What does 'low cost' actually mean in AI workflows?

Low cost means selecting the right model tier for each task so you never overpay for capability you don't need. A classification step on a $5/1M-token model is waste. The same step on a $0.03/1M-token model delivers identical results.

How do I choose the right model for a workflow step?

Classify each step's complexity: extraction and classification tasks need Eco or Cost tier. Structured generation fits Balanced tier. Complex reasoning and regulated compliance require Quality tier. Cromus automates this with Model Fit scoring in the Skill Studio.

What does a Croms score tell me about cost waste?

Croms quantify preventable waste by weighing cost waste (35%), latency overhead (25%), failure risk (25%), and structural gaps (15%). A high score on a cheap model means you're still wasting money through redundant steps, missing caching, or structural inefficiencies.

Can I use open-source models to reduce costs further?

Yes. Self-hosted open-weight models like Llama 4 Maverick eliminate per-token API costs. Cromus includes an Open Source simulation mode estimating costs across small (7B), medium (13B), and large (70B+) compute tiers.

How do I get started with cost optimization?

Go to cromus.ai/demo and paste any SOP or workflow description. Cromus compiles it into a structured SKILL.md, simulates costs across all five model tiers, calculates your Croms waste score, and generates optimization recommendations — no account required.

Low Cost AI Workflows: Model Selection Guide — Cromus

Most teams default to frontier models for every workflow step, resulting in 3-10x overspend on tasks that don't need premium capability. Low cost AI workflows require matching each step to the right model tier — Eco, Cost, Balanced, Quality, or Open Source — based on task complexity, not model popularity.

Cromus organizes 60+ verified models across 11 providers (OpenAI, Anthropic, Google, xAI, DeepSeek, Mistral, Meta, Cohere, Amazon, Microsoft, Qwen) into five tiers with verified pricing. The Eco tier (Mistral Small 4 at $0.15/1M tokens input) handles classification and extraction. The Cost tier (DeepSeek V4 Flash at $0.14/1M tokens input) covers general tasks. The Balanced tier (Claude Sonnet 4.6 at $3.00/1M tokens) handles coding and agents. The Quality tier (GPT-5.5 at $5.00/1M tokens, Claude Opus 4.7 at $5.00/1M tokens) covers complex reasoning. Open Source (Llama 4 Maverick, Gemma 4 27B) eliminates per-token costs entirely.

Croms (units of preventable AI workflow waste) identify cost waste through a weighted formula: 35% cost waste, 25% latency overhead, 25% failure risk, 15% structural gaps. A high Croms score on a cheap model still indicates waste — redundant steps, missing caching, and serial execution multiply per-run costs across thousands of executions.

Real-world model downgrades show significant savings: keyword clustering moved from Quality to Cost tier saves 94%, GEO audits moved from Quality to Balanced tier saves 40%, while schema QA stays on Quality tier because downgrading increases failure rate and rework cost.