A note for GEO Week attendees working with large-scale geospatial datasets
If you attended Geo Week, you already know: geospatial data is big. Really big. Point clouds from LiDAR captures. High-resolution aerial and satellite imagery. Dense 3D reality capture files. GIS layers spanning entire cities, watersheds, or transportation networks. The data that powers your work doesn’t just grow — it accumulates, and it needs to be accessible fast when you need it.
So here’s a question worth asking when you get back to the office: Is the way you’re storing all of that data actually working for your organization — or just for your department?
The Easy Button Has a Hidden Price Tag
It’s completely understandable how cloud storage becomes the default choice at the departmental level. You need storage, the vendor makes it easy to sign up, the initial costs look reasonable, and you’re up and running in hours. No procurement process. No IT ticket. No waiting.
But “easy to start” and “cost-effective at scale” are two very different things — and geospatial workflows have a way of making that gap painfully visible over time.
Here’s what tends to happen: Your team starts uploading datasets. Projects grow. You’re pulling full orthomosaic tiles to process locally. You’re retrieving LiDAR point clouds for analysis. You’re moving files between team members across regions. And suddenly, you’re not just paying for storage — you’re paying for every gigabyte you touch.
Egress fees. Retrieval fees. API transaction costs. Tiered access pricing. Data transfer charges between availability zones. These aren’t hypothetical line items. They’re what your finance team eventually finds when they audit the cloud bill — and they’re especially punishing for geospatial workflows where large file retrieval is the norm, not the exception.
Geospatial Data Has Specific Storage Needs That Cloud Wasn’t Designed For
Cloud storage is well-suited for a lot of things. Bursty, temporary workloads. Applications that need global distribution. Lightweight data that moves infrequently.
But geospatial data tends to be the opposite of all of that:
- It’s large by default. A single LiDAR mission can produce hundreds of gigabytes. A full aerial survey project may involve terabytes of raw captures, processed derivatives, and deliverable formats — all of which need to be retained.
- It gets retrieved repeatedly. Unlike archival data that sits untouched, geospatial datasets are actively queried, reprocessed, and referenced throughout a project lifecycle. Every cloud retrieval has a cost.
- It needs to move fast on your network. When you’re working with dense point clouds or large rasters, latency matters. Pulling a multi-gigabyte file from the cloud over a WAN connection, or waiting for retrieval from a cost-optimized cold storage tier, adds real friction to your workflows. On a local network, that same file transfers at full internal speeds, immediately, with no usage meter running.
- It needs long-term retention. Survey records, municipal GIS data, infrastructure documentation, environmental baselines – these datasets don’t disappear after the project closes. They’re retained for compliance, for historical comparison, for future reference. Paying monthly cloud rates on years of accumulating archival data adds up fast.
What On-Premises Storage Actually Looks Like for Your Team
The on-premises conversation often gets dismissed as “that’s an IT project” – and it is, in the best sense. Bringing IT into the storage decision isn’t a bureaucratic obstacle; it’s how your department gets infrastructure that’s sized, priced, and supported appropriately for the long term.
Here’s what that looks like in practice:
- Known cost from day one. On-premises storage is acquired at a fixed, negotiated price. You own it. There are no monthly usage meters, no surprise bills when project activity spikes, and no egress charges when you pull a large dataset for analysis. What you budget is what you spend.
- Predictable lifecycle. Nexsan enterprise storage systems are built for 7 to 10-year lifecycles with planned support and maintenance costs. That’s a storage decision you make once, budget for accurately, and don’t revisit every contract renewal cycle. For finance teams trying to forecast infrastructure costs over a multi-year horizon, that predictability has real value.
- Local network performance. Your team gets full internal network throughput with no throttling, no retrieval delays, no cold-tier latency. For geospatial workflows involving large file operations, this isn’t a minor convenience. It’s a material improvement in how fast your team can actually work.
- Capacity you can grow into. Nexsan systems are designed for straightforward scale-out expansion. Organizations can add substantial capacity within the same infrastructure framework, without replacing controllers, without redesigning the environment, and without triggering a forklift upgrade. Your initial investment remains intact as your data grows.
- No egress fees. Ever. This one is worth stating plainly. When your data lives on-premises, moving it between team members, between applications, and between processing stages costs nothing beyond the infrastructure you’ve already paid for.
The Cloud Cost Landscape Is Getting Less Predictable, Not More
If the economics above weren’t enough, the broader component pricing environment is making cloud cost forecasting harder than ever. Memory and storage components (DRAM, NAND flash, SSDs) are subject to significant price swings driven by AI infrastructure demand and global supply dynamics. Independent industry analysis has flagged memory chip price increases of up to 90% in early 2026 alone.
Cloud providers absorb those costs in their operations and, eventually, pass them on to customers. As a cloud storage customer, you don’t negotiate that exposure away, you inherit it. On-premises buyers who lock in hardware pricing at the point of purchase are shielded from that volatility for the life of the system.
For organizations planning 5-, 7-, or 10-year infrastructure strategies, the difference between a known cost and an unpredictable one isn’t just a budgeting inconvenience. It’s a strategic risk.
A Conversation Worth Having With IT
If your team has been managing geospatial storage at the departmental level, it may be time to bring IT into the conversation – not to slow things down, but to get to a better outcome.
IT can evaluate the total cost of ownership across the full data lifecycle, assess on-premises options that fit your capacity requirements, and put together a storage strategy that serves your team’s performance needs and your organization’s financial interests.
The question isn’t whether cloud storage has a role. For some workloads and use cases, it genuinely does. The question is whether the large-scale, frequently accessed, long-retained geospatial data you work with is a good fit for cloud-first storage economics. For most organizations handling that category of data, the honest answer is no.
Nexsan has been building enterprise-grade on-premises storage since 1999. Our platforms are designed for high-density, long-lifecycle deployments. Exactly the kind of infrastructure that makes sense for organizations managing substantial geospatial data over the long term.
If you’re evaluating your storage strategy, we’re happy to have that conversation. Contact sales@nexsan.com