Sold by: DataFramer by AIMon Labs
Deployed on AWS
DataFramer connects AI quality to business outcomes across production workflows. Discover failures, scale expert review, calibrate judges, run evaluations, and verify fixes.
Overview
This listing supports private offers. Contact info@dataframer.ai for enterprise pricing, deployment, and licensing options.
DataFramer is the AI Quality & Business Impact Platform for teams building and operating production AI. Connect AI traces, user behavior, workflow events, and expert judgment to understand not only how your AI is performing, but whether it is improving the user and business outcomes that matter. Measure accuracy, adoption, completion, cycle time, human effort, cost, and value across complete AI-powered workflows, then drill into the traces behind those outcomes.
DataFramer provides a connected quality loop for production AI. Discover known and unknown behavior patterns across traces, diagnose likely root causes with full workflow context, and route important cases to structured human review. Turn expert decisions into reusable ground truth, calibrate LLM judges against human verdicts, run evaluations and regression tests, and use calibrated judges as API guardrails. When broader test coverage is needed, generate synthetic evaluation data and rare edge cases from production traces or from scratch. DataFramer integrates with existing tracing tools including Langfuse and LangSmith and can be deployed as a DataFramer-hosted service or with the data plane in your own cloud account.
Highlights
- Connect AI quality to business impact: Correlate AI traces with user actions and workflow outcomes to measure accuracy, adoption, completion, cycle time, human effort, cost, and value across complete AI-powered journeys.
- Find, diagnose, and prevent production AI failures: Discover recurring and previously unknown behavior patterns, investigate likely root causes with full trace context, verify fixes, and track regressions as models, prompts, data, and workflows change.
- Turn expert judgment into scalable AI quality: Run structured human reviews with reusable rubrics, build auditable ground truth, calibrate LLM judges against human verdicts, run evaluations, deploy API guardrails, and generate synthetic edge cases for broader test coverage.
Details
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Deployed on AWS
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Pricing
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/month |
|---|---|---|
DataFramer Consumed Tokens | DataFramer Consumed Tokens (priced per M) | $1.00 |
DataFramer Users | DataFramer Users per month | $1.00 |
Dimensions summary
This contract has two separate billing dimensions that you buy together. The first charges for DataFramer Consumed Tokens, priced per million tokens the platform processes. This scales with how much AI activity you run through the tool. The second charges for DataFramer Users, billed per user each month. This scales with how many people access the platform. The dimensions are independent: token usage grows with workload, while user counts grow with team size. You pay for both based on your own consumption and headcount.
Top-of-mind questions for buyers
A user is a person with access to the platform, such as engineers, product managers, or domain experts who review traces. You are billed per user each month. The count scales with how many people you add to the platform, independent of how much AI activity you process.
Consumed Tokens are metered per million tokens the platform processes through model calls during evaluation, judging, and generation. You can bring your own model key (BYOM), so your provider bills you directly for those calls, or use DataFramer credits, where the platform covers model calls at raw API prices.
Both charges apply at the same time and bill independently. Token charges grow with AI workload: more traces, judge runs, and synthetic generation raise token use. User charges grow with team size. High-volume evaluation across many traces tends to make tokens the larger portion, while small teams processing light workloads see the reverse.
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Vendor refund policy
Usage-based charges (tokens, API calls, consumption) are non-refundable once metered. Subscription fees already billed are generally non-refundable. Refunds are considered in cases of verified misbilling or other rare cases, and must be requested through AWS Marketplace Support.
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Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Resources
Vendor resources
Support
Vendor support
DataFramer provides onboarding, implementation, deployment, and ongoing product support through video conferencing, Slack, email, and other agreed enterprise support channels.
Support is available at info@dataframer.ai , with product documentation at https://www.dataframer.ai/docs . Enterprise customers can receive dedicated support, deployment assistance, product updates, and enterprise service-level arrangements based on their agreement.
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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