Why You Should Build AI Agents with Ollama First

The AI PoC Paradox: High Effort, Low ROI

Building AI Proofs of Concept (PoCs) has become a daily ritual in many company DX departments. With the rapid evolution of LLM models, creating PoCs is easier than ever, often yielding better results with less effort. Yet, we’re not seeing a commensurate rise in adoption rates and Return on Investment (ROI). Why is that?

A significant reason might be that while LLM capabilities are advancing at breakneck speed, our AI engineering techniques for bridging these powerful models with real-world problems are lagging. We often get excited about new features and use cases enabled by the latest models, but translating these into tangible, organizational-level applications remains unimproved due to a lack of robust engineering practices.

Embracing Real-World Constraints with Ollama

So, how can we truly evaluate the real-world impact of our AI PoCs? One easy approach is to start building your AI agents with Ollama. Ollama allows you to run a curated selection of LLM models locally, on your own machine, with reasonable resource requirements. By beginning with Ollama, you’re essentially striving to deliver solutions that an average employee, with an average salary, can realistically utilize, rather than relying on a “superman” solution that comes with an astronomical price tag.

Ollama’s Core Disciplines for AI Engineers

Ollama specifically forces AI engineers to confront and refine crucial aspects of their work:

Realistic Context Handling

• Realistic Context Handling: Ollama’s local execution, often on more constrained hardware, naturally highlights the practical limits of LLM context windows. Unlike cloud-based models with seemingly infinite contexts that can obscure inefficient prompt engineering in a PoC, Ollama forces engineers to meticulously find and structure information sources for brevity and relevance. This disciplined approach to context management, including techniques like effective Retrieval Augmented Generation (RAG) and precise prompt design, ensures that an AI agent delivers accurate and relevant output. By designing for these constraints from the outset, your solutions become inherently more robust and efficient, capable of handling the unpredictable, often lengthy, information demands of real-world scenarios, regardless of the eventual deployment platform.

Confronting Latency and Cost Efficiency

• Confronting Latency and Cost Efficiency: The inference speeds on Ollama, typically around 20 tokens/second for a 4B model on a powerful CPU-only PC, make the cost of generating tokens palpable. Tasks like generating a summary might take tens of seconds, which immediately draws attention to the efficiency of your agent’s prompts and output. In contrast, cloud services like ChatGPT and Claude infer so rapidly that developers might overlook inefficiencies during PoC development. However, these seemingly minor inefficiencies, such as verbose prompts or redundant token generation, can quickly escalate into significant operational costs and poor user experiences when deployed at scale. Ollama provides immediate feedback on the impact of design choices on speed and resource usage, compelling engineers to optimize for lean, efficient interactions that translate directly into lower API costs and better performance in production.

Navigating Production Transition and Migration

Even if you’re persuaded by the benefits, you might still worry about the effort and cost of transitioning an Ollama-developed AI service to a real-world production environment, typically hosted on cloud platforms like AWS. If your final service is largely cloud-native, the transition is often seamless. Standard cloud components like S3 and Lambda often have readily available local alternatives, such as those provided by LocalStack. However, if your architecture heavily relies on specific cloud provider tweaks or runs on platforms like Azure, the migration might require more effort. Nevertheless, even without using Ollama, limiting your model choice to under 14B parameters can be beneficial for accurately assessing PoC efficacy early on.

Have fun experimenting with your AI PoCs!