How to cut multicloud egress bills without breaking latency for customer-facing apps

I used to dread the monthly cloud bill email. A spike in egress charges would arrive like clockwork after a product launch, blamed on "unexpected traffic" or "downloads from Region X." Over the years I've helped several engineering teams rework their multicloud topology so that customer-facing apps keep the same latency while the finance team stops fainting at every invoice. In this article I’ll walk you through practical, tested ways to cut multicloud egress bills without degrading user experience.

Understand what you’re actually paying for

The first mistake teams make is guessing where the money goes. Egress is not a single line item — it’s a function of volume, region, protocol, and where traffic crosses cloud provider boundaries. I always start by answering three questions:

• Which traffic leaves a provider’s network (egress) vs stays internal (intra-zone or intra-region)?
• Which flows cross provider boundaries (AWS ↔ GCP, Azure ↔ CDN, etc.)?
• What protocols and sizes dominate that traffic (video, images, API payloads)?

Tagging resources, enabling VPC flow logs (AWS), VPC Flow Logs (GCP), or Network Watcher (Azure), and exporting them to a central analytics stack quickly reveals the big spenders. Don’t forget to include CDN logs and load balancer logs: they often hide significant egress.

Map traffic flows — then optimise the topology

Once you know the flows, draw them. I sketch a simple diagram showing regions, clouds, CDNs, and customer exit points (browsers, mobile apps). This reveals obvious anti-patterns: e.g., an origin in GCP serving customers primarily in Europe but routing through an AWS-hosted ML inference endpoint in us-east-1, creating cross-cloud egress for every request.

Common topology fixes:

• Move data-to-compute closer: Place static assets and frequently-read data in the same cloud and region where the customers are served.
• Replicate read-only data intelligently: Use selective replication rather than full replication if data churn is low.
• Introduce an edge/CDN layer: Cache at POPs near users to prevent origin egress.
• Use cloud-native private links: Prefer internal provider connectivity (e.g., AWS PrivateLink, GCP VPC Peering) instead of public egress when possible.

Leverage CDNs and edge compute the right way

A CDN is the easiest egress-saver — but only when used properly. I’ve seen teams pay heavy egress because they bypassed the CDN for dynamic API calls or mis-configured cache-control headers.

• Cache aggressively for static assets: Images, JS bundles, fonts — set long cache TTLs and version using filenames so you can invalidate by deploy.
• Edge render when possible: Move SSR/edge functions (Cloudflare Workers, Fastly Compute@Edge, AWS Lambda@Edge) to generate personalized content closer to users, reducing back-and-forth to origin.
• Layer caches: Use a regional cache (e.g., an origin shield) between POPs and origin to reduce repeated origin hits.

Be mindful of CDN provider pricing. Cloudflare and Fastly charge egress too, but their POPs reduce long-haul cross-cloud egress dramatically.

Use private interconnects for heavy cross-cloud traffic

If you have regular, high-volume transfers between clouds or between an on-prem datacenter and cloud, public internet egress can be expensive. I recommend evaluating private interconnects:

• Equinix Fabric, Megaport — these marketplace fabrics let you stitch clouds together without traversing the public internet.
• AWS Direct Connect, Google Cloud Interconnect, Azure ExpressRoute — use these links to route traffic over private circuits; often cheaper per GB at high volumes and more predictable latency.

Private connections carry a fixed monthly fee but lower per-GB rates and better latency. Do the math: if you move terabytes daily, an interconnect often pays for itself in weeks.

Regionalise services and prefer same-region interactions

Cloud providers commonly charge less (or nothing) for traffic within the same region or availability zone. I advise teams to:

• Host front-line customer services and customer data in the same region that serves those customers.
• Keep dependent services (auth, payment, recommendations) colocated with the frontend when possible.
• If global distribution is needed, replicate read replicas geo-locally but route writes to a central master or use conflict-free synced stores to limit cross-region writes.

Protocol, compression and payload engineering

Small wins add up. Optimise what you send over the wire.

• Use HTTP/2 or HTTP/3: Multiplexed streams reduce connection overhead for lots of small requests.
• Compress payloads: Brotli for text, efficient codecs for images (WebP/AVIF) and video (AV1 where supported).
• Binary protocols for APIs: Consider protobufs/gRPC for internal service-to-service traffic to reduce payload size and latency.
• Delta sync and pagination: Send only changed data; avoid full-object re-sends for mobile clients.
• Batch requests: Aggregate small requests server-side to reduce round trips and TCP/TLS overhead.

Smart caching and conditional responses

Use conditional GETs (If-Modified-Since / ETag) to avoid re-sending large resources unnecessarily. For APIs, implement caching layers like Redis or memcached to serve common queries without touching the origin. When you control clients (mobile apps), implement client-side caching and background sync so the app talks to the server less often.

Pricing-aware routing and CDN origin selection

Some CDNs and load balancers allow you to steer traffic based on cost heuristics. I once set up origin routing that directed European traffic to a local GCP origin while American traffic hit AWS — saving substantial egress fees because the origin was already colocated with the object store.

Config options to explore:

• Geo-aware DNS routing (Route 53 latency vs weighted policies).
• Edge workers that select origins based on client location and cost table.
• Use multi-origin CDNs and prefer origins that avoid cross-cloud hops.

Monitor, alert, and automate cost-control

Once you’ve implemented changes, guardrails are essential.

• Export cost and traffic data to a central observability platform (Prometheus + Grafana, Datadog, or cloud vendor cost exports).
• Create alerts for egress anomalies (sudden volume increases or route changes).
• Automate routing changes when cheap links are saturated (e.g., shift to a secondary CDN or interconnect).
• Run monthly “egress reviews” with engineering and finance to reassess hot paths.

Quick trade-off table

Checklist I use before shipping a new feature

• Have I mapped new traffic flows and measured expected egress per region?
• Can any data be cached at the edge or precomputed?
• Is the compute colocated with the data most users will access?
• Are we using efficient protocols and compression?
• Do we have alarms for egress anomalies?

Cutting multicloud egress bills is a mix of architecture, traffic engineering, and pragmatic product choices. You don't need to rip everything apart at once — pick the high-volume flows first, put caches and CDNs where they block origin hits, and consider private interconnects if you move massive datasets. Small payload and protocol optimizations yield perennial savings, and monitoring keeps surprises away. If you want, I can review a sketch of your traffic map and point out the lowest-effort, highest-impact changes.