I’ve been looking into consolidating our AI API usage, and the idea of having 400+ models available under a single subscription is attractive from a cost perspective. But I’m running into analysis paralysis.
Right now, we’re paying separately for OpenAI, Anthropic, and occasionally experimenting with others. If I could access all of them through one platform, that’s simpler. But the real question is: without needing to audit each model individually, how do you know which model to use for what task?
Like, if I’m building a workflow where I need to extract structured data from a PDF, then that same workflow needs to generate a summary, then maybe make a decision based on what it finds—do I use the same model for all three steps, or do I switch between them? Is there a performance difference that matters?
I feel like having 400+ options could actually be paralyzing. Do I need to be a model expert to make good choices, or is there a framework for thinking about this?
How do other people handle this? Do you pick a model and stick with it, or do you customize per task?
The beauty of having access to 400+ models is actually that you don’t need to be an expert. Latenode makes this simple by letting you pick based on what you need the model to do, not by forcing you to compare benchmark charts.
For structured data extraction, you’d want something precise like Claude. For fast, simple decision-making, GPT-4 is solid. For summarization, you might pick something lighter that’s cheaper per token.
What matters is Latenode lets you customize per task without complexity. You pick the right model for that step in your workflow. One task might use Claude for parsing, then GPT for summary, then another model for decision logic. The platform handles all of it under one subscription.
You’re not stuck with one model. You optimize for the work each step does, not for convenience. And since it’s one subscription, you’re not juggling separate API keys or billing.
I went through this exact thought process last year. The paralysis is real because there’s genuinely not one “best” model for everything. What I found is that most teams end up settling on 2-3 models they trust and using those for the majority of work.
For us, that’s Claude for precision tasks, GPT-4 for general reasoning, and a faster model for simple classification. We still experiment with others for specialized needs, but most of the time, those three handle it.
The framework that helped: instead of thinking about 400 options, think about your use cases first. What are the five to ten tasks your workflow actually needs to do? Then pick a model for each one. You’re choosing among a small set, not 400.
If you can consolidate under one subscription, do it. You’ll naturally gravitate toward the models that work for you, and you’ll experiment with others when you hit limitations. That’s much better than managing separate SDK integrations.
Model selection shouldn’t be overthought. Start by categorizing what your workflows need: fast inference, high accuracy, specialized domains like medical or legal analysis. Each category has clear winners. For general structured extraction and reasoning, Claude and GPT-4 are solid. For speed-critical tasks, smaller models work fine.
The consolidated subscription approach is valuable because you can test different models against your specific data without worrying about API costs spiraling. This experimentation phase is where you discover what actually matters for your use cases. Most organizations find they use three to five models regularly with occasional exploration into others.
Consolidating AI models under one subscription eliminates implementation friction. Regarding model selection, the decision framework is straightforward: map your workflow tasks to model strengths. Extraction tasks require precision, summarization benefits from semantic understanding, classification can use lighter models. In practice, teams quickly identify two to three primary models matching their needs and use those for 80% of work.
The advantage of having 400+ available is experimentation flexibility. When you encounter edge cases or new requirements, testing alternatives is cost-effective. This beats managing separate API keys and billing structures across different providers.
Map tasks to model strengths. Extraction needs precision (Claude), summarization needs understanding (GPT-4). Most teams use 3-4 models regularly. Consolidation lets you experiment without cost overhead.