TCO Calculator for Evaluations
When AI evaluation relies on external LLMs, every evaluated trace generates a billable API call to your LLM provider. That's your Evaluation Trust Tax, the cost your LLM provider charges for every API call your monitoring tool sends out for evaluation. It scales with every trace, every evaluation metric, and every token you evaluate, and at scale it adds up to a meaningful portion of your evaluation TCO.
Fiddler Centor Models (formerly Fiddler Trust Models) are purpose-built AI evaluation models that run directly in your own infrastructure, evaluating every trace locally in under 100ms with no external API calls.
Use this calculator to compare both approaches and see how Fiddler Centor Models eliminate the per-call API cost, driving down your total evaluation TCO.
Provide Inputs to Calculate Your Evaluation Costs
Results
Methodology
How This Calculator Works
This calculator estimates and compares the annual cost of evaluating AI agent traces using external LLMs versus Centor Models running on GPU infrastructure. The Evaluations Trust Tax is the difference: what you pay for external LLM evaluation that Centor Models eliminate. Observability platform fees are excluded on both sides. All costs use publicly listed pricing and the inputs you configure.
External LLM Evaluation Cost
Each trace sent to an external LLM for evaluation generates a billable API call. Costs scale directly with trace volume, token count, number of evaluations per trace, and the model selected.
Formula:
- Input tokens = Tokens per trace × 0.70
- Output tokens = Tokens per trace × 0.30
- Cost per trace = (Input tokens / 1M × input price + Output tokens / 1M × output price) × Evals per trace
- Annual cost = Daily traces × (Sampling rate / 100) × Cost per trace × 365
Assumptions:
- Default token split: 70% input / 30% output (configurable)
- Sampling rate applies to external LLM evaluation only
- Centor Models evaluate 100% of traces with no per-call cost
- Batch pricing applies a 50% discount to both input and output
- Cached inputs apply an additional 25% discount to input tokens only
Sourced from each provider's public list pricing as of 05/2026.
Fiddler Centor Models Infrastructure Cost
Centor Models run on dedicated GPU infrastructure, with no external API calls per evaluation. Costs reflect the number of GPUs required to handle your full trace volume at 100% coverage. At lower volumes, idle GPU capacity means infrastructure cost dominates. As volume grows and utilization increases, Centor Models become increasingly cost-effective compared to per-call LLM pricing.
Formula:
- Chunks per eval = max(1, ceil(Tokens per trace / 1,000))
- Total chunks per day = Daily traces × Evals per trace × Chunks per eval
- GPU capacity per day = (1,000 / 100ms) × 86,400 = 864,000 chunks
- GPUs needed = max(1, ceil(Total chunks per day / GPU capacity per day))
- Annual Fiddler cost = GPUs needed × GPU $/hr × 24 × 365 × 1.2 (maintenance overhead)
Assumptions:
- Minimum 1 GPU regardless of utilization
- Default GPU: NVIDIA @ $0.8048/hr
- Max tokens per chunk: 1,000 — chunk latency: 100ms
Incident Risk Exposure
When sampling rate is below 100%, a portion of traces go unevaluated by the external LLM. Any AI incidents that occur on those unevaluated traces go undetected. This estimates the financial exposure from those missed incidents. Centor Models evaluate 100% of traces, so missed incident cost is always zero.
Formula:
- Missed fraction = 1 − (Sampling rate / 100)
- Annual risk exposure = Missed fraction × Daily traces × (Incident rate / 100) × Cost per incident × 365
Assumptions:
- Incident rate is expressed as a percentage of traces (e.g. 0.01% = 1 in 10,000 traces)
- Only applies when sampling rate is below 100%
- Default: $25,000 per incident
Evaluation Cost & TCO
The evaluation Cost is the annual cost difference between the least expensive selected LLM and Centor Models. TCO adds incident risk exposure on top of that, representing the full financial advantage of switching to Centor Models at your configured scale.
Formula:
- Evaluation Cost= max(0, Less ExpensiveLLM annual cost − Fiddler annual cost)
- TCO = max(0, Less Expensive LLM annual cost + Annual risk exposure − Fiddler annual cost)
Volume Discounts
- Batch API pricing: 50% discount on both input and output tokens
- Cached inputs: Additional 25% discount on input tokens only