Cloud migration promises numerous benefits, but it’s not without its challenges and hidden costs. This article draws on the experiences of seasoned professionals to reveal the often-overlooked aspects of moving to the cloud. From tackling infrastructure complexities to optimizing data transfer strategies, these insights will help organizations prepare for a smoother and more cost-effective cloud transition.
- Tackle Hidden Complexity in Cloud Infrastructure
- Compress Data to Reduce Transfer Fees
- Map Data Traffic Before Cloud Migration
- Model Costs Beyond Pricing Calculators
- Rethink Architecture to Minimize Data Egress
- Phase Migration to Address Data Complexities
- Budget for Staff Training in Cloud Systems
- Optimize Data Transfer with Strategic Planning
- Assess and Localize Data for Cost Efficiency
Tackle Hidden Complexity in Cloud Infrastructure
One of the most unexpected costs we see teams encounter during cloud migration—and one we faced ourselves—is the hidden complexity of unmanaged, undocumented infrastructure. These are the workloads, configurations, and resources that have drifted away from code or were never codified in the first place.
The impact isn’t just financial. It shows up as slower project delivery, increased cloud risk, and DevOps teams drowning in reactive tickets—trying to figure out what changed, who changed it, and how to bring it back under control.
How we solved it:
First, we audited the environment against our IaC to identify what was actually managed—and more importantly, what wasn’t. That surfaced a lot of drift and untracked resources we wouldn’t have caught manually.
From there, we focused on automating visibility and creating safe rollback points. We introduced daily snapshots of Terraform code, which gave our team the confidence to make changes knowing we could always revert if needed.
Over time, this shifted our culture from reactive to proactive. Instead of firefighting, we were back to building—knowing our infrastructure was consistently aligned with our intent.
Advice to Others:
1. Start with a gap analysis between your actual cloud and your Terraform code. You’ll likely be surprised by what you find.
2. Put safety nets in place early. Whether that’s versioned backups, policy gates, or drift alerts, having guardrails changes how teams work.
3. Treat infrastructure governance as an enabler, not overhead. It’s what allows velocity without sacrificing control.
4. Cloud migrations are never just technical—they reveal process gaps and cultural habits too. If you can build visibility and trust into the process, the rest becomes a lot more manageable.
Ori Yemini
CTO, ControlMonkey
Compress Data to Reduce Transfer Fees
When we migrated to the cloud, one surprise hit us harder than expected: data transfer fees. We were moving large volumes of data between services and regions, and those charges added up fast—faster than we’d budgeted for.
To get it under control, we compressed our data more aggressively and adjusted our transfer schedules to avoid peak times. It wasn’t flashy, but it worked. We brought the costs down significantly without sacrificing performance.
My advice to anyone planning a cloud migration is this: don’t just look at storage and compute; dig into the fine print on data egress and transfer costs. And once you’re live, monitor everything. A small inefficiency early on can escalate quickly. Catching it early saved us thousands and gave us a clearer picture of how to scale smarter.
Alexander De Ridder
Co-Founder & CTO, SmythOS(dot)com
Map Data Traffic Before Cloud Migration
One unexpected cost I encountered when migrating to the cloud was the surge in data transfer fees. Initially, I underestimated how much data we would be moving between cloud services and our on-premises systems, which led to higher-than-expected monthly bills.
To mitigate this, I conducted a thorough audit of our data flow and implemented stricter controls on unnecessary data transfers, optimizing our architecture to minimize movement between regions. I also worked with our cloud provider to switch to reserved data transfer plans, which offered better rates for our usage patterns.
Based on my experience, my advice to others is to carefully map out your data traffic before migrating and factor in transfer costs as part of your budget. Monitoring usage continuously after migration is crucial to catch any spikes early and adjust accordingly. Planning ahead can save a lot of unexpected expenses down the line.
Nikita Sherbina
Co-Founder & CEO, AIScreen
Model Costs Beyond Pricing Calculators
One unexpected cost we’ve seen clients encounter during cloud migrations is the expense tied to data egress and inter-service communication, especially when moving between regions or relying heavily on managed services. These costs often aren’t apparent in initial planning and can quickly add up post-migration.
To mitigate this, we help clients conduct a thorough cost modeling exercise before migration, factoring in data transfer patterns, usage spikes, and service dependencies. We also recommend grouping services within the same region and optimizing architecture to reduce unnecessary API calls or data movement.
Our advice: Don’t rely solely on pricing calculators. Work with your cloud partner or an experienced team to simulate real-world usage and build a cost-resilient architecture from day one.
Sergiy Fitsak
Managing Director, Fintech Expert, Softjourn
Rethink Architecture to Minimize Data Egress
Data transfer fees blindsided us. Everyone talks about how cheap storage is in the cloud, but nobody warns you that pulling your own data out — especially at scale — can rack up serious costs. We mitigated it by rethinking our architecture: caching more data locally, batching exports, and trimming what we actually needed to move. My advice? Don’t just look at storage pricing — dig into the fine print on egress fees. And talk to your cloud provider *before* you migrate, not after the bill shows up.
Justin Belmont
Founder & CEO, Prose
Phase Migration to Address Data Complexities
When we scaled Fulfill.com’s matching platform, one unexpected cost that blindsided us was data migration complexity. We significantly underestimated the resources required to properly transfer and normalize data from our legacy systems.
Our platform connects thousands of e-commerce brands with 3PL partners, processing massive datasets on warehousing capabilities, geographic service areas, and fulfillment performance metrics. What looked straightforward on paper became a labyrinth of interdependencies and edge cases.
The costs ballooned in three areas: extended developer time, consulting fees for specialized migration expertise, and most painfully, opportunity cost from delayed feature launches while we untangled data issues.
We mitigated this by implementing a phased migration approach. Instead of moving everything at once, we prioritized our core matching algorithm and customer-facing features first, then gradually migrated operational and analytics systems. This allowed us to maintain business continuity while addressing data complexities methodically.
We also invested in building automated validation tools that could verify data integrity across systems. These tools initially seemed like additional overhead, but they quickly paid dividends by catching inconsistencies early, preventing costly rework and customer-facing issues.
My advice? First, triple your time estimates for data migration. The complexity compounds with every integration point, and you’ll inevitably discover dependencies you didn’t know existed.
Second, budget for expertise. Our most effective decision was bringing in a specialized data migration consultant with logistics industry experience. They identified potential pitfalls we’d never have caught ourselves.
Third, focus on your core business capabilities first. In the 3PL world, customer experience and reliability can’t be compromised. Sequence your migration to protect what directly impacts your customers.
Finally, view this as an opportunity to clean house. We discovered duplicate records, outdated business rules, and manual workarounds that had accumulated over years. The migration became a forcing function to standardize our data model and eliminate technical debt.
The cloud ultimately delivered the scalability we needed to grow our 3PL matching platform, but the journey taught us that proper planning and phased implementation are critical to avoiding budget surprises and business disruption.
Joe Spisak
CEO, Fulfill(dot)com
Budget for Staff Training in Cloud Systems
One unexpected cost we faced when migrating our law firm to the cloud was the significant investment required for training and upskilling staff. Our employees were accustomed to working with paper, so transitioning to cloud-based systems was a substantial change. We underestimated the time and resources required, investing about $15,000 in training sessions, online courses, and temporary productivity dips over six months. Our solution was to engage with a training provider for bespoke workshops and create internal ‘cloud champions’ who could assist colleagues in returning to efficiency.
My advice to others is to budget for training early and treat it as a core part of the migration plan. I recommend budgeting at least 10-15% of your cloud budget for upskilling and getting some small, hands-on sessions under your belt so you know what you should do before fully going live. Demonstrate to staff with clear examples how the cloud saves time, for example, by providing the ability to work with case files remotely. This is an excellent way to ease them into it so they don’t push back and make the change easier.
Seann Malloy
Founder & Managing Partner, Malloy Law Offices
Optimize Data Transfer with Strategic Planning
The expense associated with data transfer is one unexpected cost I’ve encountered when migrating to the cloud. If a business is frequently moving or accessing data between different regions, it can add up quickly.
Here is how we mitigate the costs:
1. We conduct data assessment and optimization of the data that is going to be moved to the cloud. Identification of the data to be moved to the cloud, and the data that we can keep on our premises is crucial. Optimizing the data formats is also a key step.
2. We select the right cloud architecture, such as public, private, or hybrid, based on our needs. This also helps cut costs when frequently transferring large datasets.
3. We opt for colder storage options from the different pricing structures offered for various storage classes to reduce costs.
4. We keep the data localized with regular monitoring if using multiple cloud services to bring the cost down.
5. We plan and document the entire migration process and make the stakeholders aware of different cloud pricing models.
Dhari Alabdulhadi
CTO and Founder, Ubuy Peru
Assess and Localize Data for Cost Efficiency
One unexpected cost we encountered during our cloud migration was data egress charges. Initially, we focused on storage and compute pricing. What slipped through the cracks was the cost of moving data out of the cloud—especially during backups and syncs between environments.
To address this issue, we implemented two changes. First, we batched data transfers and scheduled them during off-peak hours to avoid unnecessary traffic. Second, we tightened access controls so that only essential services could initiate outbound data movement. This helped reduce both the volume and frequency of transfers, cutting costs without impacting daily operations.
If you’re planning a cloud migration, don’t overlook how your data moves. It’s not just about where it resides, but how often it travels and what that travel costs. Incorporate this into your planning early. It will save you from budget surprises later.
Vikrant Bhalodia
Head of Marketing & People Ops, WeblineIndia
