Large Language Models (LLMs) made an explosion on the product development scene. They fundamentally changed what smaller SaaS teams can build. Features that once required entire support teams, analytics departments, or custom rule engines can now be shipped by a handful of engineers (or a solo non technical founder proving a concept). Deciding to embrace AI is a game changer.
This post is not a technical deep dive. It’s a framework for thinking about LLMs from a product perspective.
What an LLM is
At a high level, a LLM is trained on massive amounts of text and datasets to predict the most likely response given an input. It doesn’t ‘understand’ your business, your users, or your intent. It’s not smart. It’s a game of math and numbers and banks on probability. That’s it.
This distinction is important because most people assume AI = intelligence instead of pattern recognition. With this in mind, it’s easy to understand why LLMs need constraints, context, and thoughtful integration.
If you’re aiming towards building an AI-native product, choosing the right LLM early actually matters more tahn people think. The model you pick influences cost, performance, tone, and even what kinds of features are realistic to ship.
Where LLMs show up
Before choosing a model, it helps to be clear about where they tpically add value. Most Saas use cases fall into a few buckets:
- Customer support copilots | chat bots, triage requests, summaries
- Internal tools + automations | analytics summaries, QA, ops workflows
- User facing intelligence | search, recommendations, content generation
- Team Productivity | product specs, documentation, research
So, first step? Determine where in your workflow AI could be leveraged to provide the most value. Identify any bottlenecks in your user experience and do a quick dive on where you can plug in AI tooling to fill any gaps.
Questions to ask before choosing an LLM
- Do you need domain-specific knowledge? General models are good at general language. Accuracy in niche domains often requires augmentation or fine-tuning.
- Does tone and brand voice matter? Some models are easier to steer and more consistent in style.
- How long will your text inputs and outputs typically be?
- What is your budget tolerance? API usage can scale faster than revenue if you’re not careful. If you don’t remember anything else, no matter what LLM you decide, implement some sort of rate limiter to ensure no one overloads your system (and budget!)
- Does training cutoff matter? Some products require awareness of recent events or evolving data.
- How fast can your team realistically ramp up? Open source offers control, but comes with operational overhead.
- Do your use cases require complex reasoning? Multi-step logic and decision-making vary significantly by model.
High level model options
These are the industry standard choices for most early SaaS teams:
- OpenAI GPT-5
- Google Gemini
- Anthropic Claude (Sonnet / Opus)
- xAI Grok
They offer strong general performance, fast ramp-up, and minimal setup. The trade-off here, though, is cost and vendor dependency. For many startups, this is the right place to start.
Open-source models
Open-source models give you more control and lower marginal cost at scale, but require more engineering investment.
Common options include:
- Meta LLaMA
- Mistral AI
- Alibaba Qwen
- DeepSeek
Open source makes sense when:
- You have in-house engineering strength
- You need deep customization
- You are cost-sensitive at scale
What not to do (respectfully)
Do not build your own LLM. Training a model from scratch is wildly expensive, operationally complex, and almost never the right move — especially for early-stage teams. Your leverage comes from how you use models, not from inventing one.
What matters most is that you are relying on good data, thoughtful retrieval, clear guardrails and system level prompts, and strong product decisions.
The fun part is that you’re not married to any one model. You can always change. LLMs are tools, not strategy. The differentiation comes from product thinking, user empathy, and execution. The real work happens when you start getting your hands dirty — designing prompts, deciding when RAG is necessary, and how you balance intelligence with trust. That’s where AI stops being flashy and starts being useful.
In all cases, don’t be the startup that puts a new UI on top of ChatGPT and call it a product – it’ll fall a part under pressure.
Happy creating!