Selecting The Guardrails Your Enterprise AI Needs
Selecting a guardrails solution for enterprise AI applications is not always clear cut. It involves evaluating multiple dimensions and understanding which factors matter most for your specific use case.
Different use cases prioritize different requirements. A customer service chatbot handling thousands of simultaneous interactions requires exceptional response times, while a financial application may place higher value on reasoning depth, and an employee Q&A system might benefit from a basic guardrails solution for protection.
Rather than prescribing a universal "best" option, we offer data-driven comparisons that empower you to align guardrails selection with your specific technical requirements.
This benchmark report provides a comprehensive technical assessment of models used for guardrails solutions across three critical dimensions — speed, cost, and accuracy — and three metrics — jailbreak, toxicity, and faithfulness. Our data shows clear performance patterns that will help you identify whether your priority is:
Optimizing for speed, security, and accuracy for task-specific, contextual applications
Implementing basic guardrails to protect your applications
Leveraging reasoning capabilities for complex applications
Guardrails Benchmarks Methodology
Our benchmarking methodology evaluated three critical value dimensions for enterprise LLM guardrails: latency, cost, and accuracy.
We ran identical tests on publicly available jailbreak, toxicity, and Q&A datasets, with all benchmarks executed in parallel across Fiddler Guardrails, Amazon Bedrock, Azure AI Content Safety, and LLM-as-a-Judge solutions (e.g., OpenAI and Ragas). All datasets are publicly available and none of the examples appear in Fiddler’s training data. Benchmarks were measured on a class-balanced, randomized subset of each dataset.
All API calls were made close to the target evaluator to give “best case” performance for each; e.g., calls to Azure content safety were made from the same Azure availability zone. Response time measurements exclude any data manipulation.
We used text units for cross-platform cost comparisons because they align with industry standards set by major providers like AWS and Azure, and provide stable character-based measurements that don't vary by tokenization method. Text units are standardized measurements representing approximately 1,000 characters of content to be evaluated. 1 text unit = 1000 characters = ~250 tokens.
This standardized approach with controlled testing environments ensures true apples-to-apples comparisons, providing you with reliable data to help you choose the best model to implement the guardrails for your use case.
The benchmark analysis was performed in April 2025.
Align The Guardrails Solution With Your Use Case Requirement
The benchmark data shows that different guardrails solutions excel in specific scenarios. Use this framework to identify which model best aligns with your guardrail requirements.
- Fastest response times, and highest accuracy
- For task-specific, contextual use cases
- Purpose-built models at runtime, optimized for guardrails
- Simple threshold-based implementation over continuous scores
- Sufficient for basic safety needs
- Use if you are tied to the AWS or Azure ecosystem, and cannot use best-of-breed tools
- For broader-task use cases
- Best chain-of-thought, world knowledge related reasoning and analysis
- When speed and cost are not the primary concern
Fiddler Trust Models
Fiddler Guardrails leverages purpose-built, fine-tuned Trust Models designed for specific tasks. Trust Models deliver high-accuracy scoring of LLM inputs and outputs while maintaining low latency at runtime. Built to handle increased traffic as LLM deployments scale, they ensure data protection in both SaaS and VPC environments.
Safety Model
Faithfulness Model
Fiddler Trust Models are:
Benchmark Findings
Below are the benchmark findings from our analysis of how each model stacks up in latency, cost, and accuracy for each metric.
Metric: Jailbreak
Value Dimension | Fiddler Trust Model (Safety) | Amazon Bedrock | Azure AI Content Safety |
|---|---|---|---|
| Performance (Best F1 / AUC) | 0.94 / 0.98 | 0.92 / Provides low, medium, high thresholds, not continuous scores | 0.87 / Provides low, medium, high thresholds, not continuous scores |
| Latency | 105ms | 260ms | 165ms |
| Cost per 1,000 text unit | $0.021 | $0.15 | $0.38 |
Metric: Toxicity
Value Dimension | Fiddler Trust Model (Safety) | Amazon Bedrock | Azure AI Content Safety | OpenAI Moderation (LLM-as-a-Judge) |
|---|---|---|---|---|
| Performance (Best F1 / AUC) | 0.91 / 0.96 | 0.88 / Provides low, medium, high thresholds, not continuous scores | 0.77 / Provides low, medium, high thresholds, not continuous scores | 0.88 / 0.94 |
| Latency | 120ms | 260ms | 165ms | 200ms |
| Cost per 1,000 text unit | $0.014 | $0.15 | $0.38 | $0.042 |
Metric: Faithfulness
Value Dimension | Fiddler Trust Model (Faithfulness) | Amazon Bedrock | Azure AI Content Safety | Ragas GPT-4o (LLM-as-a-Judge) |
|---|---|---|---|---|
| Performance (Best F1 / AUC) | 0.84 / 0.89 | 0.80 / Provides low, medium, high thresholds, not continuous scores | 0.74 / Provides low, medium, high thresholds, not continuous scores | 0.74 / 0.82 |
| Latency | 120ms | 260ms | 165ms | 8,200ms |
| Cost per 1,000 text unit | $0.014 | $0.10 | $0.38 | $0.04 |
Amazon Bedrock and Azure AI Content Safety only provide categorical thresholds (low, medium, high) for evaluation results. This difference in scoring granularity impacts the precision with which safety policies can be tuned and may affect implementation flexibility for nuanced use cases.
Benchmark Analysis Takeaways
Our analysis reveals distinct performance profiles across the evaluated guardrails solutions:
Comparative Assessment | Fiddler Trust Model (Safety) | Amazon Bedrock | Azure AI Content Safety | OpenAI Moderation (LLM-as-a-Judge) | Ragas GPT-4o (LLM-as-a-Judge) |
|---|---|---|---|---|---|
| Overall | Fastest, most cost-effective, and secure guardrails | Guardrails with middle of the pack capabilities | Guardrails with middle of the pack capabilities | Guardrails for reasoning use cases with latency and cost limitations | Guardrails for reasoning use cases with latency limitations |
| Scores |
|
|
|
|
|
| Best for |
|
|
|
|
|
| Less suitable for |
|
|
|
|
|
Same Model, Different Results: The LLM-as-Judge Reality
AI teams often assume that identical foundation models will produce consistent results across providers. However, two providers using say “GPT-4 as a judge" could produce dramatically different safety scores for identical content. Actual results can vary significantly due to the following reasons:
Guardrails Metrics Cost Comparison by Application Size and Models
Explore the interactive tool below to visualize guardrail implementation costs for benchmarked models. We calculated costs for four application sizes across three metrics using our per-unit pricing.
Select your metric type (jailbreak, toxicity, or faithfulness), application size, and provider to see detailed cost projections for each scenario. Hover over the bars for detailed cost information for each model.
Jailbreak Metric Cost Comparison
Toxicity Metric Cost Comparison
Faithfulness Metric Cost Comparison
The cost comparison reflects direct guardrail resource expenses only and does not include additional platform fees, networking costs, integration expenses, or other infrastructure requirements that may be needed for a complete deployment.
Highlights: Fiddler Guardrails Comparison on Jailbreak Attempts and Toxicity
Guardrails Metrics Comparison by Value Dimension and Models
The interactive visualizations below allow you to compare how each model stacks up in latency, cost, and accuracy for each metric. We used benchmark data to create visualizations comparing jailbreak, toxicity, and faithfulness metrics across models, highlighting key technical trade-offs.
To customize your analysis, select different value dimensions and metrics in each visualization. These side-by-side comparisons provide objective, data-driven insights to help you select the optimal model to meet your guardrails requirements.
Hover over data points for detailed model information that can inform your implementation decisions.
Jailbreak Metric Comparison
Toxicity Metric Comparison
Faithfulness Metric Comparison
Highlights: Fiddler Guardrails Comparisons on Faithfulness and Toxicity
Real-World Impact: Speed and Cost Effectiveness
See how benchmark differences impact real deployments. A financial services company needs guardrails to moderate jailbreak, toxicity, and faithfulness for their customer service chatbot.
Let’s analyze the costs to operate guardrails and the latency impact for the customer service chatbot below.
Cost Analysis
Metrics | Volume (M) | Cost per 1,000 text units | Annual Cost |
|---|---|---|---|
| Jailbreak | 300 | $0.021 | $6,300 |
| Toxicity | 300 | $0.021 | $6,300 |
| Faithfulness | 400 | $0.014 | $5,600 |
| Total | 1,000 | $18,200 |
Metrics | Volume (M) | Cost per 1,000 text units | Annual Cost |
|---|---|---|---|
| Jailbreak | 300 | $0.15 | $45,000 |
| Toxicity | 300 | $0.15 | $45,000 |
| Faithfulness | 400 | $0.10 | $40,000 |
| Total | 1,000 | $130,000 |
Metrics | Volume (M) | Cost per 1,000 text units | Annual Cost |
|---|---|---|---|
| Jailbreak | 300 | $0.38 | $114,000 |
| Toxicity | 300 | $0.38 | $114,000 |
| Faithfulness | 400 | $0.38 | $152,000 |
| Total | 1,000 | $380,000 |
The cost comparison reflects direct guardrail resource expenses only and does not include additional platform fees, networking costs, integration expenses, or other infrastructure requirements that may be needed for a complete deployment.
Business Impact
(86% cheaper)
(95% cheaper)
Latency Analysis
The table below shows how different models impact user experience and security.
Metrics and Characteristics | Fiddler Trust Model (Safety) | Amazon Bedrock | Azure AI Content Safety | LLM-as-a-Judge OpenAI Moderation Ragas GPT-4o |
|---|---|---|---|---|
| Jailbreak | 105ms | 250ms | 165ms | — |
| Toxicity | Jailbreak and toxicity invoked in parallel | Jailbreak and toxicity invoked in parallel | Jailbreak and toxicity invoked in parallel | 250ms (via OpenAI) |
| Faithfulness | 120ms | 273ms | 165ms | 8,225ms (via Ragas) |
| Total Response Time | 0.225s | 0.523s | 0.330s | 165ms |
| Response TIme Difference | Fastest | 132% slower | 47% slower | 3,667% slower |
| User Experience | Immediate, natural and safe conversation flow | Slight but noticeable pause in conversation | Generally smooth conversation with minimal delay | Significant delay, disrupting conversation flow |
- Fiddler Guardrails, Amazon Bedrock, and Azure AI Content Safety invoke jailbreak and toxicity evaluations in parallel, resulting in the same latency for both safety evaluations.
- Cost multipliers based on infrastructure needed to support 10,000 concurrent users at equivalent protection levels. Each delay compounds security vulnerabilities across your user base, creating exponentially more opportunities for undesired content.
Business Impact
Faster guardrails mean a better user experience and tighter security. Even the slightest delays create windows for data leaks and harmful content to slip through.
