Choosing the right AI model isn’t just a tech decision; it’s business-critical.
The AI model you choose will impact your app’s speed, accuracy, cost, user experience, and ultimately… whether people keep using it.
With over 78% of businesses already integrating AI into at least one process (McKinsey, 2024), the need for smarter apps is only growing. But with hundreds of models out there, from lightweight edge models to massive language models, it’s easy to feel overwhelmed.
This guide is your deep dive to pick the right AI model for your use case, data, and everything you need to know to choose, evaluate, and deploy the right AI model for your app.
Start With One Simple Question
What exactly is the task you want the model to perform?
Sounds obvious. But it’s where many founders and devs get stuck. You can’t pick the right model until you're 100% clear on the problem you're solving.
Break it down:
- Is the goal to classify, generate, predict, rank, or detect anomalies?
- Are you working with text, image, video, audio, or tabular data?
- Is real-time performance critical?
- Do users need to trust the outputs? (i.e., you might need explainability)
Here’s how tasks often align with model types:
Mapping AI Model Types to Real-World Scenarios
Let’s go deeper and break down the main families of AI models and what they’re best at:
1. Classical Machine Learning
Great when you have structured data and want speed, simplicity, and interpretability.
- Examples: Linear Regression, Decision Trees, Random Forests, SVM, KNN
- Best for: Credit scoring, churn prediction, fraud detection
- Pros: Lightweight, fast, interpretable
- Cons: Limited performance on unstructured data
2. Deep Learning
When you’re working with large, unstructured datasets (like images, videos, audio, or text).
- CNNs (Convolutional Neural Nets) → Image recognition, object detection
- RNNs & LSTMs → Time-series predictions, speech
- Autoencoders → Dimensionality reduction, anomaly detection
- GANs → Generative tasks (image/video creation)
3. Transformer Models (NLP + Beyond)
These models have redefined what’s possible with language, and now even vision.
- Examples: BERT, GPT-4, T5, LLaMA, PaLM
- Best for: Chatbots, search, summarization, code generation
- Pros: Pretrained on massive data, state-of-the-art results
- Cons: Heavy, expensive to fine-tune
Factors That Define the Right AI Model
Once you're clear on the task and data, evaluate models using these 5 factors:
1. Data Quantity & Quality
- Small datasets → Classical ML models or use transfer learning
- Large labeled data → Deep learning from scratch becomes viable
- Unlabeled data → Consider unsupervised or semi-supervised methods
Intelligence and "magic" of AI models is not in the math or the architecture but comes from the data they are trained on
— Andrew Ng, AI pioneer
Tip: If your data is limited, lean on pretrained models and fine-tune with your own data (e.g., fine-tuning GPT on customer support logs).
2. Accuracy vs Interpretability
In some industries, model transparency isn’t just preferred, it’s essential. When decisions impact health, finance, or legal matters, models that offer explanations are often favored, even if they sacrifice a bit of performance.
- Need high transparency → Linear models, decision trees
- Prioritize performance → Deep nets, transformers
Example: In healthcare or finance, interpretable models often win over black-box ones.
3. Latency & Compute Budget
Speed matters, especially on mobile or edge devices.
- Low latency needs → Smaller models like DistilBERT, MobileNet
- High compute allowed → Use larger models via cloud inference (e.g., GPT-4 via API)
DistilBERT is 60% faster than BERT with nearly identical performance on many tasks.
4. Scalability
AI solutions that work well in the prototype phase can crumble under scale if they’re not chosen wisely.
If your app is expected to scale to thousands or millions of users, the underlying model should:
- Be optimized for batch processing or asynchronous inference
- Offer efficient memory usage
- Support horizontal scaling via containers or serverless functions
Choosing a model that scales isn’t just about its architecture; it’s about how well it integrates with your infrastructure.
5. Ease of Integration
Ask yourself:
- Is the model supported by your existing stack (Python, JS, Swift)?
- Is there API access or open-source implementation?
- Does the model require a GPU to run?
Tools like Create make this part easier by abstracting away infrastructure and letting you prototype with multiple models side-by-side.
How to Evaluate AI Models
This is where most people cut corners, but it’s arguably the most important part.
Once you shortlist a few models, test them against your actual use case. Not just with benchmark datasets, but with real data and constraints from your app.
Here are the key evaluation metrics to track:
This is also where the tooling you use makes a difference. At Create, for example, teams use a smart evaluation framework called Evals to benchmark models in real-world conditions across accuracy, latency, and cost before shipping them into production.
Real-World Example → Choosing the Right AI Model for a Mental Health App
Use Case: The app wants to detect user sentiment from text inputs and suggest resources.
Step-by-step:
- Task: Sentiment analysis (NLP)
- Data: Moderate-sized dataset of anonymized chat logs
- Constraints: Should run on mobile; privacy is key
Model decision:
- Fine-tune a DistilBERT model (compact and accurate)
- Use on-device inference with quantization for privacy
- Evaluate with real user inputs across 3 languages
This approach delivers fast, private, explainable results without depending on a massive LLM API.
So, What’s the Right AI Model for Your App?
To recap, the right AI model:
- Solves your specific task using your real data
- Matches your performance, speed, and cost requirements
- Can be deployed, maintained, and trusted at scale
- It doesn’t have to be the biggest or trendiest, just effective
In the end, you don’t need to chase the best model in the world, just the right one for your app right now.
Start small. Think long-term. And test relentlessly.
Conclusion
The AI model landscape is evolving rapidly. New open-source releases, hybrid edge-cloud deployment options, and tools like Create are lowering the barrier to getting started.
But your app can be unique, and if your AI model is, too.
So, take the time to match the model to your use case and data.
Because choosing the right AI model isn’t just a tech decision, it’s product strategy in disguise.
