108–216+ engineer-hours in year oneor $29.99 a month
Building your own synthetic monitoring stack is possible — many teams have done it. This page is a realistic TCO comparison: what it costs in engineer-hours and infrastructure to build what Yorker already ships, and where building your own genuinely wins.
Last verified 2026-04-11
Year-1 total cost of ownership
Build cost is one-time; maintenance and infrastructure recur. Yorker's cost is flat. Home-rolled costs front-load in year 1 and continue at a lower but non-zero rate in subsequent years.
| Cost category | Home-rolled year 1 | Yorker year 1 |
|---|---|---|
Initial build 60–120 engineer-hours × $75/hr | $4,500 – $9,000 | — |
Ongoing maintenance 4–8 hr/mo × $75/hr × 12 mo | $3,600 – $7,200 | — |
OTel emission (optional) 20–40 engineer-hours to build OTLP sender (not in totals below) | $1,500 – $3,000 one-time | Included |
Runner infrastructure t3.small HTTP ($15/mo) + t3.medium browser ($30/mo), 1 region | $540/yr | Included in plan |
Result storage RDS t3.micro ~$15/mo | $180/yr | Included in plan |
Screenshot storage S3 standard, ~$3/mo at small scale | $36/yr | Included in plan |
Platform / subscription $29.99/month flat | — | $359.88/yr |
| Year-1 total Excludes OTel build (optional) and second region (+$63/mo or $756/yr) | $8,856 – $16,956 | $359.88 |
All assumptions are stamped above and use public data. Year-2 home-rolled cost drops to maintenance + infrastructure only ($3,600–$7,200 + $756/yr = $4,356–$7,956/yr) vs Yorker at $359.88/yr — still 12–22× higher.
What you'd need to build
This is the engineering scope that goes into matching Yorker's Phase 1 feature set. Each line item is a real engineering project, not a weekend script.
Poll-based or cron-based scheduler that dispatches HTTP checks at configurable intervals, handles retries, tracks consecutive failures for alert hysteresis, and persists results to a database.
Isolated headless Chromium execution with per-step screenshot capture, network request logging for the waterfall view, and clean teardown after each run. Playwright version pinning and update policy matter — a stale Playwright breaks checks silently.
Schema design for check results (timestamps, durations, assertion outcomes, screenshot paths), pagination for result history, time-series aggregation for availability SLA calculations, and data retention policies.
Upload per-step screenshots to object storage (S3 or equivalent), generate pre-signed URLs for UI display, manage lifecycle policies so storage costs don't compound indefinitely.
Consecutive-failure detection, alert rule evaluation, deduplication so a 10-minute outage doesn't trigger 10 separate pages, and integration with PagerDuty, Opsgenie, Slack, or your incident management tool.
Custom OTLP HTTP JSON exporter that wraps check results in the ResourceMetrics envelope, attaches correct resource attributes (check ID, location, run ID), and submits to your backend. No off-the-shelf library does this for synthetic checks — you build it from the OTLP spec.
Deploying runners in multiple regions requires a control plane that dispatches checks to the correct region, aggregates results, and handles region-specific network isolation. Each additional region multiplies the ops surface.
An outbound-only agent that polls a control plane for check assignments, executes them inside your private network, and submits results without inbound connectivity — the polling model is non-trivial to get right without race conditions or polling-interval drift.
Where Home-rolled monitoring is strongest
No tool is the right answer for every team. Here's where Home-rolled monitoring genuinely leads today — if your use case matches, start there.
A home-rolled system does exactly what you build it to do — no feature gates, no pricing tiers, no waiting for a vendor roadmap. If you need a non-standard check type, a custom assertion format, or a result storage schema that matches your data model, you build it. Yorker is opinionated about how checks run; your own system is opinionated only by your choices.
Check results, screenshots, and telemetry never leave your infrastructure. You choose where data lives, how long it's retained, who can query it, and what compliance framework governs it. Yorker stores check metadata in Neon Postgres and routes artifacts through Cloudflare R2 — both are SaaS dependencies you'd be accepting. Your own system has neither.
Yorker is a young company. If we raise prices, change the product, or shut down, you'd need to migrate. A home-rolled system has no vendor dependency — it runs until you turn it off. If long-term vendor stability matters more than near-term cost, building your own eliminates that risk.
Frequently asked
Why would I pay $29.99 when I could build this myself?
You could — and many teams have. The question is whether the 60–120 engineer-hours of initial build, plus 4–8 hours/month of ongoing maintenance, plus infrastructure costs, is a better use of your team's time than $29.99/month. At $75/hour, even the low estimate of the first year costs 25× what Yorker costs in year 1 (47× at the high end).
Are the engineer-hour estimates realistic?
The 60-hour low estimate assumes a skilled engineer working on nothing else and accepting significant scope tradeoffs: HTTP checks only, one region, basic alerting, no OTel, no screenshot filmstrip. The 120-hour high estimate is for a system with HTTP + browser checks, multi-region, alert routing, OTel emission, and result storage. Both are point-in-time estimates — ongoing Playwright version upgrades, alert tuning, and flakiness triage are real ongoing costs that don't appear in the build number.
What's the hardest part to build yourself?
OTel emission is the part most teams underestimate. Writing an OTLP HTTP JSON exporter that correctly structures synthetic check metrics — ResourceMetrics envelope, DataPoint attributes, exemplar trace IDs — takes 20–40 hours to get right. Playwright version upgrades are the ongoing cost most teams underestimate: Playwright ships regularly, browser binaries need updating, and flaky checks accumulate.
What does Yorker not do that a home-rolled system can?
Custom check types (e.g., non-HTTP protocols, proprietary internal APIs), custom result schemas that match your exact data model, on-premise deployment with no cloud dependency, and unlimited retention policies are things a home-rolled system can do that Yorker cannot. If those are hard requirements, building your own is the right answer.
Does the infrastructure cost change significantly at scale?
At low check volumes (tens of checks per team), the estimates above hold. At high volumes (thousands of checks, multiple regions), you'd need larger instances, a queue for check dispatch, horizontal scaling, and more sophisticated result storage — costs rise faster than linearly. Yorker's ephemeral architecture handles scale without you needing to architect it.
Can I use Yorker's OTel output alongside my own monitoring?
Yes. Yorker emits OTLP to any backend — if you have an existing Prometheus, Grafana, or Honeycomb stack, Yorker plugs into it. You can replace the Yorker runner with your own checks for any custom type we don't support, while using Yorker for the standard HTTP and browser coverage.
Related Reading
What OTel-native synthetic emission looks like in practice — the 20–40-hour build you get for $29.99/month.
The outbound-only agent architecture and why the polling model matters for private network checks.
Full pricing — plan fee, per-run consumption, private-location discount — compared to the TCO above.
Skip the build queue
Start free — no credit card — and run your first Playwright browser check in minutes. The 120 engineer-hours of build effort is already spent. $29.99/month to use the result.