We benchmark in public.
You reproduce locally.
No marketing-deck numbers. The harness, the upstream mock, the load profile, and the raw output are in the bench repo. Run it on your hardware. Compare engines side-by-side. Read what each one actually does under sustained load.
Boring, on purpose.
The point of the harness is to be uninteresting. Same hardware, same upstream, same load profile, same scoring rule for every engine in the comparison. The interesting part is what the numbers say once you remove every reason for them to be wrong.
- Hardware. One commodity cloud instance per engine, identical instance type and region. Same kernel, same network conditions, same upstream mock.
- Workload. AI non-streaming, OpenAI chat-completions shape, deterministic mock provider with a fixed response size. The traffic shape every engine in the category claims to handle.
- Loadgen. A separate instance running a sustained-RPS harness for 90 seconds per scenario, after a 30-second warmup. Connections held open; no per-request reconnect noise.
- Scoring. RPS is the highest sustained level where p99 stayed under a fixed latency budget for the whole run. p50 and p99 are reported at that level.
- Reproduction. Terraform for the cloud environment is in the bench repo. A docker-compose for one-command local reproduction is the next deliverable.
- Honesty. If we find a configuration that hurts a competitor, we file an issue against their repo and re-run with the fix. The published number is the engine's honest ceiling.
Loadgen, harness, plot scripts, raw output. Open source on GitHub. The terraform and docker-compose live here.
Open the repo →Stand up the harness on your own hardware, run the full campaign matrix, plot the output. Same instance type and load profile we publish against.
Read the guide →The post that walks through the methodology, the results, and the design choices that produce the gap.
Read the post →The category splits into three groups.
We are not going to tell you a single hero number on a page you cannot verify. The shape of the result, across every campaign we have run, is consistent and worth describing in plain terms.
Bottom out at low single-digit thousand RPS. Tail latency goes vertical past that. Useful for prototypes and small teams; not the engine your production traffic should sit on.
Middle of the pack. OpenResty, Node, JVM-based runtimes. Solid throughput, higher per-request overhead. Engineered for general gateway traffic; AI features add a constant cost.
An order of magnitude past Group 1, multiples ahead of Group 2 on the AI workload. Sub-millisecond p99 added latency at the published ceiling. Same binary, no separate AI variant.
We will keep updating the harness, expanding the workload set (streaming, semantic-cache hit rates, cold start), and publishing the raw output. If you want a number, run the harness; if you want the long version, read the post.
Want to talk
before you install?
The gateway is on GitHub under Apache 2.0 and you can install it right now, so nothing here is gated behind a sales call. Use this form for deployment questions, migration scoping, or anything you would rather not file as a public issue.