SuperML.org AI Calculators

AI Architecture Pattern Selector

Answer a few questions about your use case and get a recommended AI architecture pattern — RAG, GraphRAG, NL-to-SQL, Fine-Tuning, Agentic, or Hybrid — with trade-off analysis.

Your Use Case

Primary data type

Latency target

Auditability need

Regulatory sensitivity

Knowledge base size

Budget sensitivity

Additional requirements

Configure your requirements and click Select Pattern

Your recommended architecture pattern will appear here

All Architecture Patterns

RAG

Ground LLM answers in your own documents

Complexity: MediumCost: Medium

Internal knowledge bases · Document Q&A

GraphRAG

Traverse relationships, not just similarity

Complexity: Very HighCost: High

Research assistants · Biomedical / pharma

NL-to-SQL

Query your databases in plain English

Complexity: MediumCost: Low

Business intelligence · Analytics chatbots

Fine-Tuning

Bake domain knowledge into model weights

Complexity: HighCost: High

Classification / extraction at scale · Narrow-domain chat (legal, medical, code)

Agentic

Let the LLM plan, act, and loop

Complexity: Very HighCost: High

Autonomous research agents · Code generation and execution

Hybrid RAG

Best of both — docs and databases

Complexity: HighCost: Medium

Enterprise knowledge assistants · Platforms with both documents and databases

Architecture Selection Principles

  • Match pattern to data type first. Unstructured text → RAG family. Structured databases → NL-to-SQL. Mixed → Hybrid. Entity networks → GraphRAG.
  • Latency kills complexity. Real-time requirements rule out GraphRAG, Agentic, and most Hybrid approaches. Fine-tuning or cached RAG are the only paths to sub-500ms LLM responses.
  • Regulated decisions need deterministic audit trails. NL-to-SQL produces SQL. RAG produces cited chunks. Agentic produces reasoning traces — harder to audit. Fine-tuning produces nothing traceable.
  • Agentic is not a universal upgrade. Multi-step agent loops are expensive, slow, and hard to debug. Use them only when the task genuinely requires planning and tool composition.
  • Start with RAG, add complexity only when it fails. Most enterprise knowledge Q&A problems are solved well by RAG. Add a semantic layer, graph, or agent capability only when you can measure a specific gap.

How to use AI Architecture Pattern Selector for AI Architects

1. What this calculator does

Matches workload and organizational constraints to the most suitable architecture pattern, reducing expensive re-platforming caused by early pattern misalignment.

2. When to use it

  • At project inception before selecting a default AI architecture.
  • When current implementation shows quality, latency, or governance mismatch.
  • During roadmap planning for new features requiring different reasoning depth.

3. Inputs explained

  • Data topology and freshness requirements.
  • Latency, quality, and reliability targets for user-facing outcomes.
  • Governance and compliance constraints by business domain.
  • Operational maturity for orchestration, observability, and lifecycle management.

4. Formula / decision logic

  • Pattern fit score evaluates workload compatibility across candidate architectures.
  • Hard constraints eliminate infeasible patterns early.
  • Trade-off matrix highlights cost, complexity, and control implications.
  • Output recommends primary pattern and fallback migration path.

5. Example scenario

A product team starts with basic RAG but plans workflow automation and policy-sensitive operations. The selector recommends staged evolution: RAG baseline, then selective agent orchestration with governance controls for high-risk flows.

6. Architecture implications

  • Pattern choice determines long-term platform complexity and operating cost.
  • Governance and observability requirements should influence pattern decisions upfront.
  • Hybrid architectures are often optimal but require disciplined routing boundaries.
  • Pattern migration planning prevents brittle monolithic AI system growth.

7. Common mistakes

  • Selecting architectures by trend momentum rather than workload evidence.
  • Jumping to full agentic design without deterministic control requirements.
  • Ignoring governance impact during early technical prototyping.
  • Treating architecture choice as irreversible instead of stage-gated.

8. Related calculators

RAG Chunking Calculator RAG Vector DB Cost Calculator NL-to-SQL Complexity Calculator Agent Cost Calculator AI Governance Readiness Checker GPU vs API Break-Even Calculator

9. FAQ

Why is pattern selection usually wrong in early AI projects?

Teams often choose architecture based on trend familiarity rather than workload constraints. Pattern selection should be driven by data shape, latency requirements, governance risk, and update frequency.

Can one architecture pattern serve every use case?

Rarely. Most production platforms evolve toward hybrid patterns where retrieval, agents, and deterministic workflows are combined selectively per task type.

When should we move from RAG to more complex agentic design?

Move only when task decomposition, tool interaction, and decision loops are necessary for business outcomes. Complexity should be justified by measurable gains.

Share This Calculator

X LinkedIn Facebook Reddit WhatsApp Telegram Email

Help others discover this calculator by sharing it!