Platform Overview

Know what your code costs. Before it costs you.

KodeGauge scans your repository and tells you not just what's wrong — but the monthly dollar cost of each issue, your infrastructure scaling ceiling, and the exact fix order to maximise ROI.

Trusted by CTOs for technical depth. Used by PE firms for due diligence.

$47K
avg. monthly savings identified per scan
< 5 min
from repo connection to full report
0 lines
of code stored — ever

Product Overview

A platform for code health that speaks your language.

Engineering teams have access to dozens of code scanners and quality tools. They tell you about bugs, complexity, and dependencies. But they rarely answer the questions engineering leaders actually need answered:

  • What's our real infrastructure footprint right now?
  • If we improve this, how much will it save us?
  • What's the critical path to fixing this problem?
  • Are we ready to scale? What's our bottleneck?

KodeGauge fills that gap. We scan your code and give you infrastructure-grade intelligence—the throughput capacity you have, the capacity you need, and the cost of the gap.

KodeGauge Report

What you get from every scan

Scan complete
Repository health score

Across security, maintainability, performance, cost

Infrastructure analysis

Concurrency, bottlenecks, throughput capacity

Cost estimation

Monthly/annual cloud spend and savings opportunities

AI prioritization

Ranked issues with effort estimates and business impact

Fix recommendations

Actionable guidance with implementation order

KodeGauge delivers

Infrastructure-grade intelligence, every scan

Built for both sides of the table

One scan. Two conversations.

The same report speaks to your engineering team in technical language and to your CFO or investors in financial language.

For CTOs & VP Engineering

Technical credibility, not noise

Not another linter. KodeGauge finds the infrastructure-level patterns that cause production incidents — and tells you the exact cost of each one.

  • Detects N+1 queries, sync-in-async blocking, and connection pool starvation — the patterns that cause real incidents
  • Maps your concurrency model (gunicorn / asyncio / celery) and shows exactly where worker count creates a bottleneck
  • Identifies when your DB pool is undersized vs active workers — the #1 silent cause of request timeouts under load
  • AI writes root cause, ordered fix steps, and effort estimate for every issue — not just a severity label

"Finally shows me the N+1 costs $15K a month — not just that it exists. And it flagged the connection pool was 5 connections for 20 workers before we hit it in production."

VP Engineering, Series B SaaS

For PE, CFO & Non-Technical Buyers

Due diligence in 5 minutes

Technical debt is a financial liability. KodeGauge translates code health into the numbers your board understands — before they become surprises.

  • Every issue is assigned a monthly dollar cost — infrastructure waste quantified, not estimated
  • Calculates your scaling ceiling: max concurrent users before the system degrades, and what it costs to raise it
  • Security exposure valued in context — CVE severity mapped to real-world exploitability, not just raw CVSS score
  • Pre-acquisition tech due diligence in one scan — health score, cost model, scaling ceiling, comparable across companies

"We run this on every target before LOI. One scan gives us the infrastructure cost model and scaling ceiling — work that previously needed a $40K technical consulting engagement."

Principal, PE Growth Fund

The Scanning Workflow

Scan once. Understand everything.

KodeGauge analyses your codebase in two phases — deep static detection followed by AI that explains, prioritises, and estimates cost. Results in under 3 minutes. Your code is never stored.

Phase 1

Static Analysis — No LLM

Every scan covers your entire codebase across multiple dimensions simultaneously — security, quality, dependencies, and documentation. Issues are detected with precision, then handed off to AI for context.

What gets detected
Code ComplexitySecurity FlawsCode QualityVulnerable DepsLeaked SecretsJava / ScalaCVE ExposureDocumentation Gaps
Connected via GitHub App
No credentials stored
Multi-language support
Python, JS, Java, Scala & more
Unified issue format
Consistent across all languages
Code never retained
Analysed in isolation, then gone
kodegauge — scan
1$ kodegauge scan acme/backend-api
2→ Connecting to repository…
3✓ Access verified (1.1s)
4→ Scanning Python codebase (12,847 LOC)
5✓ 47 complexity issues detected
6✓ 12 security findings
7✓ 8 vulnerable dependencies
8✗ 2 credential leaks
9→ Analysis complete. Code not retained.
10→ AI prioritisation running…
11✓ Health score: 67 / 100 (grade C)
12✓ 50 issues ranked by business impact. Done.
Then AI takes over
Phase 2

AI Intelligence Pipeline

AI Analysis Pipeline
Prioritisation
rank by business impact
runs in parallel
Root Cause
Plain-English explanation
Fix Guidance
Actionable steps
Cost Impact
Remediation estimate
CVE Risk
Exploitability context
also runs in parallel
Capacity Profile
Bottleneck + throughput ceiling
Unified Report
all findings in one view
Executive Summary
AI-written · ready to share

Once detection is complete, an AI pipeline takes over — reading only the flagged areas, running multiple specialised analyses in parallel, and producing results that are ready for both developers and executives.

Root Cause Explanation

Every issue gets a plain-English explanation of why it matters and what risk it introduces — no guesswork for the developer.

Cost & Savings Estimates

Concrete numbers: remediation hours, security exposure value, and potential savings — ready for a business case.

CVE Exploitability

Vulnerable dependencies are assessed in context. Is this CVE actually reachable in your app? What's the real exposure?

Capacity Profile

Identifies your concurrency model and throughput ceiling — so you know where performance breaks before it does in production.

What you get at the end of every scan
67
Health score
Weighted · 5 dimensions
43
Ranked issues
AI-prioritised by impact
$48k
Cost estimate
Remediation + security exposure
Capacity profile
Bottleneck + throughput ceiling
Real findings from real codebases

This is what a scan actually finds.

Not variable naming warnings. Not missing semicolons. The patterns that silently degrade performance, inflate cloud bills, and create scaling ceilings.

Critical
Identified by static scan

N+1 Query Pattern

SQLAlchemy lazy loading in user feed endpoint — 47 extra DB queries per request inside a tight loop.

$15K/month
What this means

Adding one user to your feed costs 47 database queries. At 1,000 active users, you're running 47,000 unnecessary DB queries per second. DB compute costs scale linearly with users.

High
Detected by AI analysis

Sync blocking in async worker

SQLAlchemy sync session inside AsyncIO event loop — blocks the event loop on every database call.

800→120req/s
What this means

Your API can handle 800 requests/second — until any request touches the database, which drops throughput to 120 req/s. A 6.6× invisible capacity cap that only appears under real load.

High
From capacity profile

Connection pool undersized

5 DB connections configured, 20 gunicorn workers active — 4 workers share every connection slot.

30+concurrent users = timeouts
What this means

On a busy afternoon, 30 simultaneous users cause your API to start timing out. Four workers queue for every available DB connection. The fix is a one-line config change.

These findings are typical for a Python/FastAPI backend. KodeGauge supports 10+ languages and adapts patterns to your specific stack.

Core Capabilities

What you can do with KodeGauge.

Repository Health Scoring

  • Instant health score across multiple dimensions
  • Security, maintainability, performance, and cost efficiency ratings
  • Weighted scoring so you prioritize what matters most
  • Historical trending to measure improvement

Infrastructure Analysis

  • Detect concurrency models (async/await, threads, workers)
  • Identify primary bottlenecks (database, compute, memory)
  • Estimate database pool requirements and query patterns
  • Map cache layer presence and effectiveness

Cost Estimation

  • Model infrastructure needs with your current code
  • Estimate monthly/annual cloud spend
  • Calculate cost impact of optimizations
  • Show which improvements yield the highest ROI

Performance Insight

  • Estimate max sustained RPS with current resources
  • Show database throughput ceiling
  • Identify memory constraints
  • Provide scaling recommendations

AI-Powered Explanations

  • Plain-English summaries of complex findings
  • Understand why each issue matters
  • Recommended fixes with effort estimates
  • Context on industry patterns and best practices

GitHub Native Integration

  • One-click connection, no credential storage
  • Scan on demand or on schedule
  • Results available directly in your workflow
  • Works with existing GitHub policies

Platform Dashboard

Your repository at a glance.

The dashboard gives you a single view of repository health, cost baseline, and improvement opportunities — in language both engineers and executives understand.

  • Repository health score (overall and by dimension)
  • Infrastructure cost estimate and trend
  • Top cost-reduction opportunities
  • Critical bottlenecks and fix priority
  • Scan history and trending
  • Recent issues and their fixes
See the Dashboard in Action

KodeGauge Dashboard

my-api-service

Live
0
Health
0
Issues
$0K
Savings
Missing cache layer
$30K/mo
N+1 query patterns
$15K/mo
Security score improved
+12 pts
Complexity reduced
-8 issues

Last scan

2 hours ago · Code not retained

Know your code. Control your costs. Reduce your risk.

Get a clear picture of repository health, infrastructure efficiency, and technical debt impact in one scan.

No credit card required · Free tier available · Setup in minutes