Three‑Layer Caching Pipeline to Cut Bedrock Inference Costs
Add a hash‑based and semantic cache in Redis before calling Bedrock to cut token usage and costs.
Implement the hash and semantic caching layers in your Lambda to avoid unnecessary Bedrock calls.
Summary
The post describes a three‑layer caching pipeline built in a single AWS Lambda function backed by ElastiCache Redis to cut Bedrock inference costs. Layer 1 normalizes prompts, computes a SHA‑256 hash, and checks Redis for a hash hit, returning cached answers instantly. Layer 2 embeds the prompt with Titan Embeddings v2, compares it to stored vectors using cosine similarity, and serves a semantic hit if similarity exceeds a threshold.
If both layers miss, the prompt is compressed and sent to Bedrock, after which the answer is written back to Redis for future hits. The architecture uses Lambda (Python), Redis 7.1, Bedrock Nova Micro, and the pipeline runs across invocations thanks to the persistent cache. This pattern dramatically reduces Bedrock calls for FAQ‑style workloads, saving tokens and cost. The author encourages readers to implement the hash and semantic caching layers in their own Lambda functions.
Key changes
- Layer 1: normalize prompt, compute SHA‑256 hash, check Redis with "hash:" prefix.
- Layer 2: embed prompt using Titan Embeddings v2, compute cosine similarity against stored vectors.
- Layer 3: compress prompt before Bedrock call.
- Lambda (Python) uses ElastiCache Redis 7.1 for persistent cache.
- Bedrock Nova Micro called only on cache miss.
- Semantic caching uses vector embeddings and cosine similarity.
- Hash caching handles exact duplicates.
- Pipeline reduces Bedrock calls and token costs.