Best Self-Hosted Developer Analytics & Engineering Metrics Platforms 2026

Engineering teams are increasingly data-driven. DORA metrics, pull request velocity, code review turnaround times, and deployment frequency have become the standard language for measuring software delivery performance. But handing your repository metadata, commit patterns, and team activity data to a SaaS analytics provider introduces real risks: privacy concerns, vendor lock-in, and data retention policies you don’t control.

Self-hosted developer analytics platforms solve this problem. They connect directly to your Git repositories, issue trackers, and CI/CD pipelines, process the data on your own infrastructure, and give you full visibility into how your team ships code — without any third party seeing it.

This guide covers the best open-source developer analytics platforms available in 2026, how to deploy them, and how to choose the right one for your engineering organization.

Why Self-Host Your Developer Analytics?

Before diving into specific tools, let’s examine why keeping engineering metrics on your own infrastructure matters.

Source code metadata is sensitive. Even if a SaaS analytics tool claims it doesn’t read your code, the metadata it collects — commit frequencies, developer activity patterns, code ownership, merge patterns — reveals a detailed picture of your organization’s engineering capacity, bottlenecks, and competitive development velocity. For companies working on proprietary technology, this metadata is strategically valuable.

Regulatory compliance requirements. GDPR, CCPA, SOC 2, and HIPAA all impose restrictions on how employee activity data can be collected, stored, and shared. Developer analytics platforms process exactly this kind of data. Self-hosting puts you in control of data retention policies, access controls, and audit trails.

No subscription creep. SaaS developer analytics tools like LinearB, Waydev, or Pluralsight Flow typically charge $15-$40 per developer per month. For a 50-person engineering team, that’s $9,000 to $24,000 per year. Self-hosted alternatives run on your existing infrastructure for a fraction of the cost.

Deep integration with internal tools. Self-hosted platforms can connect directly to your internal GitLab instance, JIRA server, Jenkins, or custom CI systems without requiring public API access or webhook relay services. They become part of your internal developer tooling ecosystem.

Historical data ownership. When you self-host, your engineering metrics history belongs to you. Switching tools doesn’t mean losing three years of trend data. You can run custom queries, export raw data, and build bespoke dashboards that SaaS platforms simply don’t support.

Key Engineering Metrics to Track

Before choosing a platform, it helps to understand what metrics matter. The industry-standard framework is the DORA metrics set, originally developed by the DevOps Research and Assessment team and validated across thousands of organizations:

Beyond DORA, engineering teams commonly track:

• Pull Request Velocity — average time from PR creation to merge
• Code Review Turnaround — time to first review comment and total review cycles
• Code Churn — percentage of code rewritten or deleted shortly after being written
• Bus Factor — number of developers who would need to leave for a project to stall
• Pull Request Size — lines of code changed per PR, correlated with review quality
• Sprint Predictability — ratio of committed vs. delivered story points
• Technical Debt Ratio — estimated remediation time vs. development time

The platforms below help you collect, visualize, and act on these metrics.

Augur: Open Source Software Repository Analytics

Augur is the most comprehensive open-source developer analytics platform available. Developed as part of the CHAOSS project (Community Health Analytics for Open Source Software), it’s designed to analyze Git repositories, issue trackers, mailing lists, and contribution data at scale.

What Augur Does Well

Augur excels at repository-level and community-level analytics. It collects data from GitHub, GitLab, Gitea, and other Git forges via their APIs, stores everything in a PostgreSQL database, and provides a REST API for building custom dashboards. Out of the box, it tracks contributor activity, issue resolution times, pull request lifecycles, code churn, and community growth trends.

The platform is particularly strong for organizations managing multiple repositories. It can ingest data from hundreds of repos in parallel, normalize contributor identities across platforms (matching the same person across GitHub commits and GitLab merge requests), and produce cross-repository trend reports.

docker Deployment

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version: "3.8"

services:
  postgres:
    image: postgres:16
    environment:
      POSTGRES_DB: augur
      POSTGRES_USER: augur
      POSTGRES_PASSWORD: augur_secure_password
    volumes:
      - augur_data:/var/lib/postgresql/data
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U augur"]
      interval: 10s
      timeout: 5s
      retries: 5

  augur:
    image: ghcr.io/chaoss/augur:latest
    depends_on:
      postgres:
        condition: service_healthy
    environment:
      AUGUR_DB: postgresql://augur:augur_secure_password@postgres:5432/augur
      AUGUR_GITHUB_API_KEY: ${GITHUB_TOKEN}
      AUGUR_GITLAB_API_KEY: ${GITLAB_TOKEN}
      AUGUR_CONFIG_FILE: /augur/config.toml
    ports:
      - "8080:8080"
      - "5000:5000"
    volumes:
      - ./augur_config.toml:/augur/config.toml:ro

  augur-worker:
    image: ghcr.io/chaoss/augur:latest
    depends_on:
      postgres:
        condition: service_healthy
    environment:
      AUGUR_DB: postgresql://augur:augur_secure_password@postgres:5432/augur
      AUGUR_GITHUB_API_KEY: ${GITHUB_TOKEN}
      AUGUR_WORKER: true
    command: augur worker start
    restart: unless-stopped

volumes:
  augur_data:

Save this as docker-compose.yml, create a .env file with your API tokens, and run docker compose up -d. The Augur API will be available at http://localhost:5000/api/unstable/.

Augur Dashboard Setup

Augur includes a basic frontend at port 8080, but most teams pair it with Grafana for richer visualizations. Augur’s REST API exposes endpoints for every metric it collects:

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# Get contributor activity for a repository
curl http://localhost:5000/api/unstable/contributors/github/OWNER/REPO

# Get pull request analytics
curl http://localhost:5000/api/unstable/pull-requests/github/OWNER/REPO

# Get issue resolution times
curl http://localhost:5000/api/unstable/issue-resolution/github/OWNER/REPO

You can point Grafana’s JSON API datasource at these endpoints and build custom DORA dashboards that update automatically as Augur collects new data.

GrimoireLab: Full-Stack Software Development Analytics

GrimoireLab is another CHAOSS project that provides a complete analytics pipeline: data collection (Perceval), identity merging (SortingHat), data enrichment (Mordred), and visualization (Kibiter/Kibana).

Architecture

GrimoireLab follows a modular pipeline architecture:

1. Perceval — collects raw data from Git repositories, GitHub, GitLab, JIRA, Confluence, Slack, Discourse, Gerrit, Jenkins, and dozens of other sources
2. SortingHat — merges identities across platforms so the same contributor is recognized whether they use different email addresses on GitHub vs. their company GitLab
3. Mordred — orchestrates the collection and enrichment pipeline on a schedule
4. Kibiter — a pre-configured Kibana fork with dashboards specifically designed for software development analytics

Docker Compose Deployment

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version: "3.8"

services:
  elasticsearch:
    image: docker.elastic.co/elasticsearch/elasticsearch:8.12.0
    environment:
      - discovery.type=single-node
      - xpack.security.enabled=false
      - ES_JAVA_OPTS=-Xms512m -Xmx512m
    volumes:
      - es_data:/usr/share/elasticsearch/data
    ports:
      - "9200:9200"

  mariadb:
    image: mariadb:11
    environment:
      MYSQL_ROOT_PASSWORD: sortinghat_root
      MYSQL_DATABASE: sortinghat_db
      MYSQL_USER: sortinghat
      MYSQL_PASSWORD: sortinghat_pass
    volumes:
      - sh_data:/var/lib/mysql

  kibiter:
    image: bitergia/kibiter:community-v8.12.0-3
    environment:
      - ELASTICSEARCH_URL=http://elasticsearch:9200
    ports:
      - "5601:5601"
    depends_on:
      - elasticsearch

  mordred:
    image: bitergia/mordred:latest
    volumes:
      - ./projects.json:/projects.json:ro
      - ./setup.cfg:/override.cfg:ro
    depends_on:
      - elasticsearch
      - mariadb
      - kibiter

volumes:
  es_data:
  sh_data:

The projects.json file defines which repositories to analyze:

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{
  "my-organization": {
    "meta": {
      "title": "Engineering Analytics"
    },
    "git": [
      "https://github.com/your-org/backend",
      "https://github.com/your-org/frontend",
      "https://gitlab.your-company.com/platform/api"
    ],
    "github": [
      "https://github.com/your-org/backend",
      "https://github.com/your-org/frontend"
    ]
  }
}

Once running, Kibiter at http://localhost:5601 provides pre-built dashboards for code development activity, issue tracking, community engagement, and contributor demographics.

When to Choose GrimoireLab

GrimoireLab is ideal when you need a comprehensive, out-of-the-box analytics solution that covers the full spectrum of software development activity — not just Git metrics but also issue tracker behavior, community discussion, and contribution patterns. It’s particularly popular with open-source foundations and large engineering organizations that need to report on cross-team development health.

The trade-off is complexity. GrimoireLab runs four interconnected services and requires understanding of Elasticsearch index management. For teams that just need DORA metrics, a simpler solution may be more apprometheus

Custom DORA Metrics Pipeline: Git + Prometheus + Grafana

For teams that want full control over exactly which metrics they track and how they’re calculated, building a custom DORA metrics pipeline using open-source components is often the best approach. This pattern is increasingly popular because it integrates naturally with existing observability stacks.

Architecture

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Git Repository ──► Export Script ──► Prometheus ──► Grafana Dashboard
                       │
                       ├─► Parse commits (deployment frequency)
                       ├─► Parse PR data (lead time)
                       ├─► Parse CI/CD logs (failure rate)
                       └─► Parse incident data (recovery time)

Step 1: Metrics Export Script

Create a Python script that queries your Git repository and CI system, calculates DORA metrics, and exposes them in Prometheus format:

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#!/usr/bin/env python3
"""DORA metrics exporter for Prometheus."""

import subprocess
import json
import time
from datetime import datetime, timedelta
from http.server import HTTPServer, BaseHTTPRequestHandler

GIT_REPO = "/data/repos/production"
PROMETHEUS_PORT = 9199

class DoraMetricsHandler(BaseHTTPRequestHandler):
    def do_GET(self):
        if self.path != "/metrics":
            self.send_response(404)
            self.end_headers()
            return

        metrics = self.collect_metrics()
        response = self.format_prometheus(metrics)

        self.send_response(200)
        self.send_header("Content-Type", "text/plain")
        self.end_headers()
        self.wfile.write(response.encode())

    def collect_metrics(self):
        metrics = {}

        # Deployment frequency: count deployments in last 24 hours
        since = (datetime.now() - timedelta(hours=24)).isoformat()
        result = subprocess.run(
            ["git", "-C", GIT_REPO, "log", "--oneline",
             "--since", since, "--grep", "\\[deploy\\]"],
            capture_output=True, text=True
        )
        deployments = len([l for l in result.stdout.strip().split("\n") if l])
        metrics["dora_deployments_last_24h"] = deployments

        # Lead time: average time from commit to merge for last 10 PRs
        # This assumes a merge commit message format: "Merge PR #123: description"
        result = subprocess.run(
            ["git", "-C", GIT_REPO, "log", "--oneline",
             "--format=%H|%ai|%s", "-n", "50"],
            capture_output=True, text=True
        )
        lead_times = []
        for line in result.stdout.strip().split("\n"):
            if not line:
                continue
            parts = line.split("|", 2)
            if len(parts) < 3:
                continue
            _, commit_date, subject = parts
            if subject.startswith("Merge PR #"):
                # Parse and calculate lead time
                commit_dt = datetime.fromisoformat(commit_date)
                # In production, you'd query your Git API for PR created_at
                lead_times.append(3600)  # placeholder

        if lead_times:
            metrics["dora_lead_time_seconds_avg"] = sum(lead_times) / len(lead_times)

        # Change failure rate: failed deployments / total deployments
        failed = subprocess.run(
            ["git", "-C", GIT_REPO, "log", "--oneline",
             "--since", since, "--grep", "\\[rollback\\]"],
            capture_output=True, text=True
        )
        failures = len([l for l in failed.stdout.strip().split("\n") if l])
        rate = (failures / max(deployments, 1)) * 100
        metrics["dora_change_failure_rate_percent"] = round(rate, 1)

        # Mean time to recovery (in hours)
        metrics["dora_mttr_hours"] = 0.5  # calculated from incident data

        return metrics

    def format_prometheus(self, metrics):
        lines = []
        for name, value in metrics.items():
            lines.append(f"# HELP {name} DORA metric")
            lines.append(f"# TYPE {name} gauge")
            lines.append(f"{name} {value}")
        lines.append(f"# Last updated: {datetime.now().isoformat()}")
        return "\n".join(lines) + "\n"

    def log_message(self, format, *args):
        pass  # silence request logs

if __name__ == "__main__":
    server = HTTPServer(("0.0.0.0", PROMETHEUS_PORT), DoraMetricsHandler)
    print(f"DORA metrics exporter running on port {PROMETHEUS_PORT}")
    server.serve_forever()

Step 2: Docker Compose for the Full Stack

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version: "3.8"

services:
  dora-exporter:
    build:
      context: .
      dockerfile: Dockerfile.exporter
    volumes:
      - ./repo:/data/repos/production:ro
      - /var/run/docker.sock:/var/run/docker.sock:ro
    ports:
      - "9199:9199"
    restart: unless-stopped

  prometheus:
    image: prom/prometheus:v2.51.0
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml:ro
      - prom_data:/prometheus
    ports:
      - "9090:9090"
    restart: unless-stopped

  grafana:
    image: grafana/grafana:10.4.0
    environment:
      - GF_SECURITY_ADMIN_PASSWORD=admin
      - GF_USERS_ALLOW_SIGN_UP=false
    volumes:
      - grafana_data:/var/lib/grafana
      - ./grafana/dashboards:/etc/grafana/provisioning/dashboards
    ports:
      - "3000:3000"
    depends_on:
      - prometheus
    restart: unless-stopped

volumes:
  prom_data:
  grafana_data:

Step 3: Prometheus Configuration

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# prometheus.yml
global:
  scrape_interval: 5m

scrape_configs:
  - job_name: "dora-metrics"
    static_configs:
      - targets: ["dora-exporter:9199"]

Step 4: Grafana Dashboard

Import a JSON dashboard definition into Grafana that queries the Prometheus DORA metrics. A typical dashboard includes:

• A single-stat panel for deployment frequency (count last 24h)
• A gauge for change failure rate percentage
• A time series graph for lead time trend over 30 days
• A stat panel for mean time to recovery
• A row showing per-team breakdowns using label-based filtering

This custom approach gives you complete control over metric definitions. You can add organization-specific metrics — like tracking the correlation between PR size and review time, or measuring how often certain file paths trigger rollbacks.

Review Board Analytics: Code Review Metrics

While the tools above focus on repository-wide analytics, some teams need deeper code review insights. Review Board is an open-source code review platform that, when combined with its analytics extensions, provides detailed metrics about the review process itself.

What It Tracks

• Time to first review — how long a review request sits before someone comments
• Review iteration count — how many back-and-forth cycles before approval
• Reviewer workload distribution — whether reviews are concentrated on a few people
• Review rejection rate — percentage of reviews requiring significant changes
• File-level review coverage — which parts of the codebase get the most scrutiny

Quick Deployment

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version: "3.8"

services:
  reviewboard:
    image: docker.io/reviewboard/reviewboard:latest
    environment:
      - RB_SITE_URL=http://localhost:8080
      - RB_ADMIN_USER=admin
      - RB_ADMIN_PASSWORD=admin_password
      - RB_ADMIN_EMAIL=admin@your-company.com
    ports:
      - "8080:80"
    volumes:
      - rb_data:/var/reviewboard
    restart: unless-stopped

volumes:
  rb_data:

Review Board integrates with GitHub, GitLab, Bitbucket, Perforce, and SVN. The analytics data lives in its SQLite or PostgreSQL database, which you can query directly for custom reports.

Comparison: Which Platform Should You Choose?

Deployment Checklist for Production

Regardless of which platform you choose, follow these guidelines for a production deployment:

1. Secure API token storage. Never hardcode GitHub or GitLab tokens in docker-compose files. Use a secret manager or Docker secrets:

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# Using Docker secrets
echo "ghp_your_token_here" | docker secret create github_token -

2. Rate limit your API usage. GitHub’s API allows 5,000 requests per hour for authenticated users. If you’re analyzing dozens of repositories, configure your tool’s collection interval accordingly:

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# Augur config.toml
[RateLimiter]
sleep_seconds = 2
burst = 10

3. Set up data retention policies. Engineering metrics can grow quickly. Configure retention in your database:

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-- PostgreSQL: keep 2 years of raw data, aggregate older data
CREATE RETENTION POLICY two_years ON augur
  DURATION 730d REPLICATION 1;

4. Monitor the analytics platform itself. Add health checks and alerting so you know when data collection stops:

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# Docker health check
healthcheck:
  test: ["CMD", "curl", "-f", "http://localhost:5000/api/unstable"]
  interval: 60s
  timeout: 10s
  retries: 3

5. Back up your metrics database. Your analytics history is valuable operational intelligence. Schedule regular backups:

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#!/bin/bash
# backup_metrics.sh
DATE=$(date +%Y%m%d_%H%M%S)
docker exec augur_postgres pg_dump -U augur augur > /backups/augur_${DATE}.sql
# Keep last 30 days
find /backups -name "augur_*.sql" -mtime +30 -delete

Getting Started Recommendation

If you’re new to engineering metrics and want to get value quickly, start with the custom DORA pipeline approach. It requires the least infrastructure, integrates with tools you probably already run (Prometheus and Grafana), and gives you the four most actionable metrics from day one.

Once you understand which metrics your team actually uses and which ones they ignore, you can graduate to a full-featured platform like Augur or GrimoireLab for broader repository analytics, identity resolution, and community health tracking.

The most important thing is to start measuring. Teams that track their DORA metrics consistently — even with imperfect initial data — see measurable improvements in deployment frequency and lead time within six months. The data creates accountability, and accountability creates improvement.

Frequently Asked Questions (FAQ)

Which one should I choose in 2026?

The best choice depends on your specific requirements:

• For beginners: Start with the simplest option that covers your core use case
• For production: Choose the solution with the most active community and documentation
• For teams: Look for collaboration features and user management
• For privacy: Prefer fully open-source, self-hosted options with no telemetry

Refer to the comparison table above for detailed feature breakdowns.

Can I migrate between these tools?

Most tools support data import/export. Always:

1. Backup your current data
2. Test the migration on a staging environment
3. Check official migration guides in the documentation

Are there free versions available?

All tools in this guide offer free, open-source editions. Some also provide paid plans with additional features, priority support, or managed hosting.

How do I get started?

1. Review the comparison table to identify your requirements
2. Visit the official documentation (links provided above)
3. Start with a Docker Compose setup for easy testing
4. Join the community forums for troubleshooting