Total Cost of Ownership for Operationalizing Agents
Every observability platform promises visibility into your agents. But most don't tell you the full cost of that visibility, including a hidden cost called the Trust Tax.
How Most Platforms Evaluate Agents
When an agent generates a trace, it needs to be scored for quality, safety, and performance. The most common approach is to send each trace to an external LLM (OpenAI, Anthropic, etc.) to act as a judge. This means every trace scored is an API call to a third-party model provider, and that cost shows up on your bill, not your tooling vendor's.
This approach is called LLM-as-a-Judge. When the judge is an external LLM, the dependency on third-party API calls is what drives up your total cost of ownership (TCO).
The Cost of Evaluating with External LLMs
- Your trace is generated by your agent
- API call to external LLM provider for scoring (OpenAI, Anthropic, etc.)
- You pay the Trust Tax
The Hidden Costs of Calling External LLMs
- Risk Gaps: To control costs, some teams may consider sampling, but the traces you skip are often the ones that matter most: jailbreak attempts, policy violations, hallucinations, edge-case failures. These are low-frequency, high-impact events that sampling could potentially miss.
- Operational Overhead: The engineering effort to set up, manage, and maintain your evaluation infrastructure, whether that's orchestrating external API calls or standing up your own models. Your team carries the burden of prompt versioning, scoring calibration, model hosting, and ongoing maintenance.
- The Trust Tax: You are charged every time a trace is scored via an external LLM API call. This shows up on your invoice. At enterprise scale, it compounds fast.
What the Trust Tax Looks Like at Scale
These are potential external LLM API costs* you pay annually, in addition to your tooling fees.
Fiddler Trust Models: Evaluate and Observe Agents Without the Trust Tax
Fiddler Trust Models are specialized, task-specific models built in-house and deployed in your environment. They score agent and LLM prompts and responses at runtime for hallucination, toxicity, jailbreaks, PII/PHI exposure, and other critical risks.
Trust Models are built to cover a range of use cases:
- Out-of-the-Box Models
- Hallucination detection, safety scoring, toxicity, jailbreak detection, and PII/PHI identification.
- Ultra low-latency and task-specific.
- Customizable Models
- Enterprises submit prompts to create domain-specific evaluators.
- Fully managed, handles 300K+ daily events without the burden of infrastructure management.
Why Sampling Doesn't Solve the Trust Tax
Sampling reduces your bill but introduces risk gaps. Fiddler Trust Models cover 100% of traces by removing the cost barrier that draws teams to sample in the first place.
Fiddler Trust Models Power AI Observability and Security
- Evaluation: Test and benchmark agents before they go live. Run evals against test sets, compare model versions, and validate guardrail thresholds before launch.
- Observability: Continuously score and monitor every trace in production. Surface issues in real time, diagnose root causes, and trigger alerts.
- Guardrails: Enforce safety policies in real time across input, execution, and output, preventing violations before they occur.
- Analytics: Roll up aggregate reports down to granular insights across agents for a single-pane-of-glass view of behavior, risk, and performance.
Trusted by Industry Leaders and Developers
* Calculations based on Open AI GPT 5 mini. 1 trace = 1 API call to GPT 5 mini. Contact us to receive a custom calculation.
Frequently Asked Questions
What is the AI Trust Tax?
LLM-as-a-Judge is a common approach to evaluating AI outputs: you use a large language model like GPT-4 to score the quality, safety, or accuracy of another model's responses. It's flexible and easy to set up, but it breaks down at scale. Every evaluation is an external API call, which means unpredictable costs, added latency, and data leaving your environment. For enterprises running millions of traces per day, those API calls become a material line item. That's the Trust Tax: the hidden LLM costs that grow linearly with your evaluation volume, show up on your OpenAI or Anthropic invoice (not your observability vendor's), and punish you for wanting full coverage.
What is LLM-as-a-judge and why does it create a Trust Tax?
LLM-as-a-Judge is a common approach to evaluating AI outputs: you use a large language model like GPT-4 to score the quality, safety, or accuracy of another model's responses. It's flexible and easy to set up, but it breaks down at scale. Every evaluation is an external API call, which means unpredictable costs, added latency, and data leaving your environment. For enterprises running millions of traces per day, those API calls become a material line item. That's the Trust Tax: the hidden LLM costs that grow linearly with your evaluation volume, show up on your OpenAI or Anthropic invoice (not your observability vendor's), and punish you for wanting full coverage.
Does the Trust Tax apply to agentic workflows?
Yes, and the impact compounds. Agentic workflows generate multiple traces per interaction as agents plan, reason, and take actions across steps. Each trace scored via an external LLM is another API call. The more complex your agent workflows, the faster the Trust Tax grows. Fiddler Trust Models evaluate every trace in your environment without hidden costs, regardless of how many steps your agents take.
What questions should I ask an AI observability vendor about the Trust Tax?
- Where do your evaluation models run: in my environment, or through an external API?
- What does each evaluation cost at my expected trace volume, and how does that scale?
- What percentage of traces do you evaluate by default? What happens to the ones you skip?
- Does your platform send any of my AI output data to a third-party provider for scoring?
- Can you provide a total cost of ownership estimate that includes evaluation costs, not just platform fees?
What are the risks beyond the AI Trust Tax?
External evaluation calls create three problems beyond the bill:
- Risk gaps: Aggressive sampling to control costs means you're not evaluating every trace, and the ones you skip may be the ones that matter most: jailbreaks, policy violations, rare hallucinations.
- Operational overhead: Without built-in evaluation models, your team owns model selection, prompt versioning, and calibration. That's engineering time that doesn't ship products.
- Data exposure: Sending AI outputs to a third-party API means your data, potentially including sensitive customer information, leaves your environment on every evaluation call.