Engineering Analytics: When Building In-House Makes Sense (and When It Doesn't)

At some point, every engineering leader faces this decision: build an analytics system in-house, or buy one?

The question usually surfaces after someone asks "how productive is our engineering team?" and nobody has a good answer. The natural instinct — especially in engineering organizations — is to build something. Pull git data into a database. Wire up some Grafana dashboards. Write a few scripts.

I've watched this play out at dozens of companies. Some built successfully. Most spent months building something that measured the wrong things, then eventually bought a tool anyway. A few found a third option that didn't exist a year ago.

Here's how to think through the decision without wasting a quarter of engineering time.

Option 1: Build It Yourself

The DIY approach usually starts with a weekend project that grows. Someone writes a script to pull commit data from the GitHub API. They load it into PostgreSQL. They build a Grafana dashboard showing commits per developer per week. It works. Leadership loves it. Then the feature requests start.

What You Can Build

The data you can easily pull from git providers is activity data: commits, pull requests, lines changed, file types, timestamps, authors. With some effort, you can also pull CI/CD data (deployment frequency, build times, failure rates) and project management data (ticket status, cycle time).

Wire all of this into Metabase, Grafana, or Looker, and you can build serviceable dashboards for:

Commits and PRs per developer/team
Lines of code changed over time
PR cycle time (open to merge)
Review turnaround time
Deployment frequency
Build pass/fail rates

These are legitimate metrics. They have real value for spotting process problems. And building the dashboards is genuinely within reach of any competent engineering team.

What You Can't Build

Here's where DIY hits a wall: you can only measure what you can count.

Rule-based analysis can count lines, files, commits, and timestamps. It cannot evaluate the complexity of a code change. It cannot distinguish between a 200-line CRUD endpoint and a 200-line distributed locking mechanism. It cannot assess architecture decisions, risk profiles, or implementation sophistication.

This is the fundamental limitation. The metrics you can build in-house are activity and process metrics — not output metrics. They tell you how much work is happening and how fast it moves through the pipeline. They don't tell you how substantial or complex that work is.

The other thing you can't easily build is AI-powered code analysis. Training or fine-tuning models to evaluate code complexity is a research problem, not an engineering task. Using off-the-shelf LLMs requires building a rubric, handling consistency, managing costs, and continuously validating accuracy. It's possible — we did it — but it took dedicated engineering effort over months, not a weekend with some API calls.

The Hidden Cost of Building

The weekend project becomes a maintenance burden. GitHub changes their API. Your dashboard breaks when someone renames a team. The script that processes historical data runs for six hours and times out. Someone asks for a metric that requires joining data from three systems.

I've seen internal analytics projects consume 0.5-1.0 FTE of ongoing maintenance at mid-size organizations. That's $100K-200K per year in engineering time — for a system that measures activity, not output. The irony of burning engineering capacity to build an analytics tool that can't measure engineering output is not lost on me.

When Building Makes Sense

Building makes sense when:

You have very specific, unusual needs that no commercial tool addresses
You already have a data platform team with capacity
You only need basic activity dashboards as a starting point
Regulatory requirements prevent connecting to third-party services

If your needs are standard — "how productive is my team, what are they shipping, where are the bottlenecks" — building from scratch is usually the wrong call.

Option 2: Buy a Commercial Platform

The vendor landscape for engineering analytics has exploded. There are tools for every budget, team size, and philosophy. I've covered the full landscape elsewhere, but here's the buy-side view.

The Vendor Landscape

Enterprise platforms (Jellyfish, Faros): $30-50+ per developer per month. Full-featured, heavy implementation, designed for organizations with 200+ engineers. They connect engineering data to business outcomes and are built for VP/CTO-level conversations with executives. Implementation takes weeks to months.

Process platforms (Swarmia, LinearB, Sleuth): $10-25 per developer per month, often with free tiers for small teams. Focused on DORA metrics and delivery pipeline health. Faster to set up, narrower in scope. Good at identifying process bottlenecks.

Activity platforms (Waydev, Hatica, Pluralsight Flow): $10-20 per developer per month. Dashboard views of git activity, coding patterns, and meeting load. Easy to set up, but the underlying data is activity, not output.

Survey platforms (DX): Per-seat pricing, focused on developer experience surveys. Valuable for qualitative data but limited to self-reported insights.

What You're Actually Getting

Commercial platforms save you from building and maintaining infrastructure. You get professionally designed dashboards, cross-company benchmarking (sometimes), ongoing feature development, and customer support when things break. That's real value.

But here's what most buyers discover three months in: the cost scales with headcount and gets expensive fast. You're locked into a vendor's data model and workflow assumptions. The one-size-fits-all approach may not fit your culture. And — this is the big one — most platforms still measure activity or process, not output. You're paying $30/seat/month for a fancier version of what you could've built in Grafana.

Enterprise tools also carry implementation overhead that vendors understate. Connecting data sources, mapping teams, configuring dashboards, training managers — budget weeks, not days.

The Cost Reality

Let's do the math for a 100-person engineering org:

Option	Monthly Cost	Annual Cost
Enterprise (Jellyfish)	$4,000-5,000+	$48,000-60,000+
Mid-tier (LinearB, Swarmia)	$1,500-2,500	$18,000-30,000
Activity (Waydev)	$1,000-2,000	$12,000-24,000
GitVelocity	$0 (BYOK)	~$500-2,000 in API costs

The enterprise tier is a real budget line item. It needs to be justified with clear ROI — which, ironically, is hard to prove without already having good engineering metrics.

Option 3: Free Platforms (The BYOK Model)

There's a third option that didn't exist until recently: platforms that are free to use because they've shifted the cost model.

GitVelocity is free. Not freemium with a limited tier. Free. You bring your own Anthropic API key, and the platform scores every merged PR across your organization using Claude. You pay Anthropic directly for the AI inference — typically a few dollars per month for most teams — and GitVelocity charges nothing.

Why Free Works

The BYOK (bring your own key) model works because the expensive part of AI-powered analytics is the AI inference, and that cost scales with usage, not with a SaaS margin on top. When you bring your own API key, the platform doesn't need to subsidize your AI costs or charge you a premium to cover theirs.

This isn't a "free tier that upsells to paid." The platform is free. Period. The business model is built on different assumptions than traditional SaaS.

What You Get

GitVelocity scores every merged PR on a 0-100 scale across six dimensions: Scope, Architecture, Implementation, Risk, Quality, and Performance/Security. The score captures engineering complexity — how substantial the shipped code was.

You get individual-level visibility, team aggregations, trend analysis, and the ability to see what your organization is actually shipping over time. No source code is stored — diffs are processed and discarded.

What You Don't Get

GitVelocity focuses on output measurement. It doesn't replace process tools (DORA metrics) or developer surveys. It's one layer of a complete measurement system, not an all-in-one platform.

If you need investment allocation reports for the C-suite, you'll need an enterprise tool. If you need detailed cycle time breakdowns, you'll need a process tool. The question is whether those needs justify the price tag, or whether you can assemble a better stack from specialized tools.

The Decision Framework

Here's how to make the call.

Step 1: Define What You Need to Measure

List the questions you're trying to answer. Common ones:

What is each engineer and team actually shipping? (Output)
Where are the bottlenecks in our delivery pipeline? (Process)
How do our developers feel about their tools and workflow? (Experience)
How does engineering effort align with business priorities? (Strategy)
How is AI adoption changing our output? (AI impact)

Step 2: Map Questions to Metric Types

Each question maps to a metric category:

Output questions require AI-powered code analysis (GitVelocity)
Process questions require DORA/pipeline metrics (Sleuth, Swarmia, LinearB)
Experience questions require surveys (DX, internal surveys)
Strategy questions require enterprise planning tools (Jellyfish)
AI impact questions require before/after output comparison (GitVelocity)

Step 3: Evaluate Your Constraints

Budget: Can you justify $30-50/seat/month? If not, enterprise tools are out. Free and freemium tiers cover more than most teams realize.

Engineering capacity: Do you have cycles to build and maintain an in-house system? If not, buy or use free tools.

Timeline: Do you need answers this month or this quarter? In-house builds take months. Commercial tools take days to weeks. GitVelocity takes minutes.

Privacy requirements: Do regulatory or policy constraints limit what data can leave your infrastructure? Some tools offer on-premise deployment. GitVelocity processes diffs and discards them.

Step 4: Choose the Right Combination

Most teams are best served by a combination of specialized tools rather than one platform that tries to do everything.

Our recommendation:

Output measurement: GitVelocity (free). Set it up in minutes. Get data you've never had before about what your team actually ships.
Process measurement: Swarmia or Sleuth (free tiers available). Track DORA metrics and identify pipeline bottlenecks.
Experience measurement: Quarterly developer surveys. Even a simple structured survey provides valuable context.

This combination costs close to nothing, covers three of the four key measurement dimensions, and takes less than a day to set up. Compare that to a six-figure enterprise contract with a months-long implementation.

The Hidden Cost of Not Measuring Output

I want to close with something that often gets lost in the build-vs-buy debate: the cost of not measuring at all.

When you don't measure output, you default to proxies. Story points. Commit counts. "I think they're doing great." These proxies have well-documented failure modes: they reward activity over impact, they're trivially gamed, and they create perverse incentives that erode engineering culture.

Worse, without output data, you can't answer the questions that actually matter. Which engineers are growing? Where should you invest more? Is the AI tooling you bought actually increasing output? Are your senior engineers mentoring effectively, or just doing easy work?

These aren't nice-to-have questions. They're the questions that determine whether you allocate your most expensive resource — engineering talent — effectively.

The build-vs-buy question matters. But the more important question is: are you measuring output at all? If you aren't, start today. It's free.

GitVelocity measures engineering velocity by scoring every merged PR using AI. Free forever with BYOK, no source code stored, and setup takes minutes.

See how it works.