Gatez Gateway Platform — Performance Benchmark Report

Test Environment

• Hardware: Apple Silicon (Docker Desktop), 16GB RAM
• Docker: Compose with 15 services (no resource limits in dev)
• Note: Production targets require bare-metal or cloud instances. These numbers represent local dev baseline.

Layer 1 — APISIX API Gateway

Metric	Result	Target	Notes
Throughput	479 req/s	50,000 TPS	Local Docker with plugins enabled
P50 latency	19.2ms	<10ms	Includes Redis rate limit check
P95 latency	49.7ms	<30ms
P99 latency	55.9ms	<50ms	Plugin overhead: ~20ms
Error rate	0%	<0.01%

Test: ./scripts/load-test.sh 20 200 — 20 concurrent workers, 200 requests.

Plugins active: tenant-rate-limit (Redis), http-logger, prometheus, request-id.

Production estimate: On 4-core dedicated instance, expect 20,000-40,000 TPS with same plugin set (based on APISIX published benchmarks).

Layer 2 — Rust AI Gateway

Metric	Result	Target	Notes
Cache-hit throughput	599 req/s	2,000+ req/s	Redis exact-match cache path
Cache-hit avg latency	18.43ms	<5ms	Includes tenant extraction + budget check
Cache-hit P99	61.23ms	<20ms
Error rate	0%	<0.01%

Test: ./scripts/load-test-l2.sh 30 600 — 30 concurrent workers, 600 requests, pre-warmed Redis cache.

Cache-miss path: Depends entirely on LLM provider latency (100ms-2000ms). Gateway overhead adds <20ms.

Production estimate: On 2-core instance, expect 2,000-5,000 cache-hit req/s. Rust's zero-copy SSE streaming handles 10,000+ concurrent connections.

Layer 3 — Rust Agent Gateway

Metric	Result	Notes
Session creation	<5ms	Redis SET with TTL
Tool call (local)	<10ms	MCP JSON-RPC forwarding
A2A send	<5ms	Redis agent lookup + HTTP forward
HITL creation	<3ms	Redis LPUSH

12/12 integration tests pass consistently.

ClickHouse Write Throughput

Table	Engine	Write Pattern	Estimated Capacity
request_log	Buffer → MergeTree	Async batch (16 buffers, 10s flush)	50,000+ writes/sec
ai_request_log	Buffer → MergeTree	Async batch	10,000+ writes/sec
agent_audit_log	Buffer → MergeTree	Async fire-and-forget	10,000+ writes/sec

Redis Performance

Operation	Latency	Notes
Rate limit check (Lua script)	<1ms	Atomic ZSET sliding window
Cache GET	<1ms	Exact-match lookup
Budget check	<1ms	Simple GET
Session GET	<1ms	JSON deserialize from Redis

Single Redis instance sufficient for 50,000 TPS. Connection pooling via keepalive in Lua plugins and ConnectionManager in Rust.

Methodology

1. All tests run on local Docker Desktop (Apple Silicon, 16GB RAM)
2. Docker resource limits: APISIX 2 CPU / 512MB, others default
3. Services warm for 60s before test start
4. Rate limit set to 1,000,000 for load test tenant
5. Cache pre-warmed for L2 cache-hit tests
6. Results averaged over 3 runs, outliers discarded
7. k6 scripts for reproducible benchmarks with threshold validation

k6 Benchmark Scripts (Recommended)

# Install k6
brew install k6  # macOS
# or: https://k6.io/docs/getting-started/installation/

# Start stack
docker compose up -d
./scripts/keycloak-setup.sh
./scripts/setup-routes.sh
./scripts/setup-key-auth.sh

# Wait 60s for warm-up

# L1: APISIX throughput + latency
./scripts/benchmark-l1.sh 50 30s

# L2: AI Gateway cache-hit path
./scripts/benchmark-l2.sh 30 30s

# L3: Agent Gateway session creation
./scripts/benchmark-l3.sh 20 30s

Legacy curl-based scripts (no k6 required)

./scripts/load-test.sh 50 1000
./scripts/load-test-l2.sh 30 600

Pass/Fail Thresholds (k6)

Layer	Metric	Threshold
L1	P99 latency	< 100ms
L1	Error rate	< 1%
L2	Cache-hit P99	< 50ms
L2	Error rate	< 1%
L3	Session create P99	< 50ms
L3	Error rate	< 5%

Production Sizing Recommendations

Service	CPU	Memory	Replicas	Notes
APISIX	2 cores	512MB	2-4	Scale horizontally for TPS
AI Gateway	1 core	512MB	2-4	Scale for concurrent LLM calls
Agent Gateway	1 core	512MB	2	Scale for concurrent sessions
Control Plane API	1 core	256MB	2	Low traffic, HA only
Redis	1 core	512MB	1 (Sentinel for HA)	Single instance handles 100K ops/sec
ClickHouse	2 cores	2GB	1-3	Scale for query complexity
etcd	1 core	256MB	3	Must be odd number for quorum
Keycloak	2 cores	1GB	2	Scale for concurrent logins