Which AI Model Should You Put in Your SaaS? The Solo Builder's Decision Tree

Not which model is smartest. Which model fits your feature, your budget, and your user count. Real cost math for 1,000 users included.

You shipped a SaaS with Cursor. Now you want to add AI features. You open the OpenAI docs, the Anthropic docs, the Google docs. Three pricing pages. Twelve model names. No clear answer. This post is the clear answer.

The decision tree by use case

Most comparison posts rank models by benchmarks. Benchmarks do not pay your bills. What matters is: what does your feature need, and how much will it cost at your user count?

Use Case 1

Text generation: chatbot, summarizer, writer

Best balance: Claude Sonnet 4. Strongest at following complex instructions and producing consistent output. If your feature needs the AI to follow a specific format, tone, or multi-step process, Claude Sonnet is the default.

Budget option: GPT-4o-mini. About 80% of the quality at 20% of the cost. Good enough for simple summarization, FAQ answers, and short-form generation.

When Gemini wins: Very long context. Gemini supports up to 1M tokens of input, which matters if your feature processes entire documents or very long conversations.

Use Case 2

Code generation: in-app code features

Best: Claude Sonnet 4. Strongest at code editing, multi-file context, and understanding existing codebases. If your SaaS has a code-generation feature (template builder, automation scripting, no-code backend), this is the model.

For simple completions: GPT-4o-mini or Claude Haiku handle autocomplete-style features at a fraction of the cost.

Use Case 3

Vision and multimodal: image analysis, OCR, document parsing

Best: Claude Sonnet 4. Handles charts, screenshots, handwritten notes, and complex visual layouts well. GPT-4o is a close second with slightly wider image format support.

Budget: Gemini Flash. The cheapest multimodal option by far. Good enough for simple image classification and basic OCR.

Use Case 4

Embeddings and search: RAG, semantic search

Standard: OpenAI text-embedding-3-small at $0.02 per million tokens. Good enough for most retrieval tasks and the cheapest option from a major provider.

Better quality: Voyage AI. Measurably better for retrieval benchmarks, but the difference only matters at scale or for precision-critical applications.

Free and self-hosted: Sentence Transformers. Open source, no API cost, runs on your own server. Good for privacy-sensitive applications.

Use Case 5

Fast classification and routing: spam filter, intent detection, tagging

Best value: Claude Haiku 4.5. Fast, cheap, surprisingly capable for its size. Handles classification, entity extraction, and routing decisions well.

Fastest: Gemini Flash. Lowest latency of the major models. Good for real-time applications where response time matters more than reasoning depth.

Use Case 6

Local and privacy-sensitive: on-device, no data leaves

Best: Llama 3.1 (8B or 70B parameters). Meta's open-weight model. Strong general performance, runs on your own hardware.

Smaller: Mistral 7B. Lighter, faster, good for constrained environments.

Smallest: Phi-3 mini (Microsoft). Runs on a laptop. Good for edge deployment where every megabyte counts.

Real cost math: 1,000 users, 10 requests per day

Everyone talks about model quality. Nobody talks about what it actually costs at your user count. Here is the math for a typical SaaS with 1,000 active users making 10 AI requests per day, averaging 1,000 input tokens and 500 output tokens per request.

Prices as of mid-2026. Always check current pricing. These change fast.

The model stack pattern: use multiple models

Most production SaaS apps do not use one model. They use a stack:

• Heavy lift (complex reasoning, user-facing): Claude Sonnet or GPT-4o
• Fast tasks (classification, routing, extraction): Haiku or GPT-4o-mini
• Embeddings (search, RAG): OpenAI embedding model
• Fallback (if primary is down): different provider entirely

This is why AI ops tools matter. When you run multiple models, you need cost tracking, fallback routing, and logging. That is a separate post.

How to actually integrate: three patterns

1. Direct API (simplest start)

Use the Anthropic SDK or OpenAI SDK directly. Good for MVP, single model, low volume. The con: vendor lock-in, no fallback if the API goes down.

2. OpenRouter (multi-model, one API key)

A single API that routes to 100+ models. Good for experimenting and comparing models in production. You switch models by changing one parameter, not rewriting your integration.

3. AI gateway / proxy (production-grade)

Tools like Helicone (observability), Portkey (gateway + routing), or LiteLLM (self-hosted proxy). Good for cost tracking, caching, fallback routing, and rate limiting. This is where you go when your AI bill starts mattering.

Mistakes I see vibe coders make

1. Using GPT-4 for everything. Including tasks that Haiku handles fine at 1/40th the cost.
2. No cost tracking until the bill arrives. Add Helicone (free, one line of code) from day one.
3. No fallback when OpenAI goes down. It does go down. Your users see errors. Route to a backup provider.
4. Hardcoding model names. Use a config or proxy so you can switch models without redeploying.
5. Not caching identical requests. Same prompt equals same answer equals wasted money.

FAQ