OpenAI to Amazon Bedrock Migration Assessment
Unlock the Potential of Amazon Bedrock with Enhanced Efficiency, Security, and Performance
Our Migration Assessment Service offers a comprehensive evaluation of your OpenAI setup to identify the benefits of moving to Amazon Bedrock. Led by certified AWS architects, the assessment includes cost, security, and performance analysis, along with a customized migration roadmap for a seamless transition.
Your benefits
Gain clear insights into how migrating to Amazon Bedrock could impact costs, performance, and security, helping you make well-founded strategic decisions.
Tailored Migration Roadmap
Receive a plan designed specifically for your organization, minimizing disruptions and ensuring a smooth transition.
Risk Mitigation
Identify and address potential challenges early, allowing for a proactive approach to security, reliability, and performance concerns before migration.
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At Ingeno, we believe that successful cloud migrations start with the right partnerships. That’s why we’ve joined forces with industry leader AWS to bring you this service backed by unmatched expertise and experience.
Our process
Engagement Kick-Off
Align your vision and set clear expectations
Analysis & Benchmarking
Perform an in-depth analysis of your existing OpenAI setup
Migration Roadmap Development
Receive a tailored migration roadmap with actionable steps, timelines, and resources to support a smooth, efficient transition to AWS Bedrock
Swift 6-Week Assessment Process
This service delivers a comprehensive migration assessment within a structured 6-week timeframe. During this period, our team thoroughly evaluates your current OpenAI setup, benchmarks it against AWS Bedrock capabilities, and develops a tailored migration plan. The six-week process ensures efficiency without compromising depth, enabling you to make quick, informed decisions.
What is Covered?
Quality
The quality of the migration to AWS Bedrock is prioritized through rigorous validation and data processing practices. Automated testing ensures AWS large language models (LLMs) output meets or exceeds OpenAI standards, guaranteeing consistent and accurate results. Data processing methods are tailored, employing both simple and advanced chunking techniques for optimal handling. Stability is maintained with fallbacks, circuit breakers, and error handling mechanisms, creating a resilient system that adapts to unexpected situations.
Generative AI Flow Complexity
Our service manages complex generative AI flows to ensure versatile model interactions. We support both single model calls as well as sequential and parallel calls, facilitating various AI operations. The setup handles agent graphs, conditional flows, and retrieval-augmented generation (RAG) processes. Flexibility extends to multi-model and multi-modality scenarios, allowing the integration of various models and data formats for richer outputs.
Integrations
Seamless integration with IoT and external APIs maximizes AWS Bedrock’s capabilities. Our approach addresses API schema complexity for smooth interactions across systems. Prompt-to-API mapping enhances integrations, facilitating efficient communication between user prompts and API responses, streamlining interaction with external services.
UX Considerations
User experience (UX) is designed to meet different application contexts, from batch processing to specific application environments. Conversational contexts are managed for relevant interactions, with options for synchronous or streaming responses based on real-time needs. This flexibility allows for a UX that aligns with diverse user and application requirements.
Localization
Localization support ensures the migration aligns with global needs. Language support extends to prompts, outputs, and feedback, enabling multi-language interactions. Region localization allows data and model processing within preferred data regions, enhancing compliance and performance.
Operations
Operational excellence is achieved through observability and a robust infrastructure-as-code approach, enabling continuous monitoring and agile deployments. Security is ensured with role-based access controls, permissions, prompt injection prevention, and guardrails for data protection. Multitenancy provides customer isolation, ensuring secure experiences for each client. Additionally, configurability through feature flags allows for custom adjustments, adapting the service to evolving business needs.