At 2:14 AM on a Tuesday, our p99 API latency spiked to 4.7 seconds, AWS EC2 m5.2xlarge spot instance prices had surged 220% in 3 months, and our compute bill was eating 38% of our total cloud spend. We didn’t just need a cost cut—we needed a full infrastructure reset. Six months later, we’d migrated 82 production EC2 instances to GCP Axion Arm-based instances, cut compute costs by 40%, reduced p99 latency by 62%, and didn’t drop a single request during the cutover.
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Key Insights
- GCP Axion T2A instances deliver 2.1x better price/performance than AWS m5.2xlarge for compute-heavy Go workloads
- We used Terraform 1.6.0, Packer 1.9.4, and the GitHub Actions runner (https://github.com/actions/runner) for migration automation
- Total compute cost reduction: 40% ($28k/month to $16.8k/month) with no increase in instance count
- By 2026, 60% of cloud compute workloads will run on Arm-based instances, up from 12% in 2023
Why We Left AWS EC2
For 3 years, AWS EC2 had been our compute backbone. We ran 82 instances across us-east-1 and us-west-2, mostly m5 and c5 families, handling real-time analytics workloads for 1,200 enterprise customers. Our stack was standard: Go 1.21 for backend services, PostgreSQL 16 for transactional data, Redis 7.2 for caching, Kafka 3.6 for event streaming. It worked, until Q3 2023, when three trends collided:
- AWS spot instance prices for m5.2xlarge surged 220% in 3 months, from $0.1152 to $0.368 per hour, with 4 out-of-bid terminations causing 14 minutes of cumulative downtime.
- Our workload grew 35% quarter-over-quarter, pushing our monthly EC2 bill to $28k, 38% of our total cloud spend.
- We noticed that our Go binaries were only utilizing 42% of x86 vCPU capacity, due to inefficient scheduling and larger cache miss rates compared to Arm architectures we’d read about.
We evaluated three options: reserved instances on AWS (15% savings, still x86), migrating to AWS Graviton3 (25% savings), or moving to GCP Axion (40% savings per GCP’s pricing sheet). We chose Axion because GCP offered a 30-day free trial of T2A instances, and our initial benchmarks showed 18% better per-core performance for our Go workloads.
Benchmarking AWS vs GCP Axion
Before committing to a migration, we provisioned 4 test instances: 2 AWS m5.2xlarge (us-east-1) and 2 GCP T2A standard-8 (us-central1). We ran 72 hours of production traffic replay, measuring p99 latency, throughput, and memory usage. The results surprised us:
Instance Type
Architecture
vCPU
RAM (GB)
On-Demand $/hr
Spot $/hr (Avg 3mo)
Go Workload Ops/sec
Price/Performance ($/1k ops)
AWS m5.2xlarge
x86_64 (Intel Xeon Platinum 8175)
8
32
$0.384
$0.1152 → $0.368 (surge)
12,400
$0.031
AWS c5.2xlarge
x86_64 (Intel Xeon Platinum 8124)
8
16
$0.34
$0.102 → $0.312 (surge)
13,100
$0.026
GCP T2A standard-8
Arm (Google Axion 1st Gen)
8
32
$0.2688
$0.0806
14,600
$0.018
GCP T2A standard-16
Arm (Google Axion 1st Gen)
16
64
$0.5376
$0.1612
28,900
$0.019
The price/performance gap was undeniable: T2A instances delivered 42% better price/performance than m5.2xlarge, even before factoring in GCP’s 30% sustained use discount. We also found that Axion’s larger L2 cache (1MB per core vs 256KB on Intel Xeon) reduced GC pause times in Go by 28%, a major factor in our latency-sensitive workloads.
Migration Implementation
We split the migration into 4 phases: compatibility testing, infrastructure-as-code setup, traffic shifting, and decommissioning. Here’s the code we used for each phase.
1. Terraform Provisioning for GCP Axion
# Terraform 1.6.0 configuration for GCP Axion T2A instance provisioning
# Includes auto-scaling, health checks, and cost allocation tags
terraform {
required_version = ">= 1.6.0"
required_providers {
google = {
source = "hashicorp/google"
version = "~> 5.0"
}
}
}
# Variables for environment-specific configuration
variable "env" {
type = string
description = "Deployment environment (prod, staging, dev)"
validation {
condition = contains(["prod", "staging", "dev"], var.env)
error_message = "Env must be one of: prod, staging, dev."
}
}
variable "project_id" {
type = string
description = "GCP project ID"
default = "acme-analytics-prod"
}
variable "region" {
type = string
description = "GCP region for deployment"
default = "us-central1"
}
# Enable required GCP APIs
resource "google_project_service" "compute" {
project = var.project_id
service = "compute.googleapis.com"
disable_on_destroy = false
}
# Provision T2A instance template with Axion processor
resource "google_compute_instance_template" "t2a_go_worker" {
name = "t2a-go-worker-${var.env}-${timestamp()}"
description = "Axion T2A instance template for Go analytics workers"
project = var.project_id
region = var.region
machine_type = "t2a-standard-8" # 8 vCPU, 32GB RAM Axion instance
tags = ["go-worker", var.env, "axion"]
# Boot disk with optimized image for Arm
disk {
source_image = "debian-cloud/debian-12-arm64"
auto_delete = true
boot = true
disk_size_gb = 50
disk_type = "pd-ssd"
}
# Network interface with static IP for prod
network_interface {
network = "default"
access_config {
# Only assign public IP for non-prod environments
count = var.env == "prod" ? 0 : 1
}
}
# Startup script to install Go runtime and app
metadata_startup_script = <<-SCRIPT
#!/bin/bash
set -euo pipefail
apt-get update -y
apt-get install -y golang-1.21 redis-tools
export PATH=$PATH:/usr/lib/go-1.21/bin
# Download and install app binary from GCS
gsutil cp gs://acme-analytics-binaries/go-worker-v1.2.3-linux-arm64 /usr/local/bin/go-worker
chmod +x /usr/local/bin/go-worker
# Start worker as systemd service
cat > /etc/systemd/system/go-worker.service <<-EOF
[Unit]
Description=Go Analytics Worker
After=network.target
[Service]
User=root
ExecStart=/usr/local/bin/go-worker --env ${var.env}
Restart=always
[Install]
WantedBy=multi-user.target
EOF
systemctl enable go-worker
systemctl start go-worker
SCRIPT
# Service account with least privilege
service_account {
email = "go-worker-sa@${var.project_id}.iam.gserviceaccount.com"
scopes = ["cloud-platform"]
}
# Lifecycle rule to prevent accidental deletion in prod
lifecycle {
create_before_destroy = true
prevent_destroy = var.env == "prod"
}
}
# Auto-scaling group for T2A workers
resource "google_compute_region_autoscaler" "t2a_autoscaler" {
name = "t2a-go-worker-autoscaler-${var.env}"
project = var.project_id
region = var.region
target = google_compute_region_instance_group_manager.t2a_ig_mgr.self_link
autoscaling_policy {
max_replicas = var.env == "prod" ? 40 : 10
min_replicas = var.env == "prod" ? 20 : 2
cooldown_period = 60
cpu_utilization {
target = 0.7
}
}
}
# Instance group manager to manage T2A instances
resource "google_compute_region_instance_group_manager" "t2a_ig_mgr" {
name = "t2a-go-worker-ig-${var.env}"
project = var.project_id
region = var.region
version {
instance_template = google_compute_instance_template.t2a_go_worker.self_link
}
base_instance_name = "t2a-go-worker"
target_size = var.env == "prod" ? 20 : 2
}
2. Cross-Architecture Benchmark Tool
// bench_arm_x86.go: Benchmark compute-heavy workloads across architectures
// Compile: go build -o bench_arm_x86 -ldflags "-s -w" bench_arm_x86.go
// Run: ./bench_arm_x86 --iterations 100000 --workload json-parse
package main
import (
"bytes"
"compress/gzip"
"context"
"crypto/crc32"
"encoding/json"
"flag"
"fmt"
"os"
"os/signal"
"runtime"
"syscall"
"time"
"github.com/prometheus/client_golang/prometheus"
"github.com/prometheus/client_golang/prometheus/promauto"
)
// Workload types supported
type workloadType string
const (
workloadJSONParse workloadType = "json-parse"
workloadCRC32 workloadType = "crc32"
workloadCompress workloadType = "compress"
)
var (
iterations = flag.Int("iterations", 100000, "Number of benchmark iterations")
workload = flag.String("workload", "json-parse", "Workload type: json-parse, crc32, compress")
arch = flag.String("arch", runtime.GOARCH, "Architecture (amd64, arm64)")
opsCounter = promauto.NewCounterVec(prometheus.CounterOpts{
Name: "bench_ops_total",
Help: "Total benchmark operations completed",
}, []string{"workload", "arch"})
latencyHist = promauto.NewHistogramVec(prometheus.HistogramOpts{
Name: "bench_op_latency_ms",
Help: "Operation latency in milliseconds",
Buckets: prometheus.DefBuckets,
}, []string{"workload", "arch"})
)
// Sample JSON payload for parsing benchmark
var sampleJSON = []byte(`{"user_id": 12345, "event_type": "page_view", "timestamp": "2024-03-15T14:22:00Z", "metadata": {"page": "/dashboard", "referrer": "google.com", "duration_ms": 4500}}`)
func main() {
flag.Parse()
ctx, cancel := signal.NotifyContext(context.Background(), syscall.SIGINT, syscall.SIGTERM)
defer cancel()
// Validate workload input
switch workloadType(*workload) {
case workloadJSONParse, workloadCRC32, workloadCompress:
// valid
default:
fmt.Fprintf(os.Stderr, "invalid workload: %s. Must be one of: json-parse, crc32, compress\n", *workload)
os.Exit(1)
}
fmt.Printf("Starting benchmark: arch=%s, workload=%s, iterations=%d\n", *arch, *workload, *iterations)
start := time.Now()
// Run selected workload
switch workloadType(*workload) {
case workloadJSONParse:
runJSONParseBench(ctx, *iterations)
case workloadCRC32:
runCRC32Bench(ctx, *iterations)
case workloadCompress:
runCompressBench(ctx, *iterations)
}
elapsed := time.Since(start)
opsPerSec := float64(*iterations) / elapsed.Seconds()
fmt.Printf("Benchmark complete: %d ops in %v, %.2f ops/sec\n", *iterations, elapsed, opsPerSec)
opsCounter.WithLabelValues(*workload, *arch).Add(float64(*iterations))
}
// runJSONParseBench parses sample JSON repeatedly
func runJSONParseBench(ctx context.Context, n int) {
var data map[string]interface{}
for i := 0; i < n; i++ {
select {
case <-ctx.Done():
fmt.Println("benchmark cancelled")
return
default:
start := time.Now()
if err := json.Unmarshal(sampleJSON, &data); err != nil {
fmt.Fprintf(os.Stderr, "json parse error: %v\n", err)
os.Exit(1)
}
latencyHist.WithLabelValues("json-parse", *arch).Observe(float64(time.Since(start).Milliseconds()))
}
}
}
// runCRC32Bench computes CRC32 checksums repeatedly
func runCRC32Bench(ctx context.Context, n int) {
payload := make([]byte, 1024) // 1KB payload
for i := 0; i < n; i++ {
select {
case <-ctx.Done():
fmt.Println("benchmark cancelled")
return
default:
start := time.Now()
crc32.ChecksumIEEE(payload)
latencyHist.WithLabelValues("crc32", *arch).Observe(float64(time.Since(start).Milliseconds()))
}
}
}
// runCompressBench runs gzip compression repeatedly
func runCompressBench(ctx context.Context, n int) {
payload := make([]byte, 4096) // 4KB payload
for i := 0; i < n; i++ {
select {
case <-ctx.Done():
fmt.Println("benchmark cancelled")
return
default:
start := time.Now()
var buf bytes.Buffer
gz := gzip.NewWriter(&buf)
if _, err := gz.Write(payload); err != nil {
fmt.Fprintf(os.Stderr, "compress error: %v\n", err)
os.Exit(1)
}
if err := gz.Close(); err != nil {
fmt.Fprintf(os.Stderr, "gzip close error: %v\n", err)
os.Exit(1)
}
latencyHist.WithLabelValues("compress", *arch).Observe(float64(time.Since(start).Milliseconds()))
}
}
}
3. Gradual Traffic Migration Script
// migrate_aws_gcp.go: Gradual traffic migration from AWS EC2 to GCP Axion
// Uses weighted round-robin load balancing with automatic rollback on error
// Compile: go build -o migrate_aws_gcp migrate_aws_gcp.go
package main
import (
"context"
"crypto/tls"
"errors"
"flag"
"fmt"
"log"
"net/http"
"os"
"os/signal"
"syscall"
"time"
"github.com/aws/aws-sdk-go/aws"
"github.com/aws/aws-sdk-go/aws/session"
"github.com/aws/aws-sdk-go/service/elbv2"
"github.com/prometheus/client_golang/prometheus"
"github.com/prometheus/client_golang/prometheus/promauto"
)
var (
awsRegion = flag.String("aws-region", "us-east-1", "AWS region")
gcpRegion = flag.String("gcp-region", "us-central1", "GCP region")
targetWeight = flag.Int("target-weight", 100, "Target GCP traffic weight (0-100)")
step = flag.Int("step", 10, "Weight increment per step")
interval = flag.Duration("interval", 5*time.Minute, "Interval between weight steps")
healthPath = flag.String("health-path", "/healthz", "Health check path")
)
var (
migrationStep = promauto.NewGauge(prometheus.GaugeOpts{
Name: "migration_step_current",
Help: "Current migration step (0 = all AWS, 100 = all GCP)",
})
healthCheckErrors = promauto.NewCounterVec(prometheus.CounterOpts{
Name: "migration_health_check_errors_total",
Help: "Total health check errors by target",
}, []string{"target"})
)
type targetGroup struct {
name string
endpoint string
weight int
}
func main() {
flag.Parse()
ctx, cancel := signal.NotifyContext(context.Background(), syscall.SIGINT, syscall.SIGTERM)
defer cancel()
// Validate inputs
if *targetWeight < 0 || *targetWeight > 100 {
log.Fatalf("target-weight must be between 0 and 100")
}
if *step <= 0 || *step > 100 {
log.Fatalf("step must be between 1 and 100")
}
// Initialize AWS session
awsSess, err := session.NewSession(&aws.Config{
Region: aws.String(*awsRegion),
})
if err != nil {
log.Fatalf("failed to create AWS session: %v", err)
}
elbClient := elbv2.New(awsSess)
// Define target groups (simplified for example)
awsTG := targetGroup{name: "aws-ec2-tg", endpoint: "https://ec2.acme-analytics.com", weight: 100}
gcpTG := targetGroup{name: "gcp-axion-tg", endpoint: "https://axion.acme-analytics.com", weight: 0}
log.Printf("Starting migration: target GCP weight %d%%, step %d%%, interval %v", *targetWeight, *step, *interval)
// Run migration steps
currentWeight := 0
for currentWeight < *targetWeight {
select {
case <-ctx.Done():
log.Println("Migration cancelled, rolling back to AWS only")
updateWeights(elbClient, 100, 0)
return
default:
}
// Calculate next weight
nextWeight := currentWeight + *step
if nextWeight > *targetWeight {
nextWeight = *targetWeight
}
// Health check both targets
log.Printf("Step %d: shifting %d%% traffic to GCP", nextWeight/(*step), nextWeight)
if err := healthCheck(awsTG.endpoint); err != nil {
log.Printf("AWS health check failed: %v, aborting", err)
healthCheckErrors.WithLabelValues("aws").Inc()
return
}
if err := healthCheck(gcpTG.endpoint); err != nil {
log.Printf("GCP health check failed: %v, rolling back", err)
healthCheckErrors.WithLabelValues("gcp").Inc()
updateWeights(elbClient, 100, 0)
return
}
// Update load balancer weights
if err := updateWeights(elbClient, 100-nextWeight, nextWeight); err != nil {
log.Printf("Failed to update weights: %v, rolling back", err)
updateWeights(elbClient, 100, 0)
return
}
migrationStep.Set(float64(nextWeight))
currentWeight = nextWeight
log.Printf("Current weights: AWS %d%%, GCP %d%%", 100-nextWeight, nextWeight)
// Wait for next step
time.Sleep(*interval)
}
log.Println("Migration complete: 100% traffic to GCP Axion")
}
// healthCheck verifies a target is healthy
func healthCheck(endpoint string) error {
client := &http.Client{
Timeout: 5 * time.Second,
Transport: &http.Transport{
TLSClientConfig: &tls.Config{InsecureSkipVerify: true}, // Only for migration, disable in prod
},
}
resp, err := client.Get(fmt.Sprintf("%s%s", endpoint, *healthPath))
if err != nil {
return fmt.Errorf("health check request failed: %w", err)
}
defer resp.Body.Close()
if resp.StatusCode != http.StatusOK {
return fmt.Errorf("unexpected status code: %d", resp.StatusCode)
}
return nil
}
// updateWeights updates AWS ALB target group weights
func updateWeights(elbClient *elbv2.ELBV2, awsWeight, gcpWeight int) error {
// Note: In real implementation, this would update GCP target group weights too
// Simplified for example: update AWS TG weight
_, err := elbClient.ModifyTargetGroupAttributes(&elbv2.ModifyTargetGroupAttributesInput{
TargetGroupArn: aws.String("arn:aws:elasticloadbalancing:us-east-1:123456789012:targetgroup/aws-ec2-tg/123456"),
Attributes: []*elbv2.TargetGroupAttribute{
{
Key: aws.String("weight"),
Value: aws.String(fmt.Sprintf("%d", awsWeight)),
},
},
})
if err != nil {
return fmt.Errorf("failed to update AWS TG weight: %w", err)
}
log.Printf("Updated weights: AWS=%d, GCP=%d", awsWeight, gcpWeight)
return nil
}
Case Study: Acme Analytics Real-Time Pipeline Migration
- Team size: 12 engineers (4 backend, 3 SRE, 2 data, 2 frontend, 1 EM)
- Stack & Versions: Go 1.21, PostgreSQL 16, Redis 7.2, Kafka 3.6, Terraform 1.6.0, Packer 1.9.4, GitHub Actions (https://github.com/actions/runner) for CI/CD
- Problem: AWS EC2 monthly compute bill was $28k (38% of total cloud spend), p99 API latency was 4.7s during peak, spot instance prices surged 220% in 3 months, m5.2xlarge on-demand utilization was only 42% due to x86 inefficiency for Go workloads
- Solution & Implementation: Migrated 82 EC2 instances (32 m5.2xlarge, 28 m5.4xlarge, 22 c5.2xlarge) to GCP Axion T2A instances (t2a-standard-8 and t2a-standard-16) using Terraform for provisioning, Packer for custom Arm images, gradual weighted traffic shifting over 14 days, with automatic rollback on health check failures
- Outcome: Compute costs cut by 40% to $16.8k/month, p99 latency reduced to 1.8s (62% improvement), spot instance price stability (no surges in 6 months), instance utilization increased to 78%, zero downtime during migration
Developer Tips
1. Validate Arm Compatibility Early With GOARCH=arm64 Builds
One of the biggest risks in migrating x86 to Arm workloads is untested architecture-specific code. For Go shops, this is simpler than C/C++ since Go has first-class Arm support, but we still hit two issues: a third-party Kafka client that used x86 assembly for checksumming, and a custom compression library that assumed 64-bit x86 pointer size. To avoid production outages, start validation the first week of planning: set GOARCH=arm64 in your CI pipeline, rebuild all binaries, and run unit tests under qemu-arm if you don’t have native Arm hardware. We added a multi-arch Docker build step to our GitHub Actions (https://github.com/actions/runner) workflow using docker/build-push-action, which builds both amd64 and arm64 images in parallel. For teams without native Arm instances, Google offers free f1-micro Arm instances in the GCP free tier for testing, or you can use Docker Desktop’s multi-arch emulation. Never assume your x86 code will run on Arm without testing—even one failing package can delay your migration by weeks. We found that 92% of our Go dependencies supported arm64 out of the box, but the remaining 8% required version bumps or replacements, which took 3 weeks to resolve before we started provisioning Axion instances.
# CI snippet to build multi-arch Go binaries
GOARCH=arm64 go build -ldflags "-s -w" -o bin/app-linux-arm64 ./cmd/app
GOARCH=amd64 go build -ldflags "-s -w" -o bin/app-linux-amd64 ./cmd/app
2. Use GCP Axion Spot Instances With Fixed-Price Commitments
AWS spot instance price volatility was the primary driver of our cost surge—m5.2xlarge spots went from $0.1152 to $0.368 per hour in 3 months, with 4 out-of-bid outages in that period. GCP Axion spot instances have been far more stable: t2a-standard-8 spots have averaged $0.0806 per hour for 6 months with zero outages. To maximize savings, combine spot instances with GCP’s 1-year or 3-year committed use discounts (CUDs), which apply to both on-demand and spot Axion instances. We purchased a 3-year CUD for 20 T2A standard-8 instances, which locked in $0.2016 per hour (24% off on-demand) for the entire term, even if spot prices rise. For teams with predictable workloads, GCP’s spot prepurchase program lets you reserve spot capacity at a fixed price for up to 1 year, eliminating the risk of bid price spikes entirely. We allocate 70% of our Axion fleet to spot instances with CUDs, 20% to on-demand with CUDs, and 10% to on-demand without commitments for burst capacity. This mix has kept our Axion compute costs flat for 6 months, even as our workload volume increased by 35%. Avoid the trap of treating Arm spots like x86 spots—Axion supply is far more consistent, so you can safely run stateful workloads on spots with proper checkpointing.
# Create a GCP Axion spot instance with CUD eligibility
gcloud compute instances create axion-spot-test \
--machine-type=t2a-standard-8 \
--image-project=debian-cloud \
--image=debian-12-arm64 \
--region=us-central1 \
--spot \
--maintenance-policy=terminate
3. Benchmark Workloads With Real Traffic, Not Synthetic Tests
We made the mistake early on of relying on synthetic CPU benchmarks (like Sysbench) to estimate Axion performance, which overstated price/performance by 22% compared to our production Go workload. Synthetic tests don’t account for real-world factors like GC pause times, network latency, and lock contention, which vary significantly between x86 and Arm architectures. Instead, replay production traffic to both x86 and Arm test instances and measure the metrics that matter to your business: p99 latency, error rate, and memory usage per request. We used a fork of the Go benchmark tool we wrote (https://github.com/acme-analytics/arm-bench) to replay 24 hours of production Kafka events to test instances, then compared p99 processing latency. This revealed that Axion instances had 18% lower p99 latency for our JSON-heavy workload, even though synthetic CPU tests only showed 12% improvement. For stateful workloads, run benchmarks for at least 72 hours to capture daily traffic patterns and GC behavior. We also found that Arm’s larger L2 cache reduced lock contention in our Redis client by 31%, a factor that synthetic benchmarks completely missed. Never approve a migration based on vendor-provided benchmarks—run your own workload on test instances for at least a week before committing to a full cutover.
# Replay production traffic to Axion test instance
./arm-bench --replay-kafka \
--brokers=kafka.acme-analytics.com:9092 \
--topic=prod-events \
--duration=72h \
--target=https://axion-test.acme-analytics.com
Join the Discussion
We’ve shared our real-world experience migrating from AWS EC2 to GCP Axion, but every infrastructure migration has unique constraints. Whether you’re considering an Arm migration, evaluating GCP vs AWS for compute, or have already made the switch, we’d love to hear your lessons learned. Drop a comment below with your experience, and we’ll respond to every question.
Discussion Questions
- By 2027, will Arm-based instances become the default for cloud compute workloads, or will x86 remain dominant for legacy applications?
- Would you trade 10% higher latency for 40% lower compute costs in a non-customer-facing batch processing workload?
- How does AWS Graviton3 compare to GCP Axion for Go workloads, and would you choose one over the other for a new project?
Frequently Asked Questions
Does GCP Axion support all Linux distributions?
Yes, GCP provides official Arm-compatible images for Debian 12, Ubuntu 22.04 LTS, RHEL 9, and CentOS Stream 9, with community-maintained images for Alpine Linux and Arch Linux. We used Debian 12 Arm64 for all our Axion instances, which had full compatibility with our Go 1.21 runtime, Redis 7.2 client, and systemd service configurations. For custom images, Packer 1.9.4 supports building Arm images using the googlecompute builder with machine_type set to a T2A instance.
How long did the full migration take?
From initial planning to 100% traffic cutover, our migration took 14 weeks total. This included 3 weeks of Arm compatibility testing for our 140+ Go dependencies, 4 weeks of setting up Terraform 1.6.0 and Packer automation, 2 weeks of production workload benchmarking, and 5 weeks of gradual traffic shifting. Teams with simpler stateless workloads can complete the migration in 8-10 weeks, but we recommend allocating at least 2 weeks for rollback testing regardless of workload complexity.
Is GCP Axion available in all GCP regions?
As of March 2024, Axion T2A instances are available in 12 GCP regions: us-central1, us-east1, us-west1, europe-west1, europe-west4, asia-east1, asia-southeast1, australia-southeast1, southamerica-east1, and 3 additional regions launching in Q2 2024. All available regions support spot instances, committed use discounts, and regional auto-scaling for Axion instances. GCP has committed to making Axion available in all regions by the end of 2024.
Conclusion & Call to Action
After 15 years of building cloud infrastructure, I’ve seen countless "cost-saving" migrations that end up increasing operational overhead or reducing performance. Migrating from AWS EC2 to GCP Axion is the rare exception: we cut costs by 40%, improved performance, and reduced operational toil from spot instance outages. For teams running stateless, compute-heavy workloads (Go, Python, Java, Node.js) on x86 instances, Axion should be your first evaluation target. The Arm ecosystem has matured enough that compatibility risks are minimal, and the price/performance gap between x86 and Axion is too large to ignore. Start with a small test workload, run your own benchmarks, and scale up once you validate the savings. The cloud pricing model is shifting toward Arm, and early adopters will reap the majority of the cost benefits before competition drives prices up.
$11,200 Monthly compute savings for 82 migrated instances
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