech tools but they're mostly repackaged existing services:

Fraud detection: BigQuery + AI models work well for real-time transaction analysis. The latency is genuinely sub-second.

Compliance frameworks: Pre-built templates for regulations like Basel III and SOX compliance. Helpful starting points but you'll still need legal review and they cost $30K/month minimum. Most companies just check the boxes without understanding what the controls actually do.

Healthcare

HIPAA compliance: Google signs BAAs and has dedicated healthcare APIs. The medical imaging and natural language processing tools actually work well for healthcare use cases.

Genomics: Good for processing large genomic datasets, but most healthcare organizations aren't ready for this level of sophistication.

Manufacturing

IoT and edge computing: Works for connecting factory equipment and sensors. Computer vision for quality control is genuinely useful.

Digital twin: Sounds fancy but most manufacturers aren't sophisticated enough to use it effectively.

Network Infrastructure (Actually Good)

Why GCP's Network Matters

This is where Google's infrastructure background shows:

Private fiber network: Google owns over 1 million miles of fiber. Your packets take Google's network instead of the public internet. The performance difference is real, especially for international traffic.

Premium network tier: Costs 50% more but your traffic gets priority routing. Worth it for latency-sensitive applications.

CDN integration: Uses the same infrastructure that serves YouTube. It works.

Sustainability (If You Care)

Google's been carbon neutral since 2007 and runs on renewable energy. Their data centers are more efficient (1.1 PUE) than most competitors.

Bottom line: GCP works well for enterprises that need AI/ML capabilities, can tolerate some learning curve complexity, and want infrastructure that actually performs. The security is solid, the AI tools are best-in-class, but the ecosystem is smaller than AWS.