Sold by: ETIQ AI
Deployed on AWS
AWS Free Tier
A software solution to identify opportunities and test for weaknesses in your AI
Overview
Etiq.ai is a flexible and customizable software tool for data scientists, risk, and business managers in the insurance, lending, and the finance technology industry. Etiq helps to discover new business opportunities, mitigate risk, increase productivity, data accuracy, and transparency by identifying and mitigating unintended bias in machine learning algorithms. Etiq's functionality allows data scientists & risk managers to test and store pipelines, then make parallel comparisons between pipelines when experimenting with debiasing. It allows you to identify areas of improvement, perform model monitoring and diagnosis in your algorithms. For both tech and commercial teams, an interactive dashboard that helps understand and substantiate the drivers behind machine learning models, showcase risk evaluation, accuracy, fairness, and growth potential.
Highlights
- AI monitoring
- AI governance
- Machine learning
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu 22.04
Deployed on AWS
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time. Alternatively, you can pay upfront for a contract, which typically covers your anticipated usage for the contract duration. Any usage beyond contract will incur additional usage-based costs.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
If you are an AWS Free Tier customer with a free plan, you are eligible to subscribe to this offer. You can use free credits to cover the cost of eligible AWS infrastructure. See AWS Free Tier for more details. If you created an AWS account before July 15th, 2025, and qualify for the Legacy AWS Free Tier, Amazon EC2 charges for Micro instances are free for up to 750 hours per month. See Legacy AWS Free Tier for more details.
Dimension | Cost/hour |
|---|---|
t3.small Recommended | $5.34 |
t2.micro | $5.34 |
t3.micro | $5.34 |
t3.medium | $5.34 |
t2.medium | $5.34 |
c6in.large | $5.34 |
t3a.medium | $5.34 |
c5ad.large | $5.34 |
c6i.large | $5.34 |
c5n.large | $5.34 |
Dimensions summary
You pay by the hour based on the AWS EC2 instance type you run the tool on. Each dimension maps to a specific instance size, so pricing scales with the compute capacity you choose. Smaller instances like t2.micro and t3.micro cost less per hour, while compute-focused instances such as the c-series carry different hourly rates. You only pay for the hours you use, with no upfront commitment. Choose the instance that fits your workload for testing, explaining, and optimizing your data and AI code. Software charges are separate from the underlying AWS infrastructure charges.
Top-of-mind questions for buyers
Each dimension maps to one AWS EC2 instance size that hosts the tool. The t-series instances are general-purpose with burstable CPU, suited to lighter testing. The c-series instances are compute-optimized for heavier data and code analysis. You pick the size matching your workload, and you pay per running hour.
Software charges meter running hours only. A fully stopped instance stops accruing hourly software charges. Underlying AWS infrastructure charges, such as storage, may still apply while the instance is stopped. You pay for the hours the instance actively runs, with no upfront commitment.
The tool scans your code and data to build a lineage graph, then recommends and runs tests at each step. It traces failures to their root cause and verifies fixes. This supports debugging, verifying outputs, and reducing errors in data and AI pipelines while you run the chosen instance.
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Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
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Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
- Data Fingerprinting
- Added Apple Silicon Support
Additional details
Usage instructions
Resources
Vendor resources
Support
Vendor support
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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Bias Detection and Mitigation
Identifies and mitigates unintended bias in machine learning algorithms across insurance, lending, and fintech applications.
Model Testing and Comparison
Enables testing and storage of pipelines with parallel comparison functionality for experimenting with debiasing techniques.
Model Monitoring and Diagnosis
Provides model monitoring and diagnosis capabilities to identify areas of improvement in algorithms.
Fairness and Risk Evaluation
Evaluates and substantiates machine learning model drivers including fairness metrics, risk assessment, and accuracy measurements.
Interactive Analytics Dashboard
Offers an interactive dashboard for visualizing model performance, risk evaluation, fairness metrics, and growth potential across technical and commercial teams.
Data Quality Monitoring
Automated monitoring and alerting across data vitals with out-of-the-box anomaly detection and configurations for identifying data quality issues.
Multi-Modal Data Support
Capability to monitor tabular, image, and text data types across machine learning applications and data pipelines.
Privacy-Preserving Architecture
Platform operates on processed data summaries rather than raw data, enabling privacy preservation and no-configuration deployment at scale.
Comprehensive ML Observability
Unified monitoring of model inputs, outputs, performance metrics, data drift, concept drift, and upstream data quality issues in a single platform.
Broad Integration Ecosystem
Integration with popular ML and data tools including Pandas, Apache Spark, AWS SageMaker, MLflow, Flask, Ray, RAPIDS, and Apache Kafka.
Agent and Application Observability
Full visibility into AI agent behavior through tree-structured traces capturing user inputs, routing logic, tool calls, memory access, and model outputs with native support for Amazon Bedrock Agents and open frameworks.
Prompt Optimization and Testing
Prompt IDE environment for designing, testing, and comparing prompt versions side by side with live inputs, outputs, and integrated evaluation results to measure quality and performance.
LLM and Agent Evaluation
Offline and online LLM-as-a-Judge evaluations to assess accuracy, tool-calling, planning, and goal achievement with continuous evaluation against production traffic.
Governance and Audit Controls
Span-level audit trail capturing every agent action including reasoning steps, tool calls, retrieval results, model inputs and outputs, with role-based access control, SSO, custom roles, and compliance certifications including SOC 2 Type II, ISO/IEC 27001, HIPAA, and GDPR.
Real-Time Monitoring and Alerting
Custom metrics definition and monitoring of latency, token usage, and failures with configurable alerts and anomaly detection capabilities for production issue prevention.
Standard contract
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