Sold by: Seekr
Build accurate, explainable AI and AI Agents securely with your data with SeekrFlow in AWS. Improve model accuracy by up to 3x and reduce data preparation costs by 90%, all while maintaining full control over your data residency and compliance.
Overview
SeekrFlow is the trusted platform for enterprise and government customers operating in environments where mission success demands the highest standards. Offering agents, agentic data preparation, training, explainability, and orchestration in one secure platform, SeekrFlow helps you rapidly scale your AI development and speed up your time to ROI. If you're a developer looking to scale the deployment of custom AI applications, use SeekrFlow on AWS to:
- Automate the hardest parts of dataset preparation: SeekrFlow generates, cleans, labels, and augments data into an AI-ready format.
- Deploy agents that retain memory across sessions, adapt based on user history and preferences, and follow structured plans to execute tasks with autonomy and accuracy.
- Build fully customizable AI tailored to your use case, with the flexibility to scale and the control to adapt to your needs.
- Gain full visibility into model behavior with built-in explainability tools and input parameters.
Highlights
- SeekrFlow is a full-stack, enterprise-grade AI platform that addresses the entire AI lifecycle from data preparation and training to deployment and inference, empowering your team to build domain-specific AI applications quickly and securely in one place.
- SeekrFlow Agents is a full-stack solution for deploying intelligent agents securely at scale, combining model hosting, domain-specific fine-tuning, file search, custom tool integration, agent orchestration, and a comprehensive observability platform.
- Keep your data protected. SeekrFlow is SOC2 compliant, and offers containerized deployment, so no data leaves your hardware.
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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 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/12 months |
|---|---|---|
GPU | Quantity of GPU compute units | $7,000.00 |
Dimensions summary
SeekrFlow uses a contract pricing model built around a single dimension: GPU units. You commit to a quantity of GPU compute units, and your cost scales with how many units you reserve. This structure lets you match compute capacity to your workload, whether you run data preparation, fine-tuning, inference, or agentic workflows. Adding more GPU units increases capacity for training and running models. Because pricing is based on reserved compute rather than usage counts, your spend is tied to the GPU quantity you contract for.
Top-of-mind questions for buyers
A GPU unit is a reserved block of GPU compute you contract for. It powers data preparation, fine-tuning, inference, and agentic workflows within the platform. Deployments run on dedicated endpoints with reserved capacity, giving predictable throughput and latency instead of shared, serverless resources.
Your cost is tied to the GPU units you reserve, not to per-output or per-request counts. Reserved capacity holds steady through traffic spikes, so sudden demand does not change your bill. To handle larger models or more throughput, you contract additional GPU units.
The GPU unit reflects reserved compute capacity rather than a fixed chip. Dedicated endpoints let you choose from several hardware options to balance throughput, latency, and cost. The contracted unit quantity determines your capacity regardless of which hardware backs it.
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Build accurate, explainable AI securely with your data with SeekrFlow in AWS. Improve model accuracy by up to 3x and reduce data preparation costs by 90%, all while maintaining full control over your data residency and compliance.
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