Compare Spec-Driven Tools

specs.md: One Framework, Three Flows

Choose Your Level of Ceremony: specs.md offers three flows from lightweight spec generation to full methodology. Pick the one that matches your project needs.

Three Flows Available

Flow	Checkpoints	Optimized For
Simple Flow	3 (phase gates)	Spec generation only, quick prototypes
FIRE Flow	Adaptive	Adaptive execution, brownfield, monorepos
AI-DLC Flow	Comprehensive	Full traceability, regulated environments

Simple

Quick specs without execution tracking

FIRE

Rapid execution with adaptive checkpoints

AI-DLC

Full methodology with DDD

Choose your flow →

Comparison Matrix

Feature	specs.md	Spec Kit	BMAD	OpenSpec	Kiro
Target Complexity	Complex systems	Small-medium	Complex/Enterprise	Any (brownfield-first)	Any
Type	Methodology + Framework	Toolkit + Agent Prompts	Multi-agent Framework	Lightweight Framework	Full IDE
Methodology	AWS AI-DLC (formal)	Spec Kit SDD	Agentic Agile	OpenSpec SDD	Kiro SDD
Design Integration	DDD default (extensible via bolt types)	Constitution file	Optional	Design-agnostic	Design-agnostic
Rituals	Mob Elaboration, Mob Construction	None	None	None	None
Phases	3 sequential	6 sequential	4 phases	Change-centric	Requirements→Design→Tasks
Agent Model	3 phase-based agents	Prompts for 15+ assistants	19 role-based agents	AGENTS.md compatible	Built-in + Subagents
Iteration Cycles	”Hours or days” (Bolts)	Variable	Variable	Variable	Variable
Brownfield Support	Yes (with model elevation)	Yes	Yes	Yes (primary focus)	Yes
IDE Lock-in	None (any IDE)	None	None	None	Kiro IDE only
VS Code Extension	Yes (visual dashboard)	No	No	CLI dashboard	Built into IDE
Open Source	Yes	Yes	Yes	Yes	Partial (Code OSS base, proprietary features)
Pricing	Free (OSS)	Free (OSS)	Free (OSS)	Free (OSS)	Free preview → $20-200/mo

Quick Comparisons

vs GitHub Spec Kit

Lightweight toolkit vs full methodology. Learn when to choose each.

vs BMAD-Method

Both target complex projects. Compare phase-based vs role-based agents.

vs Kiro (AWS)

IDE-integrated vs IDE-agnostic. Compare automation approaches.

vs OpenSpec

Full lifecycle vs change-centric. Compare brownfield strategies.

Key Differentiators

1. AI-DLC is a Formal AWS Methodology

Unlike generic “spec-driven development,” AI-DLC is defined by AWS with specific phases, rituals, and artifacts. specs.md is a faithful implementation—not an interpretation.

2. DDD as Default, Extensible via Bolt Types

Agile frameworks leave design techniques optional. AI-DLC integrates Domain-Driven Design as its default—AI applies DDD principles during decomposition, developers validate. Extensible: Use bolt types to add other methodologies like BDD, TDD, or model your own construction flow. DDD is the default, not a limitation.

3. Bolts: Batched Stories with Full Traceability

Unlike other tools with variable iteration cycles, specs.md uses Bolts—planned batches of related stories executed together by an agent. Each Bolt follows DDD steps during execution, and artifacts created during construction provide full traceability of what was built and how.

Intent → Units → Stories → Bolt (batched stories)
                              ↓
                    DDD Construction Steps
                              ↓
                    Traceable Artifacts

4. Reversed Conversation Direction

In AI-DLC, AI initiates and directs conversations. AI proposes breakdown, trade-offs, designs. Humans validate and approve. This is the opposite of most tools where humans prompt AI.

5. Mob Rituals for Team Alignment

Mob Elaboration (Inception) and Mob Construction condense weeks of sequential work into hours while achieving deep alignment between team and AI.

6. Built for Complex Systems

AI-DLC explicitly targets systems with “continuous functional adaptability, high architectural complexity, numerous trade-offs management, scalability, integration and customization requirements.”Simpler projects → specs.md (Simple Flow).

Vendor Lock-in

Tool	Lock-in Risk
specs.md	None - works with any IDE/AI
Spec Kit	None - works with 15+ AI assistants
BMAD	None - works with any IDE/AI
OpenSpec	None - AGENTS.md compatible with any tool
Kiro	High - Kiro IDE + Claude Sonnet only

Cost Comparison

Tool	Cost
specs.md	$0 + API tokens
Spec Kit	$0 + API tokens
BMAD	$0 + API tokens
OpenSpec	$0 + API tokens
Kiro Pro	$20/month (225 vibe + 125 spec/mo)
Kiro Pro+	$39/month (450 vibe + 250 spec/mo)
Kiro Power	$200/month (2,250 vibe + 1,250 spec/mo)

Roadmap

Feature	Status	Description
AI-DLC Flow	Available	Full methodology with DDD, 4 agents
Simple Flow	Available	Spec generation (requirements, design, tasks)
FIRE Flow	Available	Rapid execution with adaptive checkpoints
Brownfield Support	Available	First-class in FIRE Flow
Monorepo Support	Available	Hierarchical standards in FIRE Flow
VS Code Extension	Available	Visual dashboard for all flows

Start where you are: Use Simple for quick specs, FIRE for rapid execution, or AI-DLC for full methodology. No upgrade path required—each flow is designed for different needs.

Request a Feature

Have a feature request or feedback? Let us know what you’d like to see in specs.md.

The AI-Native SDLC: Reimagined

Why the Agentic Age demands a new methodology

What is AI-DLC?

Deep dive into AI-DLC methodology

Getting Started

Framework

Ideation Flow

FIRE Flow

AI-DLC Flow

Simple Flow

Compare

Resources

Compare Spec-Driven Tools

specs.md: One Framework, Three Flows

Three Flows Available

Simple

FIRE

AI-DLC

Comparison Matrix

Quick Comparisons

vs GitHub Spec Kit

vs BMAD-Method

vs Kiro (AWS)

vs OpenSpec

Key Differentiators

1. AI-DLC is a Formal AWS Methodology

2. DDD as Default, Extensible via Bolt Types

3. Bolts: Batched Stories with Full Traceability

4. Reversed Conversation Direction

5. Mob Rituals for Team Alignment

6. Built for Complex Systems

Vendor Lock-in

Cost Comparison

Roadmap

Request a Feature

Further Reading

The AI-Native SDLC: Reimagined

What is AI-DLC?

​specs.md: One Framework, Three Flows

​Three Flows Available

Simple

FIRE

AI-DLC

​Comparison Matrix

​Quick Comparisons

vs GitHub Spec Kit

vs BMAD-Method

vs Kiro (AWS)

vs OpenSpec

​Key Differentiators

​1. AI-DLC is a Formal AWS Methodology

​2. DDD as Default, Extensible via Bolt Types

​3. Bolts: Batched Stories with Full Traceability

​4. Reversed Conversation Direction

​5. Mob Rituals for Team Alignment

​6. Built for Complex Systems

​Vendor Lock-in

​Cost Comparison

​Roadmap

Request a Feature

​Further Reading

The AI-Native SDLC: Reimagined

What is AI-DLC?

specs.md: One Framework, Three Flows

Three Flows Available

Comparison Matrix

Quick Comparisons

Key Differentiators

1. AI-DLC is a Formal AWS Methodology

2. DDD as Default, Extensible via Bolt Types

3. Bolts: Batched Stories with Full Traceability

4. Reversed Conversation Direction

5. Mob Rituals for Team Alignment

6. Built for Complex Systems

Vendor Lock-in

Cost Comparison

Roadmap

Further Reading