Blackwell Systems Blog

Software Release Cycles Explained: Alpha, Beta, RC, and Everything In Between

A complete guide to software release stages from alpha to stable. Learn when to use each stage, which combinations are most popular, and how modern projects structure their release cycles.

tags
#Software-Engineering #Release-Management #Versioning #Devops #Software-Development #Best-Practices #Semantic-Versioning #Ci-Cd #Deployment
categories
Software-Engineering Best-Practices
published
reading time
9 minutes

What is a Release Cycle?

A release cycle is the journey software takes from initial development to stable production use. Each stage signals the maturity level and helps users understand what to expect:

  • Early stages (alpha, beta) - Expect breaking changes and bugs
  • Testing stages (RC) - Nearly ready, testing for final issues
  • Stable stages (GA, stable) - Production-ready, safe to deploy
  • Long-term stages (LTS) - Extended support commitment

Not every project uses every stage. The choice depends on project size, user base, and release philosophy.

Stage Breakdown: What Each Means

Alpha

Meaning: Feature-incomplete, internal testing, expect major changes

Characteristics:

  • Core features still being built
  • APIs may change dramatically
  • Frequent breaking changes between releases
  • Often internal-only or invite-only testing
  • May crash or have data loss bugs

Versioning:

  • 0.1.0-alpha.1
  • 1.0.0-alpha.3
  • v2.0.0-alpha

Who uses it:

  • Developers working on the project
  • Early adopters willing to report bugs
  • Internal QA teams

Example: “We’re releasing alpha builds weekly with experimental features. Expect breaking changes.”

Beta

Meaning: Feature-complete, external testing, stabilizing

Characteristics:

  • All planned features implemented
  • APIs mostly stable (may have minor changes)
  • External user testing encouraged
  • Bug fixes and polish, no new features
  • May still have known issues

Versioning:

  • 1.0.0-beta.1
  • 2.5.0-beta
  • v3.0.0-beta.2

Who uses it:

  • Adventurous users testing new features
  • Companies testing compatibility
  • Beta testing programs

Example: “Beta 3 includes all planned v2.0 features. We’re now focusing on stability and bug fixes.”

Release Candidate (RC)

Meaning: Potentially final release, last-minute testing

Characteristics:

  • Feature-complete and stable
  • No new features or API changes
  • Only critical bug fixes allowed
  • Should be nearly identical to final release
  • If no issues found, RC becomes the final release

Versioning:

  • 1.0.0-rc.1
  • 4.0.0-rc4
  • v2.0-rc1

Who uses it:

  • Production users doing final validation
  • CI/CD pipeline testing
  • Pre-production environments

Example: “RC3 is a release candidate. If no critical issues are found, it will become v4.0.0.”

How many RCs?

  • Small projects: 1-3 RCs
  • Medium projects: 2-5 RCs
  • Large projects: 5-10+ RCs (Linux kernel sometimes reaches rc8-rc10)

Stable / General Availability (GA)

Meaning: Production-ready, recommended for all users

Characteristics:

  • Thoroughly tested
  • No known critical bugs
  • Full documentation
  • Supported with updates
  • Safe for production deployment

Versioning:

  • 1.0.0
  • 2.5.0
  • v4.0.0 (no suffix = stable)

Who uses it:

  • Everyone
  • Production systems
  • Conservative deployments

Example: “Version 3.0.0 is now generally available and recommended for production use.”

Long-Term Support (LTS)

Meaning: Extended support commitment, stability focus

Characteristics:

  • Guaranteed updates for extended period (2-5+ years)
  • Security patches throughout support window
  • Critical bug fixes only, no new features
  • Multiple LTS versions may coexist

Versioning:

  • 18.04 LTS (Ubuntu style - year.month)
  • 16.x LTS (Node.js style - major version)
  • v2.0.0-lts

Who uses it:

  • Enterprise users requiring stability
  • Systems with long deployment cycles
  • Users who can’t upgrade frequently

Example: “Node.js 20.x LTS will receive security updates until April 2026.”

Common Release Strategies

Strategy 1: Full Cycle (Alpha → Beta → RC → Stable)

Used by: Large projects, established software, enterprise tools

Timeline: 3-12 months from alpha to stable

Example flow:

  1. 1.0.0-alpha.1 - Initial features (2-3 months)
  2. 1.0.0-alpha.5 - Feature-complete
  3. 1.0.0-beta.1 - External testing (1-2 months)
  4. 1.0.0-beta.3 - Stabilization
  5. 1.0.0-rc.1 - Release candidate (2-4 weeks)
  6. 1.0.0 - Stable release

Real examples:

  • Python: Multiple alphas, 1-2 betas, 1-2 RCs
  • Kubernetes: 3-4 alphas, 3-4 betas, 3-4 RCs per minor version
  • Ubuntu: Multiple alphas/betas leading to LTS releases every 2 years

Pros:

  • Clear quality signals
  • Multiple feedback loops
  • Lower risk for users

Cons:

  • Slower release cadence
  • More coordination overhead

Strategy 2: Beta → RC → Stable

Used by: Medium projects, libraries, developer tools

Timeline: 1-3 months from beta to stable

Example flow:

  1. 2.0.0-beta.1 - Feature-complete, testing (1-2 months)
  2. 2.0.0-beta.4 - Stabilizing
  3. 2.0.0-rc.1 - Release candidate (1-2 weeks)
  4. 2.0.0-rc.3 - Final testing
  5. 2.0.0 - Stable release

Real examples:

  • Go: No alpha stage, starts with beta
  • Rust: 6-7 beta releases, then stable (they skip RC naming)
  • Most npm packages: Beta testing, then release

Pros:

  • Faster than full cycle
  • Still provides safety net
  • Clear stabilization period

Cons:

  • Less time for major changes

Strategy 3: RC → Stable (Skip Alpha/Beta)

Used by: Small projects, internal tools, rapid iteration

Timeline: 1-4 weeks from RC to stable

Example flow:

  1. 3.0.0-rc.1 - Release candidate (1-2 weeks)
  2. 3.0.0-rc.2 - Final fixes
  3. 3.0.0 - Stable release

Real examples:

  • Many open-source libraries
  • Internal tools with limited users
  • Projects with continuous integration

Pros:

  • Fast releases
  • Minimal overhead
  • Good for mature codebases

Cons:

  • Higher risk of shipping bugs
  • Less user feedback

Strategy 4: Continuous Delivery (No Pre-releases)

Used by: SaaS products, web applications, modern startups

Timeline: Continuous (daily/weekly releases)

Example flow:

  • main branch is always stable
  • Feature flags control new features
  • Deploy multiple times per day
  • No formal pre-release stages

Real examples:

  • Facebook/Meta
  • Google services
  • Netflix
  • Most modern web applications

Pros:

  • Fastest time to users
  • Immediate feedback
  • No release coordination

Cons:

  • Requires excellent testing automation
  • Not suitable for installable software
  • Users have no control over updates

Semantic Versioning Patterns

Most projects follow Semantic Versioning (semver):

MAJOR.MINOR.PATCH-prerelease+build

Examples:
1.0.0-alpha.1
2.5.0-beta.3
3.0.0-rc.2
4.0.0

MAJOR: Breaking changes (1.0.0 → 2.0.0) MINOR: New features, backward compatible (1.0.0 → 1.1.0) PATCH: Bug fixes (1.0.0 → 1.0.1)

Pre-release ordering:

1.0.0-alpha.1
1.0.0-alpha.2
1.0.0-beta.1
1.0.0-rc.1
1.0.0

When to Use Each Stage

Use Alpha when:

  • Core architecture still being decided
  • Major features incomplete
  • API design still changing
  • You need early feedback on direction
  • Breaking changes are expected

Use Beta when:

  • All features implemented
  • API mostly stable
  • Ready for external testing
  • Collecting bug reports
  • Measuring real-world performance

Use RC when:

  • Confident in stability
  • No more features planned
  • Final validation needed
  • Ready to commit to this as release
  • Want to signal “almost there” to users

Skip stages when:

  • Small project with few users
  • Internal tool
  • You have excellent test coverage
  • Continuous delivery model
  • Mature codebase with low risk

Real-World Examples

Linux Kernel

Strategy: Extended RC cycle

Pattern:

  • 2-week merge window (new features)
  • 7-10 RC releases over 8-10 weeks
  • rc1: Right after merge window
  • rc7-rc10: Final stabilization
  • Stable release

Why: Massive codebase, hardware compatibility critical, millions of users

Node.js

Strategy: Current + LTS tracks

Pattern:

  • Even-numbered majors (18.x, 20.x) → LTS
  • Odd-numbered majors (19.x, 21.x) → Current (short-lived)
  • LTS supported for 30 months
  • Active LTS → Maintenance LTS

Why: Enterprise users need stability, developers want latest features

Chrome Browser

Strategy: Beta → Stable with channels

Pattern:

  • Canary (daily builds)
  • Dev (weekly updates)
  • Beta (monthly updates, 4-6 weeks before stable)
  • Stable (every 4 weeks)

Why: Rapid iteration, multiple risk tolerance levels

PostgreSQL

Strategy: Beta → RC → Stable with long support

Pattern:

  • Multiple beta releases
  • 1-2 RC releases
  • Major version every year
  • Each major supported for 5 years

Why: Database stability critical, enterprise users

Feature Flags Over Pre-releases

Many modern projects use feature flags instead of traditional pre-releases:

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if (featureFlags.newEditor) {
  // New code path
} else {
  // Stable code path
}

Benefits:

  • Deploy unfinished features to production
  • Gradual rollout (1% → 10% → 100%)
  • Instant rollback without deployment
  • A/B testing

Used by: Facebook, Google, Netflix, Spotify

Trunk-Based Development

Single main branch, always deployable:

main (always stable)
  ↓
feature branches merge daily
  ↓
automated testing
  ↓
deploy multiple times per day

Requires:

  • Excellent CI/CD
  • Comprehensive test coverage
  • Feature flags
  • Automated rollback

Calendar Versioning (CalVer)

Date-based versions instead of semantic:

Ubuntu: 24.04, 24.10 (year.month)
pip: 24.0 (year.sequential)

Pros:

  • Clear when released
  • No confusion about “what’s newer”

Cons:

  • Doesn’t signal compatibility

Choosing Your Strategy

Small project (<10 users):

  • RC → Stable, or just Stable
  • Release when ready
  • Version numbers optional

Medium project (10-1000 users):

  • Beta → RC → Stable
  • Clear communication about stability
  • Semantic versioning

Large project (1000+ users):

  • Alpha → Beta → RC → Stable
  • Multiple feedback channels
  • LTS for enterprise users

SaaS/Web Application:

  • Continuous delivery
  • Feature flags
  • No version numbers (just “latest”)

Library/Framework:

  • Full cycle for major versions
  • RC → Stable for minor versions
  • Semantic versioning strictly

Anti-Patterns to Avoid

Perpetual Beta:

  • Staying in beta for years
  • Users don’t know if it’s safe
  • Example: Gmail was “beta” for 5 years

Too Many RCs:

  • More than 10 RCs suggests fundamental issues
  • Consider calling it “beta” instead
  • Or ship and iterate with patches

Skipping Testing Stages:

  • Alpha → Stable with no intermediate testing
  • High risk for users
  • Only acceptable for very small projects

Breaking Changes in Patch Releases:

  • Violates semantic versioning
  • Breaks user trust
  • Should be MAJOR version bump

No Clear Criteria:

  • “We’ll release RC when it feels ready”
  • Define exit criteria for each stage
  • Automate quality gates

Practical Implementation

Example: Planning a 2.0 Release

Month 1-2: Alpha

  • Feature branches merged to develop
  • Weekly alpha releases
  • Internal testing
  • Exit criteria: All planned features merged

Month 3: Beta

  • Feature freeze
  • Beta releases every 2 weeks
  • Public beta testing program
  • Exit criteria: No P1 bugs, <5 P2 bugs

Month 4: RC

  • Code freeze (only critical fixes)
  • RC every week
  • Production validation
  • Exit criteria: 2 weeks with zero critical bugs

Release Day:

  • Tag final version
  • Update documentation
  • Deploy to production
  • Announce release

Documentation Template

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# Release Status

## Current: v2.0.0-rc.2 (Release Candidate 2)

**Stability:** Production-ready pending final validation
**Recommended for:** Testing in production-like environments
**Not recommended for:** Critical production systems yet

## Known Issues
- [Minor bug in edge case](link)

## Timeline
- Alpha 1: June 1
- Beta 1: July 1
- RC 1: Aug 1
- Target GA: Aug 15

## Support
- v1.x: Supported until v2.0.0 GA + 6 months
- v2.0.0-rc: Report issues on GitHub

Conclusion

There’s no one-size-fits-all approach to release cycles. The key principles:

  1. Signal stability clearly - Users should know what to expect
  2. Match your process to your users - Enterprise vs consumers have different needs
  3. Be consistent - Once you choose a strategy, stick with it
  4. Document your process - Make expectations clear
  5. Use automation - CI/CD enables more frequent, safer releases

The trend is toward faster releases with better automation. But traditional staged releases (alpha/beta/RC) still have value for projects where stability matters more than speed.

Choose your strategy based on:

  • Project size and complexity
  • User base and risk tolerance
  • Team size and release capability
  • Industry expectations (database vs web app)

The best release cycle is the one that gives you confidence to ship and your users confidence to adopt.

Resources

Want to dive deeper? Check out these related topics:

  • Feature flags and progressive delivery
  • Trunk-based development vs GitFlow
  • Zero-downtime deployment strategies
  • Version negotiation in APIs