How should teams estimate LLM costs before choosing a model for production?

Start with real usage, not sticker price. Measure average input and output tokens per request, then calculate total cost as input tokens multiplied by input price plus output tokens multiplied by output price. Because output is usually several times more expensive and workload shape changes the cheapest model, the right choice depends on your specific token ratio and traffic pattern.

Why This Matters for Security Teams

LLM cost estimation is not just a procurement exercise. It affects whether a model can be safely used in production, whether guardrails can be sustained, and whether a proof of concept turns into an unplanned operating expense. Security and platform teams often focus on per-token pricing, but the real risk comes from uncontrolled usage patterns, large outputs, retries, and tool-driven workflows that expand token consumption over time. That is why cost modelling belongs alongside governance and capacity planning, not after deployment. Guidance from the NIST AI Risk Management Framework is useful here because it treats operational impact, measurement, and monitoring as part of responsible AI management rather than a separate finance concern.

For agentic or workflow-based systems, cost can also become a security signal. Unusual spend may indicate prompt loops, abuse, misrouted traffic, or runaway automation. In practice, many teams discover the true cost of a model only after production traffic, retries, and long responses have already inflated the bill.

How It Works in Practice

Teams should estimate LLM cost from actual workload shape, not vendor marketing examples. The basic formula is straightforward: expected monthly cost equals total input tokens multiplied by input token price, plus total output tokens multiplied by output token price. The harder part is measuring realistic inputs. That means estimating the average prompt size, system prompt overhead, retrieval context, tool results, and the output length required for the task. For many applications, output tokens cost more than input tokens, so a model that looks cheap on paper can become expensive when it produces verbose responses or retries.

Useful estimation steps include:

• Sample real requests from target users or systems, not synthetic prompts.
• Measure token counts for the full exchange, including system instructions and retrieved context.
• Separate steady-state traffic from burst traffic, because spikes can change model choice.
• Account for retries, validation passes, and multi-step agent workflows.
• Test at least two candidate models against the same workload before comparing cost.

For agentic systems, the OWASP Agentic AI Top 10 and CSA MAESTRO agentic AI threat modeling framework both reinforce the need to model tool use, escalation paths, and failure loops, because each can increase token burn. The right operational question is not only “which model is cheapest,” but “which model stays predictable under realistic load and control requirements.” These controls tend to break down when prompts are highly variable, outputs are long-form, or agents can chain multiple model calls before a human sees the result.

Common Variations and Edge Cases

Tighter cost control often increases workflow overhead, requiring organisations to balance lower spend against more complex routing, caching, and review logic. That tradeoff matters because the cheapest model is not always the safest or most reliable choice for production. Current guidance suggests using smaller models for routine classification, summarisation, or triage, while reserving larger models for high-stakes reasoning or customer-facing interactions where quality risk is higher. There is no universal standard for this yet, so the selection criteria should be explicit and measurable.

Edge cases appear quickly in real systems. Retrieval-augmented generation can make prompts much larger than expected. Agentic workflows can multiply calls through planning, reflection, and tool execution. Long-context models may reduce the number of calls, but raise per-request cost. If the use case involves regulated decisions, external reporting, or sensitive data, teams should also align cost planning with the NIST AI 600-1 Generative AI Profile, because operational cost decisions affect logging, oversight, and validation choices. The main failure mode is assuming average token usage will stay stable after launch, when prompt growth, user behaviour, and system prompts usually drift.

Related resources from NHI Mgmt Group

• How should teams evaluate LLM robustness before putting a model into production?
• What should security and governance teams do before allowing a model into production?
• How should teams govern LLM changes before they reach production?
• How should teams reduce LLM costs without breaking production quality?