🚨 Hidden LLM Issue That Can Break Your AI SaaS

While auditing a voice-driven AI eCommerce system, I discovered something alarming:

👉 A single request consumed 37K+ tokens — way beyond limits.

⚠️ The issue wasn’t the user input. It was poor architecture:

• Full MongoDB documents returned in tool responses

• Entire conversation history sent every time

• Slightly heavy system prompts

Result?Token usage exploded

→ costs increased

→ system became unstable

🛠️ What fixed it:

• Limited tool responses to essential fields (max 35 records)

• Smarter actions (combine steps into single calls)

• Context control (last 8 messages for chat, 5 for agent tasks)

• Reduced prompt size

• Proper error handling for TPM / 413 issues

📉 Outcome:

• Controlled token usage• Stable performance

• Predictable billing• Production-ready system

💡 Lesson:LLMs don’t become expensive by default —bad architecture makes them expensive.

If you're building AI systems, focus on:

→ Context

→ Data flow

→ Token control

Curious — have you faced similar scaling issues in your AI apps?

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