How much can a managed remote cache reduce CI build times?

Build infrastructure costs are invisible until they explode. A 15-minute CI pipeline that runs 100 times a day across a team of 30 engineers costs weeks of developer time per year. Yet many teams continue treating CI speeds as a fixed cost, unaware that a managed remote cache can cut that time in half with minimal setup. This post walks through the measurable ROI: what to expect, how to measure it, and why the math almost always favors the investment.

The math: what is CI time really costing you?

Start with a simple audit. For your last week of commits:

• Count the number of CI runs (one per PR, plus retries and push-to-main)
• Record the average total time from start to green (or red)
• Multiply by developer wait time per run (context-switch penalty + blocked deployment time)

Example: a 25-person engineering team with an 18-minute average CI time, running 200 builds per week across branches and main:

200 builds/week x 18 min/build = 3,600 build-minutes = 60 engineer-hours/week

At an estimated fully-loaded engineering cost of $100/hour (salary + overhead), that is $6,000 per week, or roughly $300,000 per year (about 50 working weeks) spent on CI overhead. Most of that cost is context-switching and blocked work, not raw compute.

A managed remote cache that cuts average CI time from 18 to 8 minutes (a realistic 2-2.5x speedup for an Nx monorepo) saves:

(18 - 8) min x 200 builds/week = 2,000 min/week = ~33 engineer-hours/week 33 hours x $100/hr x 50 weeks = ~$167,000 saved per year

A managed cache service costs $50-200/month ($600-2,400 per year). The ROI is roughly 70-280x.

Real-world CI time improvements

The speedup depends on two factors: your cache hit rate and how much of your CI pipeline is skippable. Most Nx monorepos see:

• First build (cold cache): no improvement. A remote cache helps when tasks have been run before.
• Incremental PRs (typical case): 60-80% of tasks are cached hits, cutting build time by 2-3x. If a PR touches a single library, most other packages' builds, tests, and lints skip entirely.
• Push to main after PR merge: usually a warm cache hit, often skipping 80-90% of work. Build time drops from 15+ minutes to 2-3 minutes.

A test scenario from a mid-sized Nx monorepo with 40 packages and shared build/test/lint targets:

Average speedup across all builds: 2.5x.

Why teams underestimate the impact

The ROI often surprises teams because it is not just about CI time. Consider the downstream effects:

• Faster feedback loops. Developers get PR feedback in 5 minutes instead of 15. They stay in flow. They ship more features per sprint because less time is blocked.
• Reduced context switching. A developer does not start a new task while waiting for CI. The average context switch costs 15-25 minutes of productivity per switch. A 3x faster CI cuts switches by 66%.
• Fewer retries and flakes. Impatient developers cancel and re-run slow builds, creating duplicate compute and noise. A fast CI is less likely to fail from timeouts or resource starvation, reducing mystery flakes and retries.
• Deployment confidence. If CI is fast, teams run it more often and trust the result more deeply. Deployments are less risky when validation is quick and frequent.
• Hiring and retention. Fast local builds and CI are a hidden recruiting advantage. A team with a 5-minute CI ships faster and feels more productive - that compounds into retention and morale.

Is a managed cache cheaper than self-hosted?

Some teams ask: can we build our own? A self-hosted remote cache (running Nx Cloud, minio S3, or Docker on a private machine) has lower marginal cost but higher operational burden:

• Self-hosted: hardware ($500-2,000 upfront) + one person on-call (even 2 hours/month at $100/hr = $2,400/year) + capacity planning + backups. Total cost of ownership is usually $3,000-5,000/year.
• Managed (e.g., Cachely, NxCloud): $600-2,400/year, zero ops, automatic scaling, built-in backup and security.

For teams under 50 engineers, managed is almost always cheaper and faster to deploy. For larger teams or with strict on-prem requirements, self-hosted may make sense - but the cache itself is not where the ROI comes from. The ROI is the CI time savings, regardless of which option you choose.

How to measure your actual savings

Before committing to a cache service, run a proof-of-concept to measure your specific monorepo:

1. Establish a baseline

Run your CI pipeline without caching for one week. Record:

• Total build time per run (from start to finish, all tasks)
• Number of builds per day
• Number of tasks that executed per build (a 40-package monorepo might run 1,000+ individual tasks per full build)

2. Verify local caching is working

Nx has local caching enabled by default in nx.json via "cache": true in targetDefaults. Most monorepos already have it on, but verify it is working by running a build twice and checking if the second is faster. You can also measure the difference explicitly:

# First run (builds normally)
npx nx build my-app

# Second run (should hit local cache)
npx nx build my-app

# Force skip local cache to measure the speedup
npx nx build my-app --skip-nx-cache

If the second run is not significantly faster than the first, your task graph may have issues (too many dependencies, tasks flagged as non-deterministic, or cache misses due to bad inputs configuration).

3. Enable remote caching

Set up a managed remote cache (1-2 hours of setup for Cachely, NxCloud, or a self-hosted instance). Re-run the same pipeline for one week and measure the new average build time and task hit rate.

Cachely dashboards expose:

• Cache hit rate per task and per build
• Time saved per build (task-seconds skipped)
• Estimated engineering time saved (useful for executives)
• Cost breakdown (storage + compute cost per workspace)

4. Calculate ROI

Use the formula from earlier:

annual_savings = time_saved_hours_per_week x weeks_per_year x hourly_cost
ROI = annual_savings / cache_service_cost_per_year

Example: if you save 20 build-hours per week at $100/hr over 50 working weeks, and the cache service costs $1,200/year:

20 hours x 50 weeks x $100/hr = $100,000 saved per year
$100,000 / $1,200 = ~83x ROI

The hidden cost: not investing in caching

The most common mistake is treating caching as an optional "nice to have" instead of essential infrastructure. In a team of 20+, slow CI is not a tech-debt item - it is a tax on every engineer every day. The cost of not investing in caching far exceeds the cost of the tool.

A final reminder: the cache only helps if your team is:

• Running 50+ builds per week (typical for mid-to-large teams)
• Using a task-based build tool (Nx, Turbo, Bazel, Gradle) where tasks can be skipped
• Hitting similar code paths repeatedly (most PRs change a subset of packages, not the entire monorepo)

If any of these is not true, the ROI may be lower. But if they are, the math is compelling.