A Realistic Cost Breakdown

In one line: $500–$3,500/month covers the entire stack for a startup at $1M ARR. That's noise next to a single engineer's payroll.

In plain English

The conversation about hosting and tooling bills sounds dramatic until you compare it to people costs. A mid-level engineer is fifteen to twenty-five thousand dollars a month, fully loaded. Your entire infrastructure bill at startup scale is rarely more than two thousand. That math is the whole reason "buy, don't build" wins at this stage.

A startup at ~$1M ARR with ~5,000 active users

For a startup at ~$1M ARR with ~5,000 active users:

Item	Monthly Cost	Notes
Vercel Team	$20–500	Scales with bandwidth & functions
Supabase Pro	$25–500	Scales with DB size + bandwidth
Clerk	$25–300	Per-MAU pricing
Sentry	$30–200	Per-event pricing
PostHog	$0–300	Generous free tier
Better Stack	$30–100	Logs + uptime + on-call
Trigger.dev / Inngest	$20–200	Background jobs
Resend	$20–100	Email volume
Stripe	2.9% + 30¢/txn	Revenue-based
GitHub Team	$4/user	$40 for 10 engineers
Linear	$8/user	$80 for 10 engineers
Doppler	$0–20/user	Secrets
Vanta (if SOC 2)	$300–1,000	Compliance platform
Domain + misc	$20
Total	$500–$3,500	Negligible vs payroll

For comparison, a single mid-level engineer costs $15–25K/month fully loaded. Infrastructure costs at this scale are noise.

Worked example: trading infra cost for engineer time

A team debates whether to "save money" by self-hosting Postgres instead of paying $200/month for Supabase Pro. The math:

• Self-hosted Postgres: ~$50/month for a VM + storage. But: someone has to set up backups (~1 day to do well), monitor disk space, handle Postgres upgrades, configure connection pooling, troubleshoot when it falls over. Realistically that's 4–8 engineer-hours per month, perpetually.
• Supabase Pro: $200/month. Backups, monitoring, pooling, upgrades all handled. ~0 engineer-hours per month.

If an engineer costs $150/hour fully loaded, the "savings" of self-hosting are negative — you're spending $600–$1,200/month in engineer time to save $150 in infrastructure. The Supabase math is obviously right.

This pattern repeats across every managed service.

Highlight: when costs do matter

Infra costs become non-noise once you're at hundreds of thousands of MAUs or when a specific service has a runaway line item (Vercel bandwidth spikes, PostHog event volume). The trigger isn't "we should save money in general" — it's "this one bill grew 5x and we can't explain it." Then you investigate and optimize the specific line. The rest you leave alone.

Common mistakes

Where people commonly trip up

• Spending an engineer-week to shave $80/month off Vercel. A senior engineer's time is roughly $1,000/day. A multi-week migration to save under $100/month never pays back at this stage. Track hours-spent-saving-dollars and you'll catch this fast.
• Not setting any billing alerts. The first time you'll learn about runaway costs is the invoice — usually two weeks after the spike started. Every paid service should have alerts at 2x, 5x, and 10x your normal spend, on day one.
• Confusing seat-based costs for usage costs. Adding 10 engineers triples your Linear, GitHub, Notion, and Vanta bills overnight in a way Vercel's per-request pricing never will. Plan headcount with a "tooling cost per seat" line item.
• Optimizing the cheap bills and ignoring the expensive ones. Founders love agonizing over a $40 Sentry tier while a $1,800/month observability vendor renews quietly. Sort the bills by absolute dollars, not by emotional weight, before deciding what to cut.
• Reading the AI bill as fixed. GenAI line items (OpenAI, Anthropic, embeddings) are increasingly the largest variable cost in a 2026 startup. Treat them like compute — cache aggressively, choose smaller models for routine work, and audit token spend quarterly the same way you audit Postgres.

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What's next

→ Continue to Sample Day-in-the-Life for a concrete picture of what a startup engineer's day actually looks like.