Sold by: NeenOpal
NeenOpal migrates your generative AI workloads from third-party LLM APIs to Amazon Bedrock, reducing costs, improving compliance, and restoring model flexibility.
Overview
Many organizations built their first generative AI applications quickly on whichever third-party API was available at the time. That approach got products to market. It also created dependencies that are now difficult to manage: data leaving your AWS environment on every inference call, inference costs that scale unpredictably with usage, models that cannot be swapped without rearchitecting integrations, and a compliance posture that requires ongoing justification.
NeenOpal's LLM Migration to Amazon Bedrock service resolves these dependencies without requiring applications to be rebuilt from scratch. Our methodology moves systematically from dependency audit through prompt adaptation, embedding migration, parallel quality evaluation, and production cutover - with rollback controls at every stage.
Proven Results
We delivered this migration for a wealth management firm that had built AI-powered content generation on OpenAI GPT but faced data security, compliance, and cost challenges. After migrating to Amazon Bedrock, the firm achieved:
- 42% reduction in inference costs
- 60% faster compliant content generation
- 50% shorter review cycles
Read the full case study at neenopal.com/case-studies/openai-to-amazon-bedrock-llm-migration
What We Deliver
- Dependency audit covering all LLM API calls, prompt templates, chain configurations, and embedding integrations in scope
- Model selection assessment across Amazon Bedrock's foundation model catalog matched to application requirements, cost targets, and quality thresholds
- Prompt adaptation and testing to preserve output quality and behavioral consistency after model substitution
- Embedding pipeline migration from third-party providers to Amazon Titan Embeddings or compatible Bedrock models
- Parallel evaluation environment running source and target models against application-specific test datasets with structured quality scoring
- Production cutover with traffic shifting controls, rollback triggers, and post-migration monitoring configuration
Data Handling and Compliance
All evaluation datasets and migration workloads remain within your AWS account. The parallel evaluation environment operates entirely within your VPC, ensuring data never leaves your AWS boundary during the migration process. This approach supports organizations operating under data residency and regulatory requirements in financial services, healthcare, and other regulated industries.
Where This Applies
- Organizations with data residency requirements that third-party LLM APIs cannot satisfy
- Technology companies seeking to reduce inference costs by migrating from premium third-party APIs to optimized Bedrock configurations
- Organizations with multi-application AI portfolios needing a consistent, governed LLM infrastructure layer
- Teams that built on OpenAI or similar providers and require model flexibility as the foundation model landscape evolves
Typical Engagement Timeline
Engagements typically span 4 to 8 weeks depending on the number of LLM integrations in scope:
- Week 1-2: Dependency audit and model selection assessment
- Week 2-4: Prompt adaptation, embedding migration, and test dataset preparation
- Week 4-6: Parallel evaluation with structured quality scoring
- Week 6-8: Production cutover with traffic shifting and rollback controls
Timelines scale based on the number of applications, complexity of prompt chains, and volume of embedding pipelines.
How to Get Started
Begin with a 30-minute dependency audit scoping call where we assess the number of LLM integrations, current API dependencies, and compliance requirements. From there, we deliver a migration feasibility assessment outlining scope, timeline, and expected outcomes tailored to your environment.
Why NeenOpal
- AWS Advanced Tier Services Partner with AI, Data and Analytics Competency
- Evaluation methodology validates output quality at the application level, not just against generic model benchmarks
- Migration approach is reversible at each stage with rollback controls built into the cutover plan
- Post-migration architecture includes cost management, model versioning, and Bedrock Guardrails configuration for long-term operational reliability
Highlights
- Proven migration results: A wealth management firm migrating from OpenAI GPT to Amazon Bedrock achieved 42% cost reduction, 60% faster compliant content generation, and 50% shorter review cycles. Our structured methodology - dependency audit, model selection, prompt adaptation, parallel evaluation, and production cutover - de-risks each stage before any production traffic moves.
- Parallel evaluation validates quality against your standards: Evaluation environments run source and target models side by side against application-specific test datasets with structured quality scoring, ensuring migrated workflows perform to the same standard users expect - not just against generic model benchmarks.
- Data stays in your AWS environment throughout migration: All evaluation datasets and migration workloads operate within your AWS account and VPC. Post-migration architecture includes cost management, model versioning, and Bedrock Guardrails configuration, giving you a governed LLM infrastructure layer with full model flexibility as the foundation model landscape evolves.
Details
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Pricing
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Support
Vendor support
Engagement Support
During the migration engagement, your team will have a dedicated NeenOpal project lead as your primary point of contact. Communication is maintained through scheduled check-ins aligned with each methodology phase (dependency audit, prompt adaptation, parallel evaluation, and cutover).
Buyer Responsibilities
To ensure successful delivery, your team provides:
- Access to existing prompt templates, API call logs, and chain configurations
- Embedding pipeline documentation and test datasets for quality evaluation
- AWS account access for deployment of the parallel evaluation environment
- A designated engineering liaison available for technical coordination
Post-Migration Support
After production cutover, NeenOpal provides post-migration monitoring configuration and a rollback playbook. The cutover plan includes defined rollback triggers so your team can revert independently if needed.
Deliverable Artifacts
Your team receives a complete migration runbook, model selection rationale document, quality evaluation report with scoring methodology, and production cutover plan with rollback procedures.
Contact
For scoping inquiries, migration feasibility assessments, or support during an active engagement, contact us at aws_marketplace@neenopal.com .
NeenOpal is an AWS Advanced Tier Services Partner with AI, Data and Analytics Competency, SaaS Competency, and Managed Service Provider accreditation.