Customize Only with Evidence

Customization should be driven by measurable gaps that cannot be solved by prompt + retrieval + tool design alone. Amazon Bedrock offers three managed customization methods, plus a path for bringing your own weights:

• Supervised fine-tuning — train on labeled prompt/response pairs to shift behavior for specific tasks.
• Reinforcement fine-tuning — define a reward function (via AWS Lambda) that scores response quality; the model learns iteratively from those scores instead of fixed labels.
• Model distillation — transfer a larger "teacher" model's behavior into a smaller, cheaper "student" model using teacher-generated or invocation-log-derived training data.
• Custom Model Import — bring your own fine-tuned open-weight model (Llama, Mistral, Mixtral, Qwen, GPT-OSS, and more) trained elsewhere (e.g. SageMaker) into Bedrock via Amazon S3 Safetensors weights.

Any customized or high-throughput model generally requires Provisioned Throughput (billed hourly in Model Units, with no-commitment, 1-month, or 6-month terms) — factor that cost into your ROI case before committing.