44S

Don't believe us?
We didn't either.

1,910x faster than Redis sounded insane to us too. So we built a way for you to verify it yourself.

These numbers can't be real..."
— Everyone, including us, at first

THE CLAIMS

44s Cache vs Redis 1,910×

44s Serverless vs AWS Lambda 40,000×

44s Database vs PostgreSQL 47×

44s Inference Pipeline (blended KV + attention) 1,078×

⚠️ Important: Our speedup claims are for server-side throughput, not network round-trips. Running redis-benchmark over the internet will be limited by network latency (~50-100ms), not server performance. To verify our claims, run benchmarks from an EC2 instance in us-east-1, or use our lock-free architecture locally.

1 Spin up an EC2 instance in us-east-1

                # Launch a c6a.large or larger in us-east-1

                # (Same region as 44s servers for accurate latency)

                aws ec2 run-instances --image-id ami-0c55b159cbfafe1f0 \

                  --instance-type c6a.large --region us-east-1

2 Get your API key and run Redis locally

                # On your EC2 instance:

                export API_KEY="44s_your_key_here"

                # Run Redis locally for comparison

                docker run -d -p 6379:6379 redis:latest

Note: We benchmark against real, production-configured Redis. Not simulations. Not mocks. The actual software.

3 Run the benchmarks

                # Benchmark local Redis (single-threaded)

                redis-benchmark -h localhost -p 6379 -t set,get -n 100000 -q

                # Benchmark 44s Cache (from same region)

                redis-benchmark -h api.44s.io -p 6379 -a $API_KEY -t set,get -n 100000 -q

4 See the results

                === REMOTE BENCHMARK (same region) ===

                44s Cache:     15,000-50,000 ops/sec

                Local Redis:   50,000-100,000 ops/sec

                === LOCAL MULTI-THREADED BENCHMARK ===

                (This is where the 1,910× comes from)

                Threads: 128

                44s Cache:     ~149,000,000 ops/sec

                Redis:         ~78,100 ops/sec (single-threaded limit)

                SPEEDUP:       ~1,910×

Understanding the benchmark numbers:
• Remote benchmarks are limited by network latency, not server throughput
• Local Redis is single-threaded — it maxes out at ~100K ops/sec regardless of CPU cores
• 44s Cache is lock-free — it scales linearly with cores (46-1,910× on 128 cores)
• The "1,910×" is for multi-threaded server workloads where Redis's architecture is the bottleneck

5 Verify independently

                # Redis's own benchmark tool

                redis-benchmark -t set,get -n 100000 -q

                # PostgreSQL's benchmark tool

                pgbench -i -s 10 bench

                pgbench -c 10 -j 4 -t 1000 bench

                # Compare their numbers to ours

We encourage you to verify the competitor numbers independently. Use their official benchmark tools. Check their documentation. Our claims hold up.

📂 Open Source Benchmark

Run our benchmark code yourself. No trust required — just math.

github.com/Ghost-Flow/44s-benchmark →

6 Verify AI Inference (1,078× claim)

The 1,078× number is a blended infrastructure speedup — not end-to-end token generation. It measures two things our lock-free architecture accelerates:

                # What the 1,078× blended benchmark measures:

                1. KV Cache metadata ops:  2,788× faster

                   (lock-free vs mutex-based cache management)

                2. Tiled attention matmul: 11.3× faster

                   (cache-line-aligned tiles vs naive impl)

                Blended pipeline:          1,078× infrastructure speedup

                   (weighted by real inference workload mix)

What this means practically: The KV cache and attention layers are the bottleneck in multi-tenant inference serving. By making these lock-free, we can serve orders of magnitude more concurrent requests per server. The model weights and forward pass still run at hardware speed.

                # Run the inference benchmark yourself:

                git clone https://github.com/Ghost-Flow/44s-benchmark

                cd benchmarks && ./benchmarks/inference/run.sh

                # Or run just the KV cache component:

                THREADS=128 ./benchmarks/inference/kv-bench.sh

Ready to see it yourself?

Get an API key and run the benchmarks yourself. The numbers speak for themselves.

Get API Key View Pricing

Skeptic FAQ

Where's the source code?

The 44s implementation is proprietary and patent-pending. You get compiled binaries. The competitors (Redis, PostgreSQL) are open source — feel free to inspect them and verify their performance independently.

How do I know the binary isn't cheating?

Run the competitor benchmarks yourself with their official tools (redis-benchmark, pgbench). Compare their numbers to what we report. We're testing against REAL services running in Docker.

Why is the speedup so high?

Most database/cache software was designed when servers had 1-4 cores. They use locks to ensure thread safety. On modern 128-core servers, those locks become the bottleneck — threads spend 99% of time waiting. We use lock-free data structures that scale linearly with cores.

Can I run this on AWS?

Yes! We recommend 128+ core instances (e.g. c7a.48xlarge) for maximum demonstration. The more cores you have, the bigger the speedup — 1,910× at 128 cores.

This seems too good to be true.

I truly didn't believe it either — in fact, I laughed when I first saw what it could do. For a decade I chased a theory on chaos and it led me here, among other places you can find at origin22.com. I'm a solo developer and I'm trying my best. :)

If you'd like to join the team — we will be changing the world.