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Build vs. buy

Should you build your own cloud & AI cost monitoring — or buy it?

"We'll just wire up Cost Explorer ourselves" is the first thing most engineering-led teams say. Enter your own numbers below and see the fully-loaded cost of doing that, versus a flat monthly plan.

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Estimate your build cost

What would it cost your team to build this?

Drag in your own numbers. Everything below computes in your browser — nothing you type here is sent anywhere.

4

AWS, Anthropic, other clouds, SaaS billing APIs

$110

Salary, benefits, and overhead, per hour

20

Ingestion framework, storage, scheduler, auth, dashboard shell

5

API integration, normalization, dedupe/idempotency, backfill, tests

12

Provider API changes, new sources, breakages, on-call, hours per month

Your build-vs-buy estimateUpdates live
Live
Year-one DIY cost$51,040
Ongoing cost / year$15,840
CostMon (Professional) / year$588
Year-one delta vs. CostMon+$50,452

At these numbers, building it yourself costs about 87× more in year one than CostMon Professional, and about 27× more every year after that just to keep it running.

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Every assumption in this estimate
  • A workday is 8 hours — build effort in days converts to hours at that rate before pricing.
  • Year-one DIY cost = one-time build (platform days + sources × days per source, in hours) plus a full year of ongoing maintenance, all at your entered hourly rate.
  • Ongoing cost / year = monthly maintenance hours × 12 × your entered hourly rate.
  • The CostMon figure is the Professional plan's list price ($49/ month), annualized — the same number shown on the pricing page, never a separate one.
  • This model is deliberately conservative: it excludes the cloud infrastructure (compute, storage, hosting) needed to actually run a DIY pipeline, and the opportunity cost of what that engineering time isn't spent on instead. Both would only widen the gap shown here.

About building your own cost pipeline

The line items a DIY estimate always misses

"We'll just call the Cost Explorer API" is true and also the smallest part of the job. A cost pipeline that's actually useful needs a place to land the data, a scheduler to pull it on a cadence, auth so not everyone can see raw billing detail, and a dashboard someone will actually open — the platform scaffolding underneath any single connector.

Then each provider adds its own tax: mapping its billing shape onto a normalized schema, deduplicating so a re-sync doesn't double-count, backfilling history for a new source, handling that provider's specific rate limits and pagination, and writing enough tests that the next change doesn't silently corrupt a month of numbers. Multiply that by every cloud, AI provider, and SaaS tool you want visibility into, and the honest total is usually several times the first estimate anyone gives in a planning meeting.

What changes underneath you after you ship it

A cost pipeline isn't done when it ships — it's done when someone stops having to think about it, and that day doesn't come. AWS adds new services and reshapes Cost Explorer's categories; Anthropic's Admin cost report evolves as the API surface grows; every provider ships breaking changes to endpoints you depend on, usually without warning tuned to your release calendar.

None of that is optional maintenance. Skip it and the numbers quietly drift — a new service lands in an "other" bucket, a schema change truncates a month of history, a credential expires and a connector goes dark for a week before anyone notices. The on-call cost of a silently wrong number is the same as the on-call cost of an outage, except nobody pages for it until finance asks why the total doesn't match the invoice.

When building it yourself is the right call

Sometimes DIY is genuinely the better trade. If you run one cloud provider with a simple, stable spend shape and no near-term plan to add AI or SaaS spend to the picture, a small internal script against a single API can be cheaper than any vendor relationship, subscription or not.

It's also the right call if cost data is a strategic asset you're already building — a platform or data-engineering team with spare capacity, an existing ingestion pipeline this is genuinely one more source into, or bespoke requirements (a custom internal chargeback model, an unusual data residency constraint) that no vendor will fit exactly. In those cases the build cost isn't a detour from other work; it's a small extension of work already happening.

When buying wins

For most engineering-led teams, the honest case for buying isn't that building is impossible — it's that engineering time is the scarcest resource in the business, and a cost pipeline is rarely the highest-leverage place to spend it. Every day sunk into ingestion plumbing is a day not spent on the product your customers pay for.

Buying also gets you the parts that are easy to skip in a first internal build and expensive to add later: coverage across many providers on day one, dedupe and backfill that already work, and a team whose whole job is keeping up with provider API churn so yours doesn't have to. That's the trade this estimator is built to make visible — not a verdict, just the real numbers on both sides.

FAQ

Common questions about building vs. buying

Isn't a script that calls the Cost Explorer API basically a weekend project?

The first call, yes — an API request against a single provider is genuinely quick. The weekend project is rarely what ships to production, though: a schema to normalize into, a scheduler, dedupe so re-syncs don't double-count, backfill for history, auth, a dashboard, and then the same work again for every additional provider. That's what the platform-build and per-source fields in this estimator are pricing.

What if we only need one provider and our spend is simple?

Then DIY is genuinely competitive — this estimator's own honest section says so. A single stable connector with no near-term plan to add more providers is one of the clearest cases where building it yourself can beat any vendor relationship. The math changes fast once a second, third, and fourth source get added, which is usually how it plays out in practice.

Doesn't an estimator built by a vendor obviously favor buying?

It's a fair thing to be skeptical of, which is why every number here is a constant you can see and change — the hourly rate, the build days, the maintenance hours — nothing is hidden or hardcoded to force a conclusion. The model is also deliberately conservative: it prices only the engineering time to build and maintain the pipeline, not the cloud infrastructure to run it, which would only widen the gap in CostMon's favor if it were included.

What does this estimate leave out that would make DIY cost more?

The compute, storage, and hosting to actually run the pipeline (a database, a scheduler, a dashboard host); the opportunity cost of what that engineering time isn't spent on instead; and any downstream incident cost from a silent data gap — a connector going dark, a schema change truncating history — that someone has to notice and fix. All of those push the real DIY number higher than what's shown here, never lower.

How is this different from the flat vs. percentage pricing page?

That page compares two ways a vendor might charge you once you've already decided to buy a tool. This page answers the question one step earlier — whether to build your own pipeline at all, versus paying anyone (CostMon included) for one that already exists.

See what your stack really costs.

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