# Decisions, made deliberately.

Source: https://customlabs.io/compare/
Updated: 2026-09-12

Compare

# Decisions, made deliberately.

The architecture calls that come up on every engagement, framed as a choice: a comparison matrix, the tradeoffs, and a stated default recommendation.

Agentic autonomy vs Deterministic pipelines · 7 min read

## Agents vs Pipelines: When Autonomy Is Worth the Reliability Cost

Constrain to a pipeline until [evals](https://customlabs.io/glossary/eval-suite/) prove the workflow genuinely needs an agent's judgment. Default to less agent, not more.

[Shipping to Production](https://customlabs.io/topics/shipping-to-production/)[Evals & Observability](https://customlabs.io/topics/evals-observability/)

[Read the guide →](https://customlabs.io/compare/agents-vs-pipelines/)

Self-hosted open-weight models vs Frontier API models · 7 min read

## Open-Weight Models vs Frontier APIs: The Real Cost/Control Tradeoff

Default to a frontier API until volume, compliance, or control needs force the switch. Self-hosting is an operations commitment first and a cost decision second.

[AI Cost & Efficiency](https://customlabs.io/topics/ai-cost-efficiency/)[Strategy & Architecture](https://customlabs.io/topics/strategy-architecture/)

[Read the guide →](https://customlabs.io/compare/open-weight-vs-frontier-api/)

Retrieval-augmented generation (RAG) vs Fine-tuning · 8 min read

## RAG vs Fine-Tuning: Which One Actually Solves Your Problem

Default to retrieval. Fine-tune only when you need a fixed output form or a latency budget retrieval can't hit, not just 'better answers.'

[Retrieval & RAG](https://customlabs.io/topics/retrieval-rag/)[Strategy & Architecture](https://customlabs.io/topics/strategy-architecture/)

[Read the guide →](https://customlabs.io/compare/rag-vs-fine-tuning/)

Dedicated vector database vs Postgres + pgvector · 8 min read

## Vector Database vs pgvector: Do You Actually Need a Dedicated Store

Start on pgvector. Migrate to a dedicated vector database only when you hit a measured scale, latency, or feature ceiling it can't clear, not preemptively.

[Retrieval & RAG](https://customlabs.io/topics/retrieval-rag/)[AI Cost & Efficiency](https://customlabs.io/topics/ai-cost-efficiency/)

[Read the guide →](https://customlabs.io/compare/vector-database-vs-pgvector/)