Migrate from AWS to GCP: The Migration Tool Guide for 2026
Back in early 2025, I sat with the CTO of a fintech startup processing 50 million transactions a month. He wanted out of AWS. Not because of performance — AWS ran fine. The problem was cost. Their monthly bill had hit $180K, and after three price hikes in 18 months, finance was screaming. We looked at GCP, ran the numbers, and found we could shave 34% off the compute cost alone. But the biggest headache wasn't the math — it was the process. How do you move a production data pipeline without breaking it?
That's what this guide is about. I'll show you exactly which migrate from AWS to GCP migration tool works for each workload, what to watch out for, and why most migration guides you've read already have it wrong. You'll learn how to plan the move step by step, estimate costs, and avoid the landmines that'll kill your timeline. And yes, I'll even cover how we used GCP's AI Vertex AI to build an mmwave material classification radar tutorial for a manufacturing client — because sometimes you need to move not just infrastructure but the intelligence on top of it.
Let's get into it.
Why Migrate Now? The 2026 Reality Check
Most people think cloud migration is about technology. It's not. It's about money and leverage.
By mid-2026, the big three cloud providers have settled into a clear pricing pattern. AWS still leads in raw service count, but they've been raising compute prices for three years straight. Google Cloud Pricing 2026: Cost Breakdown & Hidden Costs shows that GCP's sustained-use discounts and committed-use contracts can knock 30–50% off compute compared to AWS on-demand. NetApp's analysis confirms: for data-heavy workloads, GCP consistently wins on egress costs and network throughput.
But here's the contrarian take: don't migrate just to save money. Migrate because GCP's data and AI services are genuinely better for certain patterns. BigQuery beats Redshift hands-down for analytical queries. Cloud Run is simpler and cheaper than AWS Fargate for containerized apps. And Vertex AI training costs about 20% less than SageMaker for equivalent GPU types.
The catch? Migration is painful. You need the right tools and the right sequence.
The Tools That Actually Work: A Practical Survey
GCP offers a handful of migration tools. I've used most of them in anger. Here's what you need to know.
Migrate for Compute Engine (formerly Velostrata)
This is your primary lift-and-shift tool. It converts on-prem VMs or AWS EC2 instances into GCP Compute Engine VMs. The agentless discovery scans your source machines, translates disk formats, and replicates the state incrementally.
What it handles well: Windows and Linux VMs running standard software. We migrated a 200-VM cluster for a logistics company in three days using the bulk migration feature.
What it doesn't handle: Anything with custom kernel modules, exotic filesystems, or tight latency requirements. The conversion process sometimes mangles network configs — we had to patch DNS settings manually on 12 VMs.
Pro tip: Always run a test cutover first. The tool supports staging VMs in a test VPC before final switchover.
Migrate for Anthos (m4a)
If you're already containerized — or willing to containerize — this is your best bet. M4a analyzes your existing applications, generates a containerization plan using Kritis and Binary Authorization, and then deploys to GKE or Anthos clusters.
When to use: You have microservices or plan to decompose monoliths. We used m4a for a SaaS platform running on Amazon ECS. It analyzed 200 services, identified dependencies, and generated Dockerfiles for 88% of them automatically. The rest needed manual tweaks.
Price warning: m4a itself is free, but the Anthos subscription is not. At $1.80 per vCPU per month for the basic tier, it can add up. Do the math on Google Cloud Pricing Calculator before committing.
Storage Transfer Service
For moving object storage (S3 to GCS), this is your workhorse. It handles incremental syncs, scheduled transfers, and can use a storage appliance for large datasets.
Performance: We moved 50TB from S3 to GCS in 22 hours over a 10 Gbps link. The bandwidth throttling on AWS side was the bottleneck, not GCP.
Gotchas: File name encoding issues. GCS treats object names as binary strings; S3 uses UTF-8. If you have files with special characters, test a subset first.
Database Migration Service (DMS)
For relational databases, DMS is solid. It supports MySQL, PostgreSQL, SQL Server, and Oracle (to Cloud SQL or Spanner). Continuous replication with minimal downtime is possible using CDC (change data capture).
Real example: We moved a 2TB PostgreSQL database for a healthcare analytics company with 47 seconds of downtime. We failed the first time because we didn't disable triggers before cutover. Read the docs.
Transfer Appliance
When you have over 100TB and patience is low, the Transfer Appliance is a lifesaver. Google ships you a ruggedized storage device. You copy data locally, ship it back, and they upload it to GCS.
Cost: $300 per appliance + shipping. For a petabyte of raw sensor data from an IoT client, this was 40 times cheaper than shipping over the internet. But it took 14 days round-trip. Plan accordingly.
How to Migrate from AWS to GCP Step by Step (The Real Way)
Every official migration guide tells you to "assess, plan, migrate, validate." That's useless. Here's the actual step-by-step I've refined over ten migrations.
Step 1: Build a Non-Negotiable Cost Model
Don't guess. Use the Google Cloud Pricing Calculator to map your AWS resource inventory to GCP equivalents. Be realistic about SKUs — AWS t3.medium is roughly equivalent to GCP n1-standard-2, but memory optimizations differ. Also, GCP's sustained-use discount kicks in automatically after 25% of the month, so price out a full month, not just a snapshot.
We built a script that parsed AWS Cost Explorer API data and fed it into the calculator using the pricing API. Saved a week of manual work. This Google dev discussion has a similar approach.
Step 2: Map Dependencies
This is where most projects fail. You think your app is standalone, but it talks to a Redis cluster in a different VPC, which uses an RDS read replica, which is tied to a Kinesis stream. Everything is tangled.
Use GCP's Migration Center (formerly Stratozone) for automated discovery. It scans your AWS environment and builds a dependency graph. In one case, it found 14 undocumented connections between services that our architecture diagram missed.
Step 3: Choose Your Migration Pattern
You have three options:
- Lift and shift (fastest, least value): use Migrate for Compute Engine
- Refactor (moderate, medium value): containerize with m4a, move databases with DMS
- Rebuild (slowest, highest value): rewrite apps as cloud-native GCP services
For most production systems, I recommend a hybrid. Lift the stable VMs first, rebuild the data pipeline later.
Step 4: Execute a Pilot Migration
Pick a non-critical service — a report generator, a stale API, anything with low traffic. Move it using the chosen tool. Measure everything: uptime, latency, cost. This is your learning phase. Expect to find 5–10 issues you didn't anticipate.
Step 5: Full Cutover with a Rollback Plan
Use GCP's Traffic Director or a DNS-based switchover to shift traffic gradually. We've used weighted DNS records, shifting 10%, then 30%, then 100% over three days. If latency spikes, dial back and debug.
Always keep the old AWS environment running for at least one billing cycle. You'll need it for rollback and data reconciliation.
What Most Tutorials Won't Tell You: The Pain Points
Networking Is a Nightmare
AWS VPC and GCP VPC are conceptually similar but totally incompatible. Subnet routing, security groups vs. firewall rules, and peering across projects are all different. I spent three days debugging a BGP route that worked in one region but not another.
Solution: Use GCP's Network Connectivity Center or Dedicated Interconnect for hybrid connectivity. Don't rely on VPN unless latency can tolerate 20–30ms jitter.
IAM: It's Not Just Different, It's a Different Philosophy
AWS uses resource-based policies and role-based trust policies. GCP uses resource-based IAM roles globally. The mental shift is real.
Example: In AWS, you attach a role to an EC2 instance to allow it to read S3. In GCP, you attach a role to the service account that the VM runs as. Sounds minor, but it changes how you audit permissions. We migrated a 500-role AWS setup to GCP and ended up with 200 service accounts and 40 custom roles. Simpler, but took a week to design.
Service Mapping Is Non-Trivial
AWS DynamoDB → GCP Firestore or Bigtable? AWS Kinesis → GCP Pub/Sub? AWS Lambda → GCP Cloud Functions or Cloud Run? The handshake isn't one-to-one.
For our mmwave material classification radar tutorial, we had been using AWS Kinesis to stream radar signal data from edge devices. On GCP, we moved to Pub/Sub + Dataflow. The latency improved by 40ms because of GCP's global networking backbone. But the API changes required updating the edge code.
Real-World How-To: Moving a Data Pipeline
Let me show you concrete code. Here's a common pattern: an AWS Lambda function that processes S3 events and writes to DynamoDB.
AWS Original:
python
import boto3
s3 = boto3.client('s3')
dynamo = boto3.resource('dynamodb')
def handler(event, context):
for record in event['Records']:
bucket = record['s3']['bucket']['name']
key = record['s3']['object']['key']
obj = s3.get_object(Bucket=bucket, Key=key)
data = obj['Body'].read().decode('utf-8')
table = dynamo.Table('radar-classifications')
table.put_item(Item={'key': key, 'data': data})
GCP Equivalent (Cloud Functions + Cloud Storage + Firestore):
python
from google.cloud import storage, firestore
storage_client = storage.Client()
firestore_client = firestore.Client()
def handle_gcs_event(event, context):
bucket_name = event['bucket']
file_name = event['name']
bucket = storage_client.bucket(bucket_name)
blob = bucket.blob(file_name)
data = blob.download_as_string().decode('utf-8')
doc_ref = firestore_client.collection('radar-classifications').document(file_name)
doc_ref.set({'data': data})
Notice the differences: GCP passes bucket and file name differently (Cloud Storage event structure includes bucket and name). Firestore is document-based, not item-based. You'll need to refactor error handling — GCP Cloud Functions retry up to 3 times by default, which can cause duplicate writes if your logic isn't idempotent.
For streaming, here's an mmwave data ingestion pipeline:
AWS Kinesis Producer:
python
import boto3
kinesis = boto3.client('kinesis')
def send_radar_data(reading):
kinesis.put_record(StreamName='radar-stream', Data=reading, PartitionKey='sensor1')
GCP Pub/Sub Producer:
python
from google.cloud import pubsub_v1
publisher = pubsub_v1.PublisherClient()
topic_path = publisher.topic_path('my-project', 'radar-topic')
def send_radar_data(reading):
future = publisher.publish(topic_path, data=reading.encode('utf-8'))
future.result()
The key difference: Pub/Sub doesn't have partition keys — it uses ordering keys if needed. And the publishing call is async; you must call result() to block (or handle futures properly).
Cost Comparison: What The Numbers Actually Say in 2026
I've pulled data from multiple sources to give you a real comparison. According to GCP vs AWS 2026 | Which Cloud Platform Is Better?, GCP compute instances are on average 20–35% cheaper than AWS equivalents for similar vCPU/memory profiles when using committed-use discounts (1-year term). Rackspace's analysis shows that GCP storage costs for object storage (GCS vs S3) are about 15% lower for standard tier, but the real savings come from network egress — GCP charges $0.08/GB after first 1TB, while AWS charges $0.09/GB and has complex tiered pricing.
But hidden costs bite. LeanOps Tech's comparison points out that GCP's sustained-use discounts are automatic (good), but the base price for certain services like Cloud SQL is higher than RDS for small instances. EffectiveSoft's breakdown notes that for AI/ML workloads using GPUs, GCP's TensorFlow integration and preemptible pricing can cut costs by 60% compared to AWS.
The key takeaway: don't run a generic comparison. Run your own. Use the Google Cloud Pricing Calculator against your actual usage data.
mmwave Material Classification Radar Tutorial — Why I Bring It Up
You might think radar data classification is niche. It's not. Many manufacturing, automotive, and security companies are building mmwave sensing pipelines. At SIVARO, we helped a client migrate an entire mmwave material classification system from AWS to GCP because GCP's Vertex AI offered better integration with their custom TensorFlow models and lower-cost TPU training. The how to migrate from aws to gcp step by step process we followed for that project became the template for all subsequent migrations. If you're working on sensor data, note that GCP's IoT Core (now integrated into Pub/Sub) handles device ingestion natively, and Cloud Functions can trigger retraining pipelines on new data.
FAQ
Q1: What is the best migrate from AWS to GCP migration tool for large VM fleets?
A: Migrate for Compute Engine (Velostrata). It handles bulk migration, incremental sync, and automated testing. We used it to move 800 VMs in one go with 99.9% success.
Q2: How do I estimate costs before migrating?
A: Use the Google Cloud Pricing Calculator with your AWS resource metrics. GCP's sustained-use discounts are automatic — make sure you include them. Also account for network egress costs during the migration itself.
Q3: Can I migrate running databases without downtime?
A: Yes, using Database Migration Service (DMS) with continuous replication. We achieved sub-minute downtime for a 2TB PostgreSQL database. Plan a maintenance window and test the cutover procedure.
Q4: What about Kubernetes workloads?
A: Use Migrate for Anthos (m4a) to analyze and containerize existing apps. For clusters already running on EKS, GKE Autopilot is almost a drop-in replacement, but you'll need to rewrite ingress configs.
Q5: How long does a typical migration take?
A: Depends on size. A medium (100 VMs, 10 databases, moderate customizations) migration takes 4–6 weeks from planning to full cutover. The first 2 weeks are assessment and cost modeling — don't skip them.
Q6: What's the biggest hidden cost of migration?
A: Data egress from AWS. Moving data out of S3 costs $0.09/GB after the first 1TB. For 50TB, that's $4,500 in egress fees alone. Use GCP's Transfer Appliance for large datasets to reduce this.
Q7: Do I need to change my CI/CD pipeline?
A: Yes. Cloud Build is different from CodePipeline. You'll also need to update IAM service accounts. Budget a week for CI/CD reconfiguration.
Q8: Is GCP better for AI workloads?
A: In 2026, yes — especially if you use TensorFlow or JAX. Vertex AI training costs about 20% less than SageMaker, and TPU availability is exclusive to GCP. For mmwave material classification radar tutorials, GCP's Dataflow and BigQuery made the analytics pipeline 3x faster than our AWS setup.
Final Thoughts
Migration isn't a one-time project. It's a forcing function to clean up your infrastructure. When we finished our first AWS-to-GCP migration in 2023, we had removed 40% of obsolete resources along the way. The migrate from AWS to GCP migration tool you choose matters less than the willingness to treat the move as an audit of your current architecture.
Start small. Pick a low-risk service. Use the Google Cloud Pricing Calculator to build conviction. And never trust a migration timeline that doesn't account for debugging IAM policies.
If you're working on data pipelines or AI systems — especially something like an mmwave material classification radar tutorial — the tooling and pricing advantages of GCP are real. But they only matter if you execute the migration with discipline.
Go move something.
Nishaant Dixit — Founder of SIVARO. Building data infrastructure and production AI systems since 2018. Built systems processing 200K events/sec.