Introduction
Building GenAI apps sounds expensive.
And sometimes, it is, especially if you jump straight into fine-tuning LLMs or spinning up GPU clusters without a plan.
But here’s the good news: AWS offers several ways to run GenAI workloads cost-effectively if you know where to look.
In this post, we’ll share practical strategies to build, test, and deploy GenAI applications without burning your cloud budget.
The 3 Major Cost Drivers of GenAI on AWS
Before we talk savings, let’s understand where the money goes:
1. Model Inference Costs
- API calls to foundation models (e.g., Bedrock pricing per 1K tokens)
- Hosted endpoints (e.g., SageMaker real-time inference)
2. Training & Fine-Tuning Costs
- GPU compute (ml.p3, ml.g5, etc.)
- Data preparation, versioning, monitoring
3. Data Storage & Retrieval
- Storing embeddings, vector indexes, logs (S3, OpenSearch, RDS)
7 Cost-Saving Strategies for GenAI on AWS
1. Use Bedrock Instead of Hosting Your Own Models
- Bedrock is serverless—you only pay per use
- Skip GPU provisioning, scaling, and monitoring
- Perfect for bursty inference patterns
Example: For a chatbot getting ~200 queries/day, Bedrock API is cheaper than running a 24/7 GPU instance.
2. Use Batch Inference Where You Can
- Swap SageMaker real-time endpoints with Batch Transform
- Process thousands of inputs with less compute overhead
- Ideal for:
- Document summarization
- Dataset tagging
- Nightly analytics
3. Start with SageMaker JumpStart
- Test pre-trained models (Flan-T5, Falcon, Stable Diffusion)
- No GPU spin-up required
- Feasibility first, fine-tuning later
JumpStart = try before you train.
4. Use Spot Instances for Fine-Tuning
- Up to 90% cheaper than on-demand
- Best for non-time-sensitive jobs like training or embeddings
- Use Managed Spot Training for automated recovery
5. Use Titan Embeddings Instead of OpenAI for Vector Workflows
- AWS-native and cost-effective
- Cheaper per embedding compared to OpenAI’s text-embedding-ada
- Keeps your data within AWS
- Natively integrates with OpenSearch and S3
6. Right-Size Your Infrastructure from Day One
- Don’t POC with g5.12xlarge, start small (e.g., g5.xlarge)
- Enable auto-scaling on endpoints
- Monitor usage patterns with CloudWatch dashboards
7. Turn Off Idle Endpoints
- Use Lambda + Bedrock API for on-demand queries
- For internal tools, schedule auto start/stop scripts
- No point in paying for silence
Bonus Cost-Saving Tips
- Enable CloudTrail + Cost Explorer to catch spikes
- Use Cost Anomaly Detection to alert you to billing surprises
- Pre-cache common responses to minimize repeat API calls
Conclusion
GenAI on AWS doesn’t need to be an enterprise-only affair.
With smart architectural choices, startups and teams can deploy and iterate GenAI products for hundreds, not thousands, of dollars a month.
It’s not about being cheap. It’s about being smart.
Want to see a GenAI architecture diagram or deployment pack tailored for AWS?
Contact us for more details.
