Balancing quality, cost, and speed in AI development

Many organizations develop AI applications without a clear plan. They experiment with different models and implementations but lack a systematic approach. This often results in suboptimal solutions that are too expensive, too slow, or fall short on quality.

In this article, I'll show how to systematically approach AI development by balancing three crucial factors: quality, cost, and speed. By considering these factors from the start, you avoid expensive detours and build something that works faster.

Quality vs Cost vs Speed

For effective implementation, it's essential to understand how these three factors influence each other. Higher quality usually means higher costs or slower responses. Faster responses often require more expensive infrastructure. Finding the right balance within your context is crucial.

An important initial choice is between self-hosting or cloud - this impacts all three factors. While privacy considerations often lead to self-hosting, modern cloud providers offer sufficient security and compliance that government and healthcare organizations use them. In my view, self-hosting is not the right choice for most organizations, so I won't discuss it further in this post.

Setting Concrete Parameters

You want an AI application that's as good as possible, fast, and affordable. Without clarity on what these terms mean for your application, you'll chase three moving targets, leading to inefficiency and wasted time.

Start with the question: what kind of AI experience do you want to offer your users? The answer determines your quality trade-off space.

Developers alone can't answer this question. If you're offering a premium experience where users pay extra, you can focus on quality with costs being secondary. For a free feature, cost management becomes the priority.

Translate this into concrete, measurable goals for the team:

Quality: 83% of AI suggestions are adopted without modifications
Cost: Maximum $0.05 per processed query
Speed: 95% of responses within 2 seconds

Setting a quality target is often challenging, causing many teams to hesitate launching to production. It helps to attach concrete values to correct and incorrect AI responses. This way, you can calculate when your application performs well enough to break even.

You'll likely adjust these numbers once you go live, but they ensure clear communication and shared expectations among all stakeholders.

Optimizing Each Factor

With your goals clear, start with one factor: quality. Only once your output is consistently good enough should you optimize for cost and speed. This order is crucial - a cheap or fast solution that doesn't meet your quality requirements is unusable.

Quality

Start by developing a solution that shows your accuracy target is reachable. Begin with premium models like GPT-4, Claude 3.5 Sonnet, or Gemini. If these models aren't good enough, experimenting with smaller models won't help.

Test your solution with a small pilot group before optimizing. Users often have different expectations, and an early pilot prevents wasting time on unnecessary improvements.

Key tips for quality optimization:

1. Implement Systematic Monitoring

• Create synthetic test questions for baseline performance

• Build clear user feedback mechanisms

• Cluster and analyze user questions to find patterns

2. Improve Model Output

• Implement effective prompt engineering

• Apply Chain-of-Thought for better reasoning

• Use RAG for relevant context

Cost

Once you've validated quality goals with a pilot group, optimize for costs. Model costs per token have dropped dramatically and continue to fall. Waiting for costs to decrease is often a good strategy. Focus on desired quality first - premature cost optimization can lead to unnecessary work.

Key tips for cost optimization:

1. Avoid Unnecessary AI Use

• Identify where traditional solutions suffice

• Route simple queries to rule-based systems

2. Optimize Token Usage

• Use logit_bias for classification tasks

• Limit context to what's relevant

• Cache common responses

Speed

Application speed affects user experience. While some use cases can wait for responses, others require real-time interaction.

Key tips for speed optimization:

1. Choose the Right Infrastructure

• Use provisioned resources for stable performance

• Select broader data zones for minimal latency

• Consider smaller, faster models for time-sensitive tasks

2. Implement Robust Error Handling

• Build retry mechanisms for responses exceeding limits

• Monitor both p0 and p99.9 latency

Conclusion

By balancing these three technical pillars and monitoring them systematically, you create a solid foundation for your AI application. Remember: start with quality, then optimize for cost and speed - in that order.