Platform Deep Dive

How Cachee Actually Works

A Rust-native AI caching layer that overlays your existing infrastructure. No migration required. Four steps from request to response, measured in nanoseconds, with AI predicting what your systems need before they ask.

1.21ns

L1 Cache Hit

827M

Operations / sec

95%+

L1 Hit Rate

<1hr

Deploy Time

Architecture

Request Lifecycle: Before vs After

Watch how a data request travels through your stack. Every hop adds latency you are paying for. Then see what happens when Cachee intercepts the chain.

👤

User Request

0ms

🌐

API Gateway

2.5ms

App Server

5ms

🔴

Redis Cache

12ms

Cache Miss → DB

25ms

Response

3ms

Total Request Latency

47.5ms

6 hops · 2 network round-trips · 1 database query

👤

User Request

0ms

🌐

API Gateway

0.5ms

Cachee L1

0.001ms

Response

0.3ms

🔴

Redis

skipped

Database

skipped

Total Request Latency

0.801ms

3 hops · 0 database queries · 95% served from L1 · 59x faster

👤

User Request

0ms

AI Pre-Fetched

0.001ms

Instant Response

~0ms

🌐

Gateway

bypassed

🔴

Redis

bypassed

Database

bypassed

Total Request Latency

0.001ms

Data pre-fetched by AI · Already in L1 before request arrives · 47,500x faster

Auto-cycling views

The Pipeline

Four Steps. Sub-Millisecond.

Every request that hits Cachee passes through a four-stage pipeline. Each stage is optimized in Rust for zero-copy, lock-free execution. The entire pipeline completes before most systems finish a single network hop.

AI Prediction

ML models analyze access patterns in real-time, predicting which data your application will request next. Models train continuously on your traffic, reaching 95%+ accuracy within hours.

~50ns prediction

Tiered Storage

Hot data lives in L1 CPU cache (1.21ns). Warm data in L2 memory (3-5ns). Cold data in L3 SSD (~100ns). AI manages promotion and eviction across all tiers automatically.

1.21ns L1 hit

Consistency Engine

Write-through invalidation with causal ordering ensures stale data is never served. Sub-microsecond propagation across all cache tiers. CRDT-based conflict resolution for distributed deployments.

<1µs propagation

Adaptive Tuning

The system continuously optimizes itself. Cache sizes, eviction policies, TTLs, and prefetch aggressiveness are all adjusted in real-time based on workload characteristics. Zero manual tuning required.

Continuous

Try It

See It Running Live

Deploy Cachee in your environment in minutes. Our CLI handles configuration, connection, and optimization automatically.

$ npm install -g @cachee/cli

$ cachee init --project my-app

Detecting infrastructure... found Redis 7.2, PostgreSQL 15

Generating config... done

$ cachee deploy --watch

Deploying Cachee overlay...

L1 cache initialized (4096 slots, 512MB)

AI model training started on live traffic

Status: ACTIVE | Hit rate: 87% (warming) | Latency: 3.2ns

Status: OPTIMIZED | Hit rate: 95.3% | Latency: 1.21ns

Origin load reduced by 94.7% | Est. savings: $2,847/mo

Live Interactive Demo Start Free Trial

Capabilities

Platform Capabilities

Every feature is designed for production workloads at scale. No toy benchmarks. No asterisks. These are the capabilities running in production today.

Rust-Native Engine

Zero-copy, lock-free data paths. No garbage collection pauses. No runtime overhead. The entire hot path runs in CPU cache lines, delivering consistent nanosecond latency under load.

1.21ns average L1 hit latency

AI Prediction Engine

Lightweight transformer models trained on your access patterns. Predicts next-access with 95%+ accuracy. Models update every 30 seconds without downtime. Custom per-tenant model isolation.

95.3% hit rate in production

3-Tier Storage

L1 (CPU cache, 1.21ns), L2 (memory, 3-5ns), L3 (NVMe, ~100ns). AI manages data placement across tiers. Hot data automatically promoted, cold data evicted. No manual tuning.

128x storage reduction vs raw

Overlay Architecture

Deploys alongside your existing Redis, Memcached, or database. No migration. No code changes. Cachee intercepts requests at the network layer and serves from L1 when possible.

Zero code changes required

Multi-Region Sync

CRDT-based eventual consistency across regions. Sub-millisecond local reads with automatic conflict resolution. Causal ordering guarantees prevent stale reads after writes.

Global consistency in <5ms

Enterprise Security

AES-256 encryption at rest and in transit. SOC2 Type II compliant. GDPR data residency controls. Role-based access. Audit logging. Tenant isolation with zero data leakage.

SOC2, GDPR, HIPAA ready

Comparison

How Cachee Compares

Side-by-side with the caching solutions you already know. Same metrics, same workloads, independently verifiable.

Metric	Redis	Memcached	CloudFront	Cachee
Read Latency (p50)	0.8 - 2ms	0.5 - 1ms	5 - 50ms	1.21ns
Read Latency (p99)	5 - 15ms	3 - 8ms	50 - 200ms	12ns
Throughput	500K ops/s	1M ops/s	N/A (CDN)	827M ops/s
AI Prediction	None	None	None	95%+ accuracy
Auto-Tuning	Manual TTLs	Manual config	Basic TTLs	Fully autonomous
Network Hops	2-3 hops	2-3 hops	1-4 hops	0 (in-process)
GC Pauses	Rare (C)	None (C)	Varies	None (Rust)
Origin Load Reduction	60 - 80%	60 - 75%	40 - 70%	95%+
Deploy Complexity	Moderate	Moderate	Low (CDN)	1 command overlay

ROI Calculator

Calculate Your Savings

Input your current infrastructure metrics. See exactly what changes when Cachee deploys. All calculations use conservative estimates based on production deployments.

Monthly Requests

100M

Current Infra Spend ($/month)

$85,000

Average Response Latency (ms)

47ms

Current Cache Hit Rate (%)

65%

Monthly Savings

$0 annually

0ms

New Avg Latency

0x faster

ROI Multiplier

Based on Scale tier ($500/mo)

Benchmarks

Production Benchmarks

These numbers come from production deployments, not synthetic benchmarks. Measured on real infrastructure under real workloads. All benchmarks are independently reproducible.

L1 Cache Read Latency

nanoseconds (p50)

Redis: 800,000ns Cachee: 1.21ns

Operations Per Second

million ops/sec (single node)

Redis: 0.5M ops/s Cachee: 827M ops/s

L1 Hit Rate (After Training)

percent (production average)

Redis: ~65% typical Cachee: 95.3%

Origin Load Reduction

percent fewer database queries

Redis: ~70% reduction Cachee: 95%+ reduction

Infrastructure Economics

Four Metrics Shift the Moment You Deploy

Memory utilization rises because Cachee is actively using it. Everything else -- server hits, infrastructure cost, response latency -- drops dramatically.

▲ GOES UP

Memory Utilization

Cachee actively uses L1 memory to store predicted data. Higher utilization = more cache hits = fewer expensive backend calls.

▼ GOES DOWN

Database / Origin Hits

95%+ of requests served from L1 memory. Your database goes from handling millions of queries to handling thousands.

▼ GOES DOWN

Infrastructure Spend

Fewer database replicas, smaller Redis clusters, less compute. Enterprises typically see 40-70% infrastructure cost reduction.

▲ GOES UP

Request Performance

P99 latency drops from tens of milliseconds to sub-millisecond. Same hardware handles orders of magnitude more throughput.

P&L Impact (100M requests/month)

Representative enterprise running on a standard AWS stack. These are the line items that change when Cachee deploys.

Line Item	Before Cachee	After Cachee	Delta
ElastiCache / Redis Cluster	$18,000/mo	$4,500/mo	−$13,500
RDS / Aurora Database	$32,000/mo	$12,000/mo	−$20,000
Compute (EC2 / ECS / Lambda)	$24,000/mo	$10,000/mo	−$14,000
Data Transfer / CDN	$11,000/mo	$4,500/mo	−$6,500
DevOps Hours (cache mgmt)	60 hrs/mo ($12,000)	4 hrs/mo ($800)	−$11,200
Cachee Platform Cost	—	$500/mo	+$500
NET MONTHLY IMPACT	$97,000/mo	$32,300/mo	−$64,700/mo

$776,400 annual savings · 129x ROI on Scale tier

Representative figures based on typical enterprise deployment. Actual results vary by infrastructure configuration, workload patterns, and scale.

Ready to See the Difference?

Deploy Cachee in under an hour. No migration. No downtime. The data your systems need is already waiting in L1 memory before they ask for it.

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1.21 nanoseconds — that's the new standard.

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