Generative AI on AWS: SageMaker AI & Bedrock

Most of the demand we see for SageMaker in 2026 is not classic machine learning. It is generative AI: fine-tuning an open-weight model on proprietary data, putting a retrieval layer in front of an LLM, or serving a model at a cost the unit economics can bear. AWS gives you two very different ways to do that, Amazon Bedrock (managed model APIs) and Amazon SageMaker AI (you own the model and the endpoint), and the right answer is usually a mix. We help you choose, then build it.

Fine-tuning open-weight models

When a prompt is not enough, we fine-tune. Typical engagements:

• Instruction and domain fine-tuning of Llama, Mistral, and Qwen families using SageMaker JumpStart recipes or custom Hugging Face training jobs with LoRA / QLoRA adapters.
• Large runs on SageMaker HyperPod, including flexible training plans when you need reserved accelerator capacity for a multi-day job rather than hoping on-demand p5 / p4d instances are available.
• Dataset engineering: deduplication, PII scrubbing, instruction formatting, and held-out evaluation sets, which matter more to the final result than the training hyperparameters do.

Every fine-tuning engagement ends with an evaluation report against your own task set, not a leaderboard number.

Retrieval-augmented generation (RAG)

RAG is the workhorse pattern for "chat with our documents" and internal assistants. We build both flavours:

• Bedrock Knowledge Bases when you want a managed ingestion pipeline, managed vector store, and fast time to value.
• Custom retrieval on Amazon OpenSearch Service or PostgreSQL with pgvector when you need hybrid search, custom chunking, metadata filtering, or re-ranking that the managed path does not expose, with the generation step on a SageMaker AI endpoint or Bedrock.

We instrument retrieval quality (recall at k, answer faithfulness) from day one so you can tell a retrieval problem from a model problem.

LLM inference engineering

Serving is where generative AI budgets go to die. We size and tune SageMaker AI endpoints for LLM workloads:

• The Large Model Inference (LMI) container with vLLM or TensorRT-LLM backends, and the Hugging Face TGI container, with continuous batching and paged attention enabled correctly.
• AWS Inferentia2 (inf2) instances for price-performance on steady-state traffic, and g6 / g5 GPU instances when flexibility matters more.
• Quantization (AWQ, GPTQ, FP8) and speculative decoding, with latency and quality measured before and after.
• Autoscaling, asynchronous inference, and serverless endpoints so you are not paying for a GPU at 3 a.m. to serve nobody.

Evaluation and guardrails

A model that cannot be measured cannot be shipped. We build evaluation harnesses (LLM-as-judge plus human review where it counts), regression suites that run in your CI pipeline, and guardrails for prompt injection, PII leakage, and off-topic output using Bedrock Guardrails or custom classifiers.

Build vs. buy decision support

Not every team should fine-tune, and not every team should own endpoints. We run a short, structured GenAI readiness assessment: your use cases, data, latency and cost targets, compliance constraints, and team skills, followed by a written recommendation (Bedrock, SageMaker AI, or hybrid) with a cost model at your expected traffic. If the answer is "use a managed API and move on", we will say so.

Why NeuralArmada

• Senior, US-based consultants who have shipped production LLM systems on AWS, not just demos.
• Onsite or remote, as part of your team or as a stand alone resource.
• We cover the whole path: data, training, serving, evaluation, and the MLOps around it.

Ready to move a generative AI project from prototype to production? Contact us for a GenAI readiness assessment.