How to Avoid Surprise API Bills in Your AI Application

AI Observability Tips to Monitor and Control Token Usage in LLM Applications

As more companies integrate powerful LLMs into their products, they often rely on external providers like OpenAI, Anthropic, or Microsoft Azure for model access. These models are typically accessed through paid APIs, and pricing is usually based on usage, either per request or per token.

While this approach speeds up deployment and eliminates the need for self-hosting, it comes with one major caveat: costs can escalate fast if not properly monitored.

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The Hidden Costs of Using LLM APIs

Most companies don’t have the infrastructure or in-house expertise to host models like GPT-4 or Claude. So they turn to APIs, which is a great solution in theory. But without proper AI observability in place, many teams end up with surprise five-figure bills they didn’t budget for.

Here are two common and sneaky causes of high usage:

1. Complex AI Workflows

Your application might be triggering multiple LLM API calls per user interaction, for example, chaining summarization, classification, and generation steps together. This can quickly multiply your token usage without visibility.

2. Error Retries

If an error occurs during an API call, many systems are set to retry automatically. These silent retries can generate costly duplicate requests, especially with longer prompts or large context windows.

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Why Budget Caps Aren’t Enough

Most LLM API providers offer basic cost control features like dashboards, usage alerts, and budget caps. While helpful, these tools are not sufficient for production-grade reliability:

• 💥 Hitting a cap = unexpected service outage
• ⏱ Alerts often come too late during traffic spikes
• ❓ You don’t know which user, feature, or request is driving usage
• 📉 No visibility into optimization opportunities

You’re flying blind without proper LLM monitoring infrastructure in place.

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What You Should Do Instead

To keep your LLM costs under control without compromising user experience, we recommend setting up a more granular and proactive AI observability layer.

🔧 Key Practices:

• Track token usage per user, internal request, or feature flag
• Set soft thresholds internally and visualize them via dashboards
• Cache common prompts and responses where appropriate
• Rate-limit expensive operations to prevent abuse or runaway usage
• Monitor retries and ensure you don’t retry unnecessarily
• Analyze trends over time to improve prompt engineering and workflow design

These practices not only help you stay within budget but also improve reliability and user trust.

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Tools for Monitoring Token Usage

Modern observability platforms are beginning to offer LLM-specific monitoring features. Here are a few we recommend looking into:

• Langfuse
• Arize
• Datadog (with custom integrations)

These tools allow you to track token usage over time, break it down by source, and create alerts when things go off track.

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Final Thoughts: Treat Token Costs Like a Production Metric

Just like you monitor latency or server uptime, token usage should be a core production metric for any team building with LLMs. With the right monitoring in place, you can avoid outages, optimize performance, and save significantly on cost.

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Let’s Talk AI Monitoring

We at Kedmya help companies implement remote monitoring services for LLMs, including:

• Token usage tracking
• Custom dashboards and alerting
• Cost optimization strategies
• API observability and incident response

If you're scaling LLM-based features or offering AI solutions to clients, we’d love to help you build the right observability layer.

💬 Let’s talk about AI monitoring in production. Reach out to start the conversation.

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