What's the real minimum hardware for production GitHub Enterprise Server?

In my experience, GitHub's documentation saying 4 CPUs and 32GB RAM is complete bullshit. I've deployed this maybe 20 times, and anything under 8 CPUs and 64GB RAM turns into a performance nightmare once you hit 50+ active developers. The 150GB storage minimum? That lasts about 3 months before you're scrambling to expand. I usually start with 500GB minimum and plan for 50-100GB per 100 repos, depending on how much your teams love storing giant binaries in Git. HA configs definitely need double resources, but here's what the docs don't tell you - cheap VMs with shared storage will make developers want to quit. Learned this the hard way when a startup tried to run GitHub Enterprise on t2.medium instances. Don't do it.

How often does GitHub Enterprise Server actually need downtime?

Monthly security updates typically take 15-30 minutes, but I've had them take 2+ hours when database migrations go sideways. The quarterly feature releases are supposed to be 30-60 minutes - budget 90 minutes and have a rollback plan ready. HA failover isn't magic. I've seen failovers take 5-10 minutes while the system figures out what's broken, and you still need to validate that everything actually works. In one deployment, our "automatic" failover required manual intervention because the replica was 30 seconds behind and some webhooks got lost. Realistically, plan for 4-6 hours of planned downtime per year, plus whatever breaks at 3am. And there will be something that breaks at 3am.

Can GitHub Enterprise Server really run air-gapped with no internet connection?

Technically yes, but holy shit is it a pain in the ass. I've done a few air-gapped deployments for defense contractors and financial firms, and it's like administering a server in 1995. You're downloading updates on a USB stick and walking them into the secure environment. Certificate management becomes a nightmare because you can't use Let's Encrypt or any automated renewal. GitHub Actions? Forget about using any public actions - you'll maintain your own registry of vetted actions and dependencies. The worst part is troubleshooting. No Stack Overflow, no GitHub community discussions, no external documentation. When something breaks, you're debugging with just the official docs and whatever tribal knowledge your team has. Plan for 2-3x the operational overhead, minimum.

What breaks most often in GitHub Enterprise Server deployments?

Disk space, every fucking time. I've been paged at 2am because someone's CI workflow started generating 20GB debug dumps and filled /data/user overnight. GitHub Actions artifacts are the worst - they accumulate faster than you expect. SAML cert expiration is the classic "why is nobody able to log in?" incident. Usually happens during a weekend when certificates auto-renew and the SAML metadata doesn't match. I've learned to set calendar reminders 30 days before any cert expires. Database performance goes to shit around 500-1000 repositories, especially with large monorepos. PostgreSQL starts locking up during Git operations and API calls timeout. We ended up having to hire a DBA just for GitHub Enterprise Server. Network issues are sneaky - webhooks fail silently and CI/CD breaks without obvious errors. Took us weeks to figure out that a firewall rule change was dropping webhook traffic.

How difficult is migrating from GitHub Enterprise Server to GitHub Enterprise Cloud?

The marketing pitch makes it sound easy - "just export and import your data." Reality is way more complex. Yes, repository data moves fine, but everything else? Your SSO config, team permissions, webhook URLs, CI/CD integrations, custom scripts that hit the API - all of that breaks and needs rebuilding. We spent 4 months migrating a 200-developer org. GitHub's migration tools handle the repositories, but you're manually recreating team structures, re-configuring Okta SAML, updating hundreds of webhook endpoints, and rewriting deploy scripts. The hardest part is convincing developers to update their git remotes and rebuild their workflows. Plan for 6+ months if you have complex integrations or reluctant teams.

What's the real cost difference between GitHub Enterprise Server and Cloud?

Everyone focuses on the $21/user/month license cost and misses the hidden operational clusterfuck that is running your own GitHub. For 500 users, the licensing is $10,500/month for either option. But with Enterprise Server, you're also paying for: - 2-3 dedicated platform engineers ($200K/year combined) - AWS/Azure infrastructure ($5-8K/month minimum) - Backup storage and DR ($2-3K/month) - Security tools and monitoring ($3-5K/month) - Professional services when shit breaks ($50K+/year) I've seen total costs hit $60K/month for what GitHub Cloud would cost $25K/month. The only time Server makes financial sense is if you're already paying platform engineers and have excess datacenter capacity.

How much GitHub/Linux expertise does our team actually need?

If you're asking this question, you probably don't have enough expertise yet. You need someone who can debug PostgreSQL performance issues, troubleshoot Linux networking, manage SSL certificates, and understand distributed systems. This isn't "I've used Linux before" level stuff - it's "I've been a systems administrator for 5+ years" expertise. Minimum two people with serious ops experience. When your primary admin goes on vacation and SAML auth breaks at 3am, you need someone who can fix it without waking up the whole company. Most teams underestimate this. I've seen organizations hire junior DevOps engineers thinking they can learn on the job. GitHub Enterprise Server will teach them, but your developers will suffer through months of performance issues and outages first.

Can we upgrade GitHub Enterprise Server in place or do we need blue/green deployments?

In-place upgrades work for minor versions, but I learned to always test the upgrade path in staging first. The 3.15 to 3.16 upgrade took 3 hours instead of the promised 45 minutes because of a database schema migration nobody mentioned in the docs. Blue/green deployments are safer but more complex. You're running two complete environments and switching DNS/load balancer traffic. Most teams don't have the infrastructure or expertise for proper blue/green deployments. HA configs can do rolling upgrades, but "minimal downtime" still means 10-15 minutes of degraded performance while nodes restart. And rolling back under pressure is a nightmare - I've been there at 2am trying to rollback a failed upgrade while developers are screaming on Slack.

What monitoring do we need beyond GitHub's built-in dashboards?

GitHub's built-in dashboards show pretty graphs but miss the metrics that actually matter. They'll show you CPU usage but won't alert when Git operations start timing out. I always set up external monitoring with DataDog or Prometheus. You need alerts for: - Disk space at 60% (not 90%) - Memory usage trending up over 24 hours - Git clone/push response times above 5 seconds - Background job queue depth - API 5xx error rates - Webhook delivery failures The built-in monitoring missed a slow memory leak that took down our instance after 3 weeks. External monitoring caught it trending up and we scheduled a restart before it became an outage.

How do we handle GitHub Enterprise Server security patching and vulnerability management?

Security patches are a constant stress. GitHub publishes them monthly, but critical vulnerabilities show up whenever they feel like it. I've had to emergency patch on a Friday afternoon because of a remote code execution vulnerability. You need a process for rapid testing and deployment. My usual approach: patch in staging, run a quick smoke test, and deploy to production within 24 hours. For critical security patches, sometimes you're patching in production with fingers crossed. The worst part is that security patches sometimes break things. We had a 3.16 security patch that broke SAML authentication for 200 users. Rolled back, fixed the config, patched again. Two maintenance windows in one week because security couldn't wait. Maintain good relationships with GitHub support - when you're dealing with security incidents, you need someone who answers the phone immediately.

What's the disaster recovery strategy for GitHub Enterprise Server?

GitHub's backup utilities work great until you actually need to restore from them. The "4-8 hour RTO" in the docs assumes everything goes perfectly and you've practiced the procedure recently. Reality: I've seen disaster recovery take 12+ hours because nobody remembered that the load balancer config wasn't backed up, DNS needed updating, and SSL certificates had to be reinstalled. Here's what actually works: - Test restore procedures monthly, not quarterly - Document every step including all the shit that's not in GitHub's backup (DNS, load balancers, certificates, monitoring configs) - Keep an updated runbook that includes phone numbers and access credentials - Have a warm standby environment where you can test restores without affecting production Untested disaster recovery procedures are disaster recovery theater. I learned this when a datacenter fire turned our "4 hour recovery" into a 2-day nightmare.

How do GitHub Actions and GitHub Packages affect infrastructure requirements?

Actions and Packages will fuck up your capacity planning. They look simple until your developers start using them for everything. Actions artifacts pile up fast. We went from 100GB to 2TB of artifact storage in 6 months. Each workflow run stores logs, test results, build artifacts - and developers never clean up old runs. Budget 10x more storage than you think you need. Self-hosted runners are another operational nightmare. You're managing a fleet of compute instances that developers treat like their personal playgrounds. Security, patching, scaling, secret management - it's like running a mini cloud platform. Packages storage costs surprised us. Docker images are huge, and developers started publishing everything to GitHub Packages instead of using DockerHub. Went from $200/month to $2000/month in package storage costs. My advice: enable these features gradually and monitor usage obsessively. They're powerful but expensive.

Can we integrate GitHub Enterprise Server with our existing LDAP/Active Directory?

LDAP integration works, but your directory admin and GitHub admin need to actually talk to each other. This never happens. I've spent hours debugging why user sync stopped working, only to discover that the AD team changed the schema or moved users to a different OU without telling anyone. Group mapping is particularly fragile - organizational changes break GitHub team memberships. SAML SSO is more reliable but more complex to set up initially. Once it's working, it mostly stays working until certificates expire (see my earlier rant about SAML cert renewals). SCIM provisioning sounds great in theory - automatic user lifecycle management! In practice, it breaks when HR systems change, identity providers update, or someone modifies group mappings. When SCIM breaks, users can't access their code until you fix it. Plan for ongoing maintenance. Authentication integration isn't "set it and forget it" - it's "set it and maintain it forever."

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GitHub Enterprise Server: Infrastructure Management & Operations Guide

Configuration: Production-Ready Settings

Hardware Requirements (Real-World)

Minimum: 8 CPUs, 64GB RAM, 500GB storage (not the documented 4 CPUs/32GB RAM)
Scaling: Add 50-100GB storage per 100 repositories
Performance threshold: System degrades at 100+ active developers without tuning
HA configurations: Double all resources, dedicated storage with high IOPS required

Storage Architecture

Root filesystem: Operating system and application
User data volume: Git repositories, databases, search indices, uploads
Growth pattern: 20GB per developer per year average
Critical threshold: 90% disk usage = system failure imminent

Platform-Specific Configurations

VMware vSphere: Most stable, requires dedicated VMware expertise
AWS EC2: Flexible but complex networking, use dedicated instances not shared
Air-gapped deployments: 3-4x operational overhead, manual updates only

Resource Requirements: Time and Expertise Costs

Staffing Requirements

Minimum team: 2 dedicated platform engineers with 5+ years Linux/DevOps experience
Skills needed: PostgreSQL tuning, Redis management, Elasticsearch, SSL certificates
On-call rotation: 24/7 coverage required for production incidents

Time Investment

Initial deployment: 2-4 weeks for basic setup
Production hardening: Additional 4-8 weeks
Monthly maintenance: 8-16 hours for patches and updates
Quarterly upgrades: 4-8 hours with potential rollback scenarios

Total Cost of Ownership (500 users)

Licensing: $10,500/month
Infrastructure: $5-8K/month
Operations staff: $16-25K/month (2-3 engineers)
Tools and monitoring: $3-5K/month
Total: $35-50K/month vs $25-30K for GitHub Enterprise Cloud

Critical Warnings: Production Failure Modes

Disk Space Management

Failure pattern: 70% to 100% usage overnight from CI artifacts
Impact: Complete system failure, developers cannot access code
Solution: Alert at 60% usage, implement automated cleanup
Common cause: GitHub Actions generating gigabyte debug dumps

Database Performance Degradation

Threshold: Performance drops significantly at 500-1000 repositories
Impact: Git operations timeout, API calls fail, webhooks drop
Cause: PostgreSQL locking during concurrent Git operations
Solution: Requires dedicated database administrator

Authentication Failures

SAML certificate expiration: Zero grace period, immediate total access loss
LDAP sync breaks: Directory schema changes break user provisioning
Impact: 200+ developers unable to access repositories
Prevention: Monthly certificate renewal testing, direct line to directory team

Network Issues

Webhook delivery failure: Silent failures break CI/CD pipelines
Git operation timeouts: Firewall rule changes cause intermittent failures
Detection: Often discovered during critical deployments

Backup and Recovery Reality

Documentation claims: 4-8 hour RTO
Actual experience: 12+ hours for complete restoration
Missing dependencies: DNS, load balancers, certificates not included in backups
Testing requirement: Monthly restore validation to prevent disaster recovery theater

Decision Criteria: When to Choose GitHub Enterprise Server

Valid Use Cases

Regulatory compliance: Cannot use cloud services due to government/industry requirements
Air-gapped environments: Defense, financial, healthcare with no internet connectivity
Complete audit control: Need detailed logs of all code access and modifications
Legacy system integration: Complex on-premises workflows that cannot migrate

When Cloud is Better

Limited operational expertise: Team lacks dedicated platform engineering resources
Predictable scaling: Cloud provides automatic scaling without infrastructure planning
Faster feature access: Cloud gets new features 6-12 months before on-premises
Reduced complexity: Eliminate infrastructure, backup, security patch management

Implementation Reality: What Official Documentation Doesn't Cover

Default Settings That Fail in Production

Memory allocation: Default PostgreSQL settings cause performance issues
Log rotation: Default log retention fills disk space rapidly
Background job processing: Default Redis configuration causes queue backlogs

Upgrade Process Challenges

Timing estimates: Double all documented upgrade timeframes
Database migrations: Can extend maintenance windows from 45 minutes to 3+ hours
Rollback complexity: Failed upgrades require manual intervention, not automated rollback

High Availability Limitations

Failover time: 5-10 minutes for "automatic" failover plus validation time
Data synchronization: Replica lag can cause lost webhooks and data inconsistency
Operational complexity: HA adds significant networking and storage requirements

Security and Compliance Overhead

Monthly security patches: 24-hour emergency patching requirements
Vulnerability management: Integration with enterprise security tools required
Audit logging: SIEM integration requires custom parsing scripts

Migration Complexity: Moving Between Platforms

GitHub Enterprise Server to Cloud

Timeline: 4-6 months for 200+ developer organizations
Breaking changes: SSO configuration, webhook URLs, API integrations
Manual work: Team permissions, CI/CD pipeline updates, developer tooling
Hidden complexity: Hardcoded server IPs, custom scripts, integration dependencies

Cloud to Enterprise Server

Infrastructure lead time: 2-4 months for proper production deployment
Operational readiness: Staff hiring and training adds 3-6 months
Feature gaps: Some cloud features unavailable on-premises

Operational Intelligence: Community Wisdom

Performance Thresholds

UI becomes unusable: Above 1000 spans in distributed tracing
Search index corruption: Occurs during peak usage when rebuilds are impossible
Memory leak patterns: 3-week cycles requiring scheduled restarts

Common Misconceptions

"Set and forget it": Requires ongoing operational attention
"Same as GitHub.com": Missing features, delayed updates, different performance
"Easy migration": Complex organizational change management required

Tool Quality Assessment

Built-in monitoring: Shows pretty graphs but misses actionable metrics
Backup utilities: Reliable for data, unreliable for complete system restoration
High availability: Marketing promise vs engineering reality gap
Community support: Active forums but official support quality varies

Success Factors

Test everything: Backup restoration, certificate renewal, upgrade procedures
Monitor proactively: External monitoring catches issues built-in dashboards miss
Plan for 3x: Documentation timelines, hardware requirements, operational overhead
Maintain expertise: Dedicated platform engineering team with Linux/database skills

This guide represents operational reality based on dozens of production deployments, focusing on the intelligence needed to successfully implement and maintain GitHub Enterprise Server in enterprise environments.

Useful Links for Further Investigation

Essential GitHub Enterprise Server Resources

Link	Description
GitHub Enterprise Server Administration Guide	The official docs are comprehensive but the examples never work in production. Good reference material once you figure out the quirks, but expect to spend time on Stack Overflow filling in the gaps.
System Overview and Architecture	Actually useful for understanding what you're getting into. The architecture diagrams are accurate and help when things go sideways at 3am.
Installation Guides by Platform	The 'quick start' guides assume you have their exact dev environment. VMware docs are solid, AWS guides miss real-world VPC scenarios. Skip the examples, use this [Stack Overflow thread](https://stackoverflow.com/questions/tagged/github-enterprise) instead.
High Availability Configuration	Decent coverage of HA setup but glosses over networking requirements that will bite you. The failover docs are accurate - just test them before you need them.
Management Console Documentation	The web console is intuitive enough, but these docs help when you're debugging why authentication suddenly stopped working. Screenshots are outdated but the concepts are solid.
Backup and Disaster Recovery	The backup docs are solid - one of the few sections that actually works as documented. Recovery procedures are thorough, just budget 4x longer than the estimated times.
Monitoring and Performance	Built-in dashboards show pretty graphs but miss the metrics that actually matter. The external monitoring integration steps work, but you'll need [Datadog's own GitHub Enterprise guide](https://docs.datadoghq.com/integrations/github/) for production setups.
Command-Line Administration Tools	Essential for when the web console is broken (which happens). The CLI commands are well documented, unlike most vendor documentation. Bookmark this section.
SAML Single Sign-On Configuration	SAML setup that works until cert renewal breaks everything. Troubleshooting section is helpful after you've already been paged at midnight. Test cert renewals quarterly or suffer.
LDAP Authentication Integration	LDAP docs assume your directory admin will actually talk to you. Performance tuning section is crucial - LDAP can bring down your entire instance if misconfigured.
SCIM User Provisioning	SCIM works great when your IdP supports it properly. Okta integration is smooth, Azure AD has quirks. The error messages are useless - good luck debugging.
Security Hardening Guide	Actually follow this guide - it covers the security basics that will get you fired if you miss them. TLS config section is thorough and accurate.
GitHub Actions for Enterprise Server	GitHub Actions setup is complex and the docs know it. Storage backend configuration is solid, runner management docs are helpful. Budget 2-3x the estimated setup time.
Self-Hosted Runners Management	Runner docs cover the basics but miss production scaling gotchas. Security section is crucial - don't run untrusted code on your runners without reading this twice.
GitHub Connect Configuration	Connect setup works as documented, which is rare. Enables some useful hybrid features but adds complexity. Only enable if you actually need the cloud integration.
GitHub Enterprise Server Release Notes	Actually read these before upgrading. GitHub buries breaking changes in the middle of feature announcements. Early 3.15-3.17 releases had performance issues they fixed later.
Upgrade Documentation	Upgrade docs are thorough but optimistic on timing. Budget 2x their estimates and have rollback plans ready. The troubleshooting section has saved my ass multiple times.
Audit Log Configuration	Audit logging works but the log format is painful to parse. SIEM integration docs are basic - you'll need custom scripts for anything useful.
GitHub Enterprise Support	Support quality varies wildly. Enterprise customers get priority but expect Level 1 to ask if you've tried turning it off and on again. Escalate quickly for production issues.
GitHub Community Discussions	Community forum where you'll find the solutions that actually work in production. Search here first before opening support tickets - real users share real fixes.
GitHub Public Roadmap	Roadmap gives you a sense of what's coming, but timelines are more like gentle suggestions. Enterprise Server features usually lag cloud by 6-12 months.
GitHub Blog - Enterprise Software	Marketing fluff mixed with actually useful technical posts. Security advisories are buried in feature announcements - [subscribe to security notifications directly](https://github.com/advisories) instead.
GitHub Skills Training	Basic training that covers GitHub.com features. Doesn't touch Enterprise Server admin tasks where you actually need help. Skip this, use the admin docs instead.
System Requirements Calculator	Minimum requirements are fictional. Multiply by 4x for production workloads. The capacity planning guidance is conservative but realistic.
GitHub Enterprise Trial	45-day trial that works exactly like production - good for testing before you commit to the operational overhead. Use this to verify your backup procedures actually work.
Professional Services	Expensive but they know GitHub Enterprise Server better than anyone. Worth it for complex migrations or if your team has never run this before. They'll save you months of troubleshooting.

GitHub Enterprise Server: Infrastructure Management & Operations Guide

Configuration: Production-Ready Settings

Hardware Requirements (Real-World)

Storage Architecture

Platform-Specific Configurations

Resource Requirements: Time and Expertise Costs

Staffing Requirements

Time Investment

Total Cost of Ownership (500 users)

Critical Warnings: Production Failure Modes

Disk Space Management

Database Performance Degradation

Authentication Failures

Network Issues

Backup and Recovery Reality

Decision Criteria: When to Choose GitHub Enterprise Server

Valid Use Cases

When Cloud is Better

Implementation Reality: What Official Documentation Doesn't Cover

Default Settings That Fail in Production

Upgrade Process Challenges

High Availability Limitations

Security and Compliance Overhead

Migration Complexity: Moving Between Platforms

GitHub Enterprise Server to Cloud

Cloud to Enterprise Server

Operational Intelligence: Community Wisdom

Performance Thresholds

Common Misconceptions

Tool Quality Assessment

Success Factors

Useful Links for Further Investigation

Essential GitHub Enterprise Server Resources

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