Amazon Nova Forge is a development environment within Amazon SageMaker AI dedicated to building "Novellas" - private, custom versions of Amazon’s Nova frontier models.
Unlike typical AI services that only allow you to use a model or fine-tune its final layer, Nova Forge introduces a concept called Open Training. This gives you access to the model at various "life stages" (checkpoints), allowing you to bake your company’s proprietary knowledge directly into the model’s core reasoning capabilities.
This blog post is an introduction to Amazon Nova Forge and what makes it unique in the training process.
What Makes it Different?
Prompt engineering and RAG provide external context but fail to change a model's core intelligence. Standard fine-tuning also falls short because it happens too late in the lifecycle, attempting to steer a "finished" model that is already set in its ways. Nova Forge solves this by moving customization earlier into the training process, embedding specialized knowledge where it actually sticks.
Nova Forge occupies a unique middle ground between Managed APIs (Bedrock) and building from scratch.
- Amazon Bedrock: Bedrock is for consuming models. You can fine-tune them, but you are working on a "black box" model. Nova Forge is for building the model itself using deeper training techniques.
- Azure AI / Google Vertex AI: While Azure and GCP offer fine-tuning, they generally don't provide access to intermediate training checkpoints of their frontier models. Nova Forge allows for Data Blending, where you mix your data with Amazon’s original training data to prevent the model from "forgetting" how to speak or reason.
Terminology
- Novella: The resulting custom model you create. It’s a "private edition" of Nova.
- Checkpoints: Saved "states" of the model during its initial training (pre-training, mid-training, post-training).
- Data Blending: The process of mixing your proprietary data with Nova-curated datasets so the model stays smart while learning your specific business.
- Reinforcement Fine-Tuning (RFT): Using "reward functions" (logic-based feedback) to teach the model how to perform complex, multi-step tasks correctly.
- Catastrophic Forgetting: A common AI failure where a model learns new information but loses its original abilities. Nova Forge is designed specifically to prevent this.
The Workflow: From Training to Production
The process bridges the gap between the "lab" (SageMaker) and the "app" (Bedrock).
- Selection: You choose a Nova base model and a specific checkpoint (e.g., a "Mid-training" checkpoint) in Amazon SageMaker Studio.
- Training (SageMaker AI): You use SageMaker Recipes—pre-configured training scripts—to blend your data from S3 with Nova’s datasets. The heavy lifting (compute) happens on SageMaker's managed infrastructure.
- Refinement: Optionally, you run RFT in SageMaker to align the model with specific business outcomes or safety guardrails.
- Deployment (Bedrock): Once the "Novella" is ready, you import it into Amazon Bedrock as a private model.
- Production: Your applications call the custom model via the standard Bedrock API, benefitting from Bedrock’s serverless scaling and security.
Below is a sample training workflow:
Data Privacy and Protection
The security model is the most critical part:
- Sovereignty: Your data stays in your S3 buckets and within your VPC boundaries.
- No Leakage: AWS explicitly states that customer data is not used to train the base Amazon Nova models. Your "Novella" is a private resource visible only to your AWS account.
- Encryption: Data is encrypted at rest via KMS (AWS-managed or Customer-managed keys) and in transit via TLS 1.2+.
- Governance: Access is controlled via standard IAM policies, and all training activity is logged in CloudTrail.
Pricing Model
Nova Forge carries a distinct cost structure that reflects its "frontier" status:
- Subscription Fee: Access to the Forge environment starts at approximately --$100,000 per year.
- Usage Costs: On top of the subscription, you pay for the SageMaker compute (GPUs) used during the training phase.
-
Comparison: Cheaper than Training from Scratch: Building a frontier model from zero costs millions in compute and months of R&D. Nova Forge provides the "shortcuts" to get the same result for a fraction of that.
- More Expensive than Basic Fine-Tuning: Standard fine-tuning on Bedrock is much cheaper (often just a few dollars per hour), but it cannot achieve the deep "domain-native" intelligence that Nova Forge provides.
Summary
Amazon Nova Forge marks a shift from generic AI to native intelligence, where models don't just reference your data—they are built from it. By using "Open Training," you can bake specialized knowledge into the model’s core at the pre-training or mid-training stages. This results in a private Novella that understands your specific industry as naturally as its base language.
Organizations managing high-value proprietary data should consider moving beyond treating that information as an external reference. If your workflows involve specialized terminology or regulated processes that standard LLMs struggle to master, shifting customization earlier in the training lifecycle is often more effective than basic fine-tuning.
Disclaimer: AI tools were used to research and edit this article. Graphics are created using AI.
Additional references
About the Author
Eyal Estrin is a cloud and information security architect and AWS Community Builder, with more than 25 years in the industry. He is the author of Cloud Security Handbook and Security for Cloud Native Applications.
The views expressed are his own.
Top comments (0)