LLM Migration to Amazon Bedrock by NeenOpal

Many organizations built their first generative AI applications quickly, on whichever third-party API was available and capable 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. We have executed this migration for businesses where compliance, cost, and model flexibility were each driving factors. Our methodology moves systematically from dependency audit through prompt adaptation, embedding migration, parallel quality evaluation, and production cutover, with rollback controls at every stage.

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

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 continues to evolve

Why NeenOpal • Delivered LLM migration for businesses on Amazon Bedrock with 60% faster compliant content generation post-migration • 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 before any production traffic shifts • Post-migration architecture includes cost management, model versioning, and Bedrock Guardrails configuration for long-term operational reliability