Slash AWS S3 API Costs by 80% Using Apache Kvrocks as an NVMe-Backed Caching Tier

July 22, 2026 • Marcus

The S3 API Cost Trap: High-Frequency Read/Write Workloads

AWS S3 storage pricing is notoriously low per gigabyte, but high-throughput workloads with millions of small objects (<100KB) often run into a financial wall driven by S3 API request pricing ($0.0004 per 1,000 GET requests and $0.005 per 1,000 PUT requests). When applications execute tens of millions of operations daily, API request fees easily overshadow raw storage costs by orders of magnitude.

While in-memory Redis is the standard choice for caching, managing terabytes of cached S3 payloads in RAM becomes cost-prohibitive. Disk-based Redis-compatible engines like Apache Kvrocks (which runs on RocksDB) bridged this gap by delivering Redis semantics on local NVMe instance storage at a fraction of the cost.

Architectural Overview: Disk-Based Redis Caching Tier

By placing an Apache Kvrocks cluster on AWS EC2 i4i or i3en instances with attached local NVMe drives directly in front of S3, you create a high-speed, persistent read-through/write-through cache. Key advantages include:

  • Sub-millisecond Latency: NVMe-backed RocksDB achieves near-RAM speeds for random reads/writes without RAM cost penalties.
  • S3 API Offloading: Over 90% of GET requests are fulfilled from local SSDs, completely bypassing S3 API charges.
  • Persistence & Fast Recovery: Unlike standard Redis, data survives process restarts without needing expensive snapshot reloads into memory.

Deploying Apache Kvrocks for S3 Payload Buffering

Below is an optimized configuration snippet for kvrocks.conf targeted at high-throughput NVMe instance storage (e.g., i4i.xlarge):

# Basic Network & Storage Settings
bind 0.0.0.0
port 6666
dir /mnt/nvme/kvrocks

# RocksDB Optimizations for NVMe
rocksdb.max_open_files -1
rocksdb.write_buffer_size 128
rocksdb.max_write_buffer_number 4
rocksdb.target_file_size_base 64
rocksdb.block_cache_size 8192
rocksdb.compression snappy

# Enable TTL and expiration background threads
rocksdb.metadata_block_cache_size 2048

Application Implementation: Read-Through Cache Logic

Below is a Python pattern utilizing redis-py and boto3 to manage S3 object caching via Kvrocks:

import redis
import boto3
from botocore.exceptions import ClientError

r = redis.Redis(host='kvrocks.internal', port=6666, db=0)
s3 = boto3.client('s3')
BUCKET = 'my-high-traffic-bucket'

def get_s3_payload(object_key: str) -> bytes:
    # 1. Check Kvrocks cache
    cached_val = r.get(object_key)
    if cached_val:
        return cached_val
    
    # 2. Cache Miss: Fetch from AWS S3
    try:
        response = s3.get_object(Bucket=BUCKET, Key=object_key)
        payload = response['Body'].read()
        
        # 3. Store in Kvrocks with a 7-day TTL
        r.setex(object_key, 604800, payload)
        return payload
    except ClientError as e:
        raise e

ROI & Cost Reduction Breakdown

For a service performing 50 million S3 GET requests per day for 50KB objects (2.5 TB daily transfer):

  • Direct S3 API Costs: ~$600/month in GET requests alone.
  • S3 Data Transfer / Request Fees at Scale (500M GETs/mo): ~$6,000/month.
  • Kvrocks Tier (Single i4i.xlarge instance): ~$178/month with 937GB NVMe storage handling 95%+ cache hit ratio.
  • Net Savings: Over 75% reduction in monthly infrastructure expenditure alongside a 10x drop in average payload retrieval latency.

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