I’m trying to figure out the financial reality of RAG. Like, everyone talks about having 400+ models available in one subscription, but I haven’t seen much clarity on how cost factors into model selection.
Do cheaper models for generation actually work if you’re using premium models for retrieval? Or does cutting costs somewhere create quality problems that cascade through the workflow?
I’m building something that might run thousands of requests monthly. How do people actually approach the cost-vs-performance tradeoff without either overspending or shipping garbage outputs?
Does the execution-based pricing model actually make it easier to experiment with different model combinations, or does cost still become a bottleneck?
Cost matters but it’s not binary. With execution-based pricing, you pay per run, not per API. That fundamentally changes how you think about cost.
You can use premium models for retrieval where accuracy matters and cheaper models for generation. The blended cost per workflow execution is way lower than managing separate APIs for different models.
I’ve built workflows that use GPT-4 for retrieval (needs high accuracy) and a faster model for generation (just needs to format well). Total cost per execution is maybe 30% of what it’d be if both were premium models.
The key insight: retrieval quality gates everything. If retrieval fails, generation can’t fix it. So weight your model spending there. Generation is more forgiving for optimization.
Latenode’s model access in one subscription means you can test combinations without infrastructure changes. That experimentation is where you actually find good cost-performance ratios.
Started with premium models for both retrieval and generation. Costs were reasonable for low volume, but once I hit higher volumes, it became an issue.
Reasoned through it: retrieval accuracy directly impacts output quality. Generation can be cheaper as long as it’s reliable. So I tested a cheaper generation model against our quality standards.
Turned out it performed fine. Dropped costs by almost 40% once I made that swap.
The execution model helps because testing different combinations is cheap. You’re not locked into one model combination for cost reasons. You can actually experiment and find the balance that works for your use case.
Cost optimization in RAG follows a tiered approach. Retrieval quality is non-negotiable—it determines what information is available for generation. Generation has more flexibility for cost optimization; the quality requirements are usually lower than retrieval.
Execution-based pricing enables rapid model experimentation. In traditional API-based approaches, trying different models incurs per-call costs immediately. Latenode’s model access within one subscription allows testing without multiplying per-call expenses.
Optimal strategies: premium retrieval model, test multiple generation models for quality-cost balance, measure total execution cost per workflow until finding acceptable performance-cost ratio.
invest in retrieval quality, optimize generation cost. execution model lets you test combos cheap. find your quality threshold, then cut costs under it.