Choosing the Right AI Model

AI models generally fall into two broad categories: open-source and proprietary (closed-source). Understanding their differences helps you choose the right approach for your project, whether that's automating data workflows, classifying records, or powering intelligent search tools.

Open models

Open-source models can be downloaded, customized, and hosted wherever you choose, on-premises or in the cloud.

They offer greater control over data, which can be important in scientific or research settings where datasets include sensitive information (for example, ecological or location-based data).

You can find a growing ecosystem of open models on platforms such as Hugging Face or the Open LLM Leaderboard.

Tip: Open models are ideal when data governance, reproducibility, or integration flexibility are priorities.

Closed models

Proprietary models, provided via APIs by companies like OpenAI, Anthropic, or others, are easier to adopt and often deliver strong results immediately.

They remove the burden of infrastructure management but require sending data to an external service. For many organizations, this trade-off is acceptable for non-sensitive tasks like report generation or content summarization.

Tip: Closed models are a good starting point for experimentation or where rapid development matters more than full control.

Cost considerations

How you access or host a model directly affects cost.

API-based (closed models)

You typically pay per token, which roughly corresponds to chunks of text. Costs scale with usage, for example, large reports or batch processing can add up quickly.

Self-hosted (open models)

Running your own model shifts costs to infrastructure, compute, GPUs, and storage but gives you predictable expenses and full control over data.

Pragmatic approach: Start with API-based models while prototyping. Move to open-source or self-hosted deployments once you understand your workloads and data sensitivity.

Evaluating model performance

Accuracy and domain understanding matter, particularly when models process structured or technical data.

While general benchmarks (like MMLU or HELM) help compare models, your own evaluation framework is the most valuable indicator.

Ask questions such as:

• Does the model interpret domain-specific terminology correctly?
• Can it extract structured data from unstructured text?
• How consistent are its outputs across similar inputs?

Building lightweight internal evals lets you track progress as models or datasets evolve.

Context windows and data volume

A model’s context window determines how much information it can consider at once, both your input and the model’s response.

Larger windows enable tasks such as summarizing long documents, comparing multiple records, or reasoning over datasets.

If your data exceeds this limit, chunking or retrieval-augmented generation (RAG) techniques can break it into manageable pieces and feed only the most relevant parts to the model.

Choosing what fits

Final Thoughts

AI models are tools, not solutions in themselves. The key is matching model capabilities with your real-world needs: automation, data interpretation, or intelligent interfaces.

Whether you’re working with biodiversity data, research metadata, or any other structured information, the same principle applies: start small, test often, and evolve your approach as the technology changes.