Redis vs In-Process
L1 Cache
Redis adds serialization, TCP, TLS, and deserialization to every operation.
Cachee L1 is a pointer dereference. 32,258x faster.
Two Paths. Same Data.
Every Redis read crosses six boundaries. Every Cachee L1 read crosses zero.
Every Metric. Side by Side.
| Metric | Redis / ElastiCache | Cachee L1 |
|---|---|---|
| Read latency | 310us - 1ms | 31ns |
| Write latency | 350us - 1.2ms | 45ns |
| Serialization | Required (JSON/msgpack) | None (native memory) |
| Network hop | Required | None |
| TLS overhead | ~50us per connection | None |
| Scales with payload | Yes (O(n) serialization) | No (constant 31ns) |
| Thread blocking | Yes (async await) | No (sync, zero-wait) |
| Failure blast radius | All services lose cache | Per-process only |
| Throughput | ~100K ops/sec/node | 32M+ ops/sec |
| Redis commands | 140+ | 140+ (compatible) |
| PQ attestation | None | ML-DSA + FALCON + SLH-DSA |
| Eviction policy | LRU / LFU | CacheeLFU (adaptive) |
Redis is not slow. It is architecturally constrained. Serialization and TCP are the tax. Cachee L1 eliminates the tax entirely.
Redis Gets Slower. Cachee Doesn't.
Redis latency grows linearly with payload size because every byte must be serialized, transmitted, and deserialized. Cachee L1 stays at 31ns regardless of payload size — it is a pointer dereference.
Payload Size vs Latency
Redis latency is O(n) in payload size. Cachee L1 is O(1). The gap widens with every byte. At 1MB payloads, Redis is 403,226x slower.
When to Use What
Redis is a great tool. Cachee is a different tool. Here is when each one wins.
At 1 Billion Ops/Month — the Math
Redis infrastructure costs scale with ops. Cachee L1 runs inside your existing compute. No new infrastructure. No new network hops. No new failure domain.
| Line Item | ElastiCache (Redis) | Cachee |
|---|---|---|
| Instance / License | $480/mo (r7g.xlarge) | $499/mo (flat) |
| Network transfer | $50-200/mo (cross-AZ) | $0 (in-process) |
| Serialization CPU | Hidden (your app pays) | $0 (native memory) |
| Monitoring / Alarms | $30-80/mo (CloudWatch) | Built-in metrics |
| Ops overhead | Patching, failover, scaling | Zero infra |
| Compute recapture | None | Freed CPU from no serialization |
| Effective Total | $560-760/mo + hidden costs | $499/mo, all-in |
The biggest cost isn't the Redis bill. It's the CPU your app spends serializing and deserializing on every operation. Cachee eliminates that entirely.
Add Cachee L1 in Front of Redis — Zero Risk
You don't rip out Redis. You add Cachee in front of it. One function call wraps your existing Redis client. Reads hit L1 first. Misses fall through to Redis. Writes go to both.
Before: Direct Redis
// Every read hits Redis over TCP
const session = await redis.get(`session:${userId}`); // 310us - 1ms
const profile = await redis.hgetall(`user:${userId}`); // 350us - 1.2ms
const flags = await redis.get(`flags:${tenantId}`); // 310us - 1ms
// Total: ~1ms - 3.2ms for three reads
After: Cachee L1 + Redis Fallback
// L1 hit: 31ns. L1 miss: falls through to Redis.
const cache = cachee.wrap(redis); // one line to add L1
const session = await cache.get(`session:${userId}`); // 31ns (L1 hit)
const profile = await cache.hgetall(`user:${userId}`); // 31ns (L1 hit)
const flags = await cache.get(`flags:${tenantId}`); // 31ns (L1 hit)
// Total: ~93ns for three reads (99% of the time)
Shadow Mode: Run Both, Compare
// Shadow mode: reads from both, logs discrepancies, serves from L1
const cache = cachee.wrap(redis, {
mode: 'shadow', // read from both, serve from L1
logDiscrepancy: true, // alert if L1 != Redis
ttl: '5m', // L1 entries expire after 5 min
maxMemory: '256mb', // cap L1 memory usage
});
// Zero risk. Full visibility. Migrate with confidence.
Redis vs Cachee — Side by Side
Run it yourself: brew install cachee && cachee bench --compare redis
Why Redis Is Architecturally Slower
Redis is fast for a networked datastore. It is slow for a cache. The difference is architectural, not implementation quality. Redis cannot be faster because of what it is.
The six costs Redis cannot eliminate
Common Questions
Is Redis fast enough for real-time systems?
Redis read latency is 310us to 1ms in production. For systems that require sub-microsecond response — trading engines, biometric authentication, gaming tick loops — Redis is too slow. The bottleneck is architectural: serialization, TCP, and TLS add latency that cannot be optimized away. An in-process L1 cache returns in 31 nanoseconds.
What is an in-process L1 cache?
An in-process L1 cache lives inside your application's memory space. Reads are pointer dereferences — 31 nanoseconds, no serialization, no network hop. The data is already in native memory format. Cachee is an in-process L1 cache that supports 140+ Redis-compatible commands, CacheeLFU eviction, and post-quantum attestation of every entry.
Can I use Cachee with Redis?
Yes. The recommended architecture is Cachee L1 in front of Redis. Cachee handles 99% of reads at 31 nanoseconds. Cache misses fall through to Redis. You get sub-microsecond reads for hot data and Redis durability for cold data. Migration takes one line: cachee.wrap(redis).
Why is Cachee 10,000x faster than Redis?
Redis requires six steps per read: serialize, TCP connect, TLS handshake, network transmit, process, deserialize. Each step adds latency. Cachee L1 requires one step: a pointer dereference into local memory. No serialization (data is already native structs). No network (same process). No TLS (same memory space). The result: 31ns vs 1ms.
Does Cachee support Redis commands?
Yes. Cachee supports 140+ Redis-compatible commands: GET, SET, MGET, HGET, HSET, LPUSH, SADD, ZADD, EXPIRE, TTL, and more. The command interface is identical. You can migrate by changing the connection string. The difference is architectural: Redis executes commands over TCP in a separate process, while Cachee executes them as in-process function calls at 31 nanoseconds.
31 nanoseconds. 140+ Redis commands. Zero infrastructure.
Put Cachee L1 in front of Redis. 99% of reads never touch the network.
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