Build vs Buy vs Self-Host: A TCO Model for Enterprise Analytics
The total cost of an analytics platform over three years is rarely dominated by the licence fee. For SaaS it is usually driven by event-volume growth; for self-hosted, by operational ownership; for building in-house, by the engineering years nobody costed at the start. Comparing licence prices is how organisations end up surprised in year two.
This model breaks the three options into the cost lines that actually move, so you can build a defensible number rather than a persuasive one. The figures below are structural rather than benchmarked — substitute your own.
The three options
Buy (SaaS). A vendor operates it. You pay a subscription, typically scaling with events, users, or seats.
Self-host. You operate vendor-built software in your own infrastructure. You pay a licence, or nothing for open-source editions, plus infrastructure and operational time.
Build. You construct a pipeline from components — collection, ingestion, storage, query, visualisation. No licence, maximum engineering.
Building is chosen far more often than it should be, usually because the first version is genuinely cheap. The cost arrives in years two and three when the person who built it has moved teams and the requirements have grown into a product.
The cost lines
| Line | Buy (SaaS) | Self-host | Build |
|---|---|---|---|
| Licence / subscription | Primary cost, scales with volume | Fixed or tiered; free for OSS editions | None |
| Infrastructure | Included | You pay — compute, storage, network | You pay, plus more of it |
| Initial deployment | Days | Days to weeks | 6–18 months of engineering |
| Ongoing operations | None | 0.1–0.5 FTE typical, more at scale | 1–3 FTE sustained |
| Feature development | Vendor's roadmap | Vendor's roadmap | Yours, forever |
| Upgrades / patching | Vendor | You | You |
| Compliance evidence | Vendor provides | You produce | You produce |
| Exit cost | Export dependent on vendor | You already hold the data | N/A |
| Scaling cost curve | Steepest — priced per event | Sub-linear — infrastructure scales better than pricing tiers | Sub-linear, plus engineering |
The line that decides most comparisons is the scaling curve. SaaS analytics is typically priced per event or per tracked user. Event volume grows with product usage, and product usage is the thing you are trying to increase. Self-hosted costs grow with infrastructure, which scales sub-linearly. The crossover point is where the comparison lives, and it moves earlier every time your product succeeds.
The lines people forget
Five, in descending order of how badly they distort the estimate.
Event volume growth. Model three years at your actual growth rate, not today's volume. A platform priced comfortably at 50 million events a month is a different conversation at 500 million.
Compliance work. In a regulated context, SaaS costs you DPIAs, transfer assessments, sub-processor reviews, and — under DORA, binding since 17 January 2025 — a documented and tested exit strategy. These are recurring hours, and they are usually charged to a compliance budget rather than the analytics line, which is precisely why they never appear in the comparison.
The parallel run. Any migration means paying for both platforms for 60–90 days. Budget it once and it disappears; forget it and it becomes an overrun.
Retention growth. Event storage compounds. A deployment correctly sized at launch is undersized at eighteen months unless retention policy was set deliberately.
Exit — and its expiry date. For SaaS, price the export while you are still a customer worth keeping. In the EU this line is shrinking on a schedule: the Data Act permits only directly incurred switching costs until 12 January 2027 and prohibits switching charges entirely from that date. Outside the EU, or before that date, it still belongs in the TCO.
A three-year worked comparison
Illustrative structure, not benchmark figures — substitute your own.
| Buy (SaaS) | Self-host | Build | |
|---|---|---|---|
| Year 1 | Subscription + integration | Licence + infra + deployment | Engineering build + infra |
| Year 2 | Subscription at grown volume | Licence + infra + 0.2 FTE | 1.5 FTE + infra |
| Year 3 | Subscription at grown volume | Licence + infra + 0.2 FTE | 1.5 FTE + infra |
| Plus | Compliance hours, exit cost | Compliance evidence production | Everything, indefinitely |
The pattern that emerges consistently: buy wins on year one, self-host wins by year three at scale, and build almost never wins unless analytics is your product.
Which should you choose?
Buy when volume is modest and predictable, you have no infrastructure capability, compliance requirements are light, and speed matters most.
Self-host when event volume is large or growing fast, you have infrastructure capability, or you have sovereignty, residency, or exit-strategy requirements that SaaS satisfies only contractually.
Build when analytics is your product, or you have a genuinely unmet requirement no platform serves — and you have costed three years of sustained engineering rather than the first release.
The honest failure mode for self-hosting is choosing it on TCO and then not staffing it. An unmaintained deployment costs less on paper and more in reality.
Frequently asked questions
Is self-hosted analytics cheaper than SaaS? At low volume, usually not — SaaS is cheap and self-hosting has a fixed operational floor. At high volume, usually yes, because SaaS pricing scales with events while infrastructure scales sub-linearly. The crossover depends on your volume and your cost of engineering time.
How much does it cost to run self-hosted analytics? Infrastructure sized for peak ingestion plus retention, and operational ownership typically between a tenth and a half of an engineer for mid-sized deployments. Enterprise scale with high availability requirements needs dedicated ownership.
Should we build our own analytics platform? Rarely. It is defensible if analytics is your product or you have a requirement no platform meets. Otherwise you are committing to sustained engineering to reproduce something that already exists.
What is usually missing from analytics TCO comparisons? Event volume growth, compliance and audit hours, the parallel-run period during migration, retention growth, and the cost of exit.
Do EU rules change the exit-cost calculation? Yes. The EU Data Act limits cloud switching charges to directly incurred costs until 12 January 2027 and prohibits them entirely from that date, which removes a line that has historically deterred migrations.
How do you compare analytics platforms on cost fairly? Model three years at projected volume, include infrastructure and operational time for self-hosted, include compliance hours for SaaS, and include exit cost for both.
Where to go next
- Deployment: Choosing an Analytics Deployment Model
- Exit: Escaping Analytics Vendor Lock-In
- Repatriation: EU Data Repatriation
Countly is a first-party product analytics and customer engagement platform that runs self-hosted, on-premises, or in a private cloud.
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