How much does S3 to Azure Blob replication cost in 2026?

Replicating object data from Amazon S3 to Azure Blob Storage is one of the most common cross-cloud data-plane patterns platform teams run, and it is also one of the most commonly mispriced. The short answer: for a typical enterprise workload moving tens of terabytes per month, expect to pay roughly $0.02–$0.09 per GB in egress plus API request charges on the AWS side, and $0.01–$0.02 per GB per month in storage on the Azure side, before you count the cost of the replication tooling itself. A 50 TB initial seed over AWS Direct Connect or an ExpressRoute circuit can run anywhere from about $1,000 (with committed-use discounts and free-tier allowances) to well over $4,500 at published on-demand rates. This article breaks down every component of that bill, why it accumulates the way it does, and where teams routinely overspend by 30–60%.

The Direct Answer: What You Actually Pay

Also worth reading: How does cross-cloud object storage replication work, and what's the best way to replicate S3 data across AWS, Azure, and GCP? · How do you optimize cross-cloud egress costs in a multi-cloud architecture? · What are the best cloud egress fee optimization strategies in 2026?

The dominant cost component is AWS S3 data transfer out to the internet or to another cloud provider. As of 2026, AWS charges approximately $0.09 per GB for the first 10 TB/month transferred out to the internet, dropping to roughly $0.085 per GB for the next 40 TB, $0.07 per GB from 50–150 TB, and lower tiers beyond that. Data transferred into Azure Blob Storage is free — Microsoft does not charge ingress — so the egress leg is where nearly all network cost lands. For a one-time migration of 100 TB at on-demand internet rates, that is roughly $8,000–$9,000 in pure egress before any tooling, storage, or operational overhead.

On top of egress, you pay S3 request costs: PUT, COPY, and LIST requests are billed per thousand. A full re-copy of a bucket with hundreds of millions of small objects can generate thousands of dollars in request charges alone, which surprises teams migrating telemetry or log data with millions of tiny files. Azure side, you pay Blob Storage capacity at roughly $0.018–$0.021 per GB per month for hot tier LRS, less for cool ($0.01/GB) and archive tiers (~$0.002/GB), plus transaction costs that are usually trivial relative to storage unless your workload is metadata-heavy.

There are three legitimate ways to reduce the headline number. First, AWS offers free egress up to 100 GB/month account-wide, which matters only for very small workloads. Second, if both endpoints sit inside the same physical region pair connected via private interconnect (for example, AWS us-east-1 and Azure East US), traffic routed over a private connection such as Direct Connect paired with ExpressRoute can be priced as interconnect data transfer out rather than internet egress, often cutting the rate meaningfully depending on your commitment tier. Third, third-party replication platforms and negotiated enterprise agreements can reduce effective per-GB rates by 20–50% at volume. None of these are automatic; each requires deliberate architecture decisions made before the first byte moves.

Why Cross-Cloud Replication Costs What It Does

Cloud providers price egress high for structural reasons, not arbitrary ones. Egress fees function as a switching cost between clouds; analysts have documented this dynamic since the early cloud era, and Flexera's 2026 cloud cost research continues to list data transfer among the top sources of wasted cloud spend, with many organizations reporting that data movement costs exceed their forecasts by double-digit percentages. When you move data from S3 to Azure Blob, AWS loses the storage revenue and Microsoft gains it, so the pricing asymmetry — expensive out of AWS, free into Azure — reflects each provider's incentive structure rather than underlying bandwidth cost, which is a fraction of a cent per GB at scale.

The second reason is architectural: there is no native, first-party replication service between S3 and Azure Blob. S3 Cross-Region Replication works only between S3 buckets, and Azure's object replication works only between Azure Storage accounts. Any cross-cloud copy therefore requires a third party doing the work — a managed replication SaaS, a self-hosted tool like Rclone or AWS DataSync running against an S3-compatible endpoint, or a custom pipeline on compute you pay for separately. That intermediary adds its own costs: compute hours, licensing, orchestration, and often a second set of API calls against both providers. NetApp's Cloud Volumes ONTAP, for instance, can present Fabric Pool tiering across both Amazon S3 and Azure Blob, but requires per-TB licensing alongside the underlying cloud storage charges — a reminder that hybrid-layer products stack their own fees on top of raw transfer.

Finally, cost scales non-linearly with object count, not just volume. Two buckets holding the same 10 TB can differ by 100x in replication cost if one holds ten 1 TB objects and the other holds ten million 1 MB files. Per-object overhead — LIST pagination, HEAD checks, PUT operations, retry storms on transient failures — compounds quickly. Teams that model cost purely on terabytes routinely see bills two to five times their estimate on small-object workloads.

Practical Steps: Planning and Executing a Cost-Controlled Replication

Start with a full inventory audit before quoting anything. Use S3 Storage Lens or an inventory report to capture total bytes, object counts, size distribution, and current storage class. Objects sitting in S3 Glacier or Glacier Deep Archive cannot be replicated directly; they must be restored first, and restore plus early-deletion fees on those classes can add $0.03–$0.10 per GB depending on retrieval speed and how long the restored copies live. Skipping this step is the single most common cause of blown migration budgets.

Second, choose your transport path deliberately. If you are moving more than roughly 50–100 TB one time, evaluate AWS Snowball Edge or Azure Data Box for the bulk seed even though they move data physically — a Snowball job for 80 TB costs a few hundred dollars versus $7,000+ in internet egress, and Azure Data Box inbound to Azure has no ingress charge on the receiving side. For ongoing incremental replication after the seed, a continuous pipeline over private interconnect or a discounted egress commitment makes sense. Third, right-size the tooling: Rclone is free but consumes your own compute and offers no built-in cost observability; commercial replication platforms charge typically $0.005–$0.02 per GB managed, which is cheap insurance at scale given they handle checksumming, retries, and bandwidth throttling that prevent accidental egress spikes.

Fourth, stage the cutover. Run an initial full sync, then incremental deltas, then a final delta during a low-traffic window before flipping readers. Each pass only transfers changed objects, so the second and third passes typically move under 5% of total volume. Finally, instrument everything from day one: tag the replication traffic, set AWS Budgets alerts on the egress line item, and review daily during active migration windows. Egress overruns are almost always silent until the invoice arrives.

Comparing Your Options: Native Tools, Open Source, and Managed Platforms

Self-hosted tooling looks free on paper and frequently ends up the most expensive option once you price engineer time and the cost of a misconfigured sync that re-copies a large prefix twice. Managed platforms earn their margin by absorbing exactly the failure modes — throttling, partial failures, consistency verification — that make DIY replication fragile. Note also that AWS DataSync, while excellent for Azure-to-S3 migrations (AWS documents this direction explicitly), is not designed for sustained S3-to-Azure replication; it is a migration accelerator, not a steady-state pipeline, and using it as one produces recurring agent-hour charges without delta efficiency.

A fourth option worth naming is avoiding the copy entirely. If the driver for replication is analytics access rather than residency requirements, query federation — letting Azure-side compute read S3 directly through external tables or open table formats like Iceberg and Delta Lake — eliminates most transfer cost at the price of per-query latency. Databricks' own architectural writing in 2026 pushes toward decoupled lakehouse layers precisely because duplicating data across clouds is expensive and creates consistency drift. Similarly, Snowflake customers routinely discover that cross-cloud/cross-region data transfer is a material line item in their bill, and redesigning around shared external stages beats brute-force replication. Replication should be the answer only when sovereignty, latency, or resilience genuinely require a second copy.

Common Mistakes That Inflate the Bill

The most expensive mistake is ignoring storage-class restore costs. Teams replicate a bucket snapshot, discover half the data was in Glacier, and pay retrieval plus interim Standard storage on restored objects while the copy runs. On a 200 TB archive-heavy bucket this alone can exceed $6,000. The second mistake is underestimating request charges on small objects: a bucket with 50 million objects incurs tens of millions of LIST and PUT operations per full pass, and at roughly $0.005 per thousand requests each way, a single verification-heavy pass can add four figures.

Third, teams forget that failed and retried transfers still consume egress. An unstable link or an aggressive parallelism setting that triggers S3 throttling can silently double transfer volume. Rate-limit the pipeline and use checksum-manifest-based resume rather than naive restarts. Fourth, many organizations leave data flowing after cutover — a forgotten producer still writing to S3 keeps the replication pipeline (and its egress bill) alive indefinitely. Decommission explicitly, with an owner and a date. Fifth, some teams assume Azure-to-AWS is symmetrically priced; it is not. Azure egress runs about $0.087 per GB at low tiers, so the reverse direction costs similarly, but bidirectional replication doubles everything — validate whether you truly need active-active or whether one-way with failover scripts suffices.

Finally, watch for the private-interconnect trap: Direct Connect and ExpressRoute have their own port-hour and data-transfer-out charges, and if your interconnect provider routes traffic through a public peering point rather than a direct path, you may still be billed at internet egress rates. Confirm the billing classification with both providers in writing before committing architecture to it.

When to Act and How to Sequence It

If you are reading this before a migration, act now on three things: run the inventory audit, get an egress quote or commitment from your AWS account team (enterprise discount programs routinely shave 20–40% off published transfer rates at committed volumes), and decide whether a physical seed is justified above roughly 50 TB. These decisions have long lead times — Snowball and Data Box jobs take one to three weeks door-to-door, and interconnect provisioning takes six to twelve weeks — so sequencing them late delays the project regardless of budget.

If replication is already running, audit within the next billing cycle. Pull the last 90 days of CUR (Cost and Usage Report) data filtered to the data transfer category, compare actual GB moved against what your pipeline reports, and investigate any gap larger than 10% — that gap is usually retries, verification reads, or forgotten producers. Re-baseline quarterly. Cross-cloud transfer pricing changes slowly but does change, and commitment discounts expire; a pipeline optimized in 2024 may be leaving money on the table in 2026.

For ongoing steady-state replication, revisit the build-versus-buy decision annually. If monthly volume exceeds roughly 20–30 TB, a managed platform's per-GB fee is usually offset by egress discounts it can negotiate and by avoided engineering time. Below that threshold, a well-tuned Rclone deployment on spot compute is hard to beat on cost, provided someone owns its reliability.

Bottom Line on Pricing

Budget conservatively at $0.09 per GB for AWS egress at on-demand rates, treat Azure ingress and storage as secondary line items, add 15–25% contingency for request charges, restores, and retries, and negotiate before you commit. A disciplined 100 TB migration with a physical seed and a managed incremental pipeline typically lands between $2,000 and $5,000 all-in; the same migration done naively over public internet endpoints with no planning regularly exceeds $12,000. The difference is not the cloud providers' pricing — it is whether the platform team treated replication as an engineered project or a checkbox.