GenAI & LLMs - Proof of Concept on AWS
Ankercloud GmbH · Operations & Productivity
Certification per AWS Marketplace.
Evidence tier Source Confirmed · 4 captures on record
What the publisher says
As described on AWS Marketplace.
## Problem Statement:
In the context of Generative AI (GenAI), the most common applications are based on Large Language Models (LLMs) which require powerful resources for training and hosting.
Show the rest of the publisher’s description (26 more lines)
Suitable on-premises resources are not always available and/or not giving optimal performance when dealing with such LLMs, especially if GPUs are needed.
With the AWS Cloud, you can access the necessary computing capacity on-demand, but powerful instances are generally quite expensive, and that’s why a successful Cloud adoption requires proper planning, testing, and cost-performance analysis.
## Our PoC solution
The Proof of Concept (PoC) approach is designed to give you all the tools to start using the Cloud to train and host LLMs and GenAI workloads. It includes the design of a complete solution and the deployment of a working sandbox environment on AWS which can be used for testing and benchmarking different server configurations or instance families.
## PoC components
Specifically, the following components are included in the PoC:
- Networking & Security
- Application Set-Up & Hosting
- Price and Performance Benchmarking
- Monitoring & Documentation
## Outcomes
At the end of the PoC, you will have a working prototype implemented on AWS, giving you full clarity on Cloud advantages, cost-performance ratio, Total Cost of Ownership (TCO), and important factors that you need to consider before deciding to commit long-term to the Cloud.
You will also understand how a Cloud setup can be integrated with your current IT infrastructure as a Hybrid or Multi-cloud environment, or if switching to a full Cloud-native environment is more convenient.
## Get Started With your PoC
To get started with a PoC for GenAI and LLMs, submit a request (Click 'Continue' on top right of the page) and get your FREE intro session with us. For any further questions, don’t hesitate to reach out at [cloudengagement@ankercloud.com](https://)
## AI Decision-Making for BFSI
Kickstart a pilot project where intelligent agents help automate credit checks, loan approvals, and document verification—all powered by LLMs.
## Smart Support Agent for Customer Care
Explore how an AI agent trained on your FAQs and historical tickets can handle real customer queries and escalate only when necessary.
## Virtual Shopping Assistant for Retail
Run a short-term demo of an AI-powered shopping assistant that understands customer behavior and suggests relevant products in real-time.
## Back-Office Task Bots
Test AI agents that can handle internal tasks like invoice approvals, meeting summaries, and basic HR workflows to show tangible time savings.
## Why Us?
Ankercloud is an AWS Advanced Partner with 80+ AWS Professional Certifications and 10+ AWS designations (competencies and delivery programs), with extensive Cloud, HPC and ML experience. Bring us the next challenge!
For further reading about use cases and past WAFR projects, please visit our website: [Ankercloud: case studies](https://www.ankercloud.com/case-studies)
Highlights
Highlighted by the publisher on AWS Marketplace.
Start using the AWS Cloud with a Proof of Concept implementation
Train, host, and benchmark your GenAI workloads and LLMs on a sandbox environment
Conduct cost-performance analysis for a fully informed Cloud evaluation
Agent build and provenance
See the full provenance
The layer-by-layer build, the evidence behind each claim, the risk basis and the cross-marketplace links are open to any account. Some rows are disclosed, some the source leaves Unknown; a free account shows you which.
Sources
Linked repositories
Unknown means this listing does not publish a repository. It is not a statement that the code is closed, and a linked repository is not a claim that the publisher wrote it: the registry computes that relationship privately and does not publish it.
Evidence risk is the share of the build you cannot see before you deploy, not a security rating. Sign in to see the layer-by-layer basis for this band.