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Cortex Load Test Report

Production results — Cloud Run (publishable)

  • Date: 2026-07-13
  • Environment: Google Cloud Run (cortex, us-central1), live production service and revision config
  • Client: k6 via Docker, single client in Europe → us-central1 (network RTT ~145 ms floor is included in all latencies)
  • Method: per-IP rate limiter (RATE_LIMIT_DEFAULT, default 60/min) temporarily raised via env var for the run, then restored and re-verified (60 pass / overflow 429s confirmed after revert)
Scenario Load Requests Error rate p50 p95 p99 Max
API health (prod) 20 req/s × 1m 1,197 0.00% 146 ms 154 ms 164 ms 373 ms
API health (prod) 200 req/s × 1m 12,000 0.00% 146 ms 155 ms 166 ms 3.15 s

Publishable claims

  • Production sustains 200 req/s with 0% errors and no latency degradation: p95 held flat (154 ms → 155 ms) from 20 to 200 req/s — a 10× load increase.
  • 13,197 production requests, zero failures.
  • Latency is network-bound, not server-bound: ~145 ms of every measurement is Europe→us-central1 RTT; server processing is single-digit ms (confirmed by the local runs below on identical code).
  • Cloud Run autoscaling absorbed the 10× jump transparently; the single 3.15 s outlier is one instance cold start, after which p99 stayed ≤166 ms.
  • Per-IP rate limiting (60/min) enforced in production, verified empirically: 70 parallel requests → exactly 60 accepted, remainder rejected with HTTP 429.

Not claimed: a long soak. A 10-minute 200 req/s sustained-stability run was intentionally not executed against live production from this session; run it during a maintenance window if you want a "sustained for N minutes" claim.

Local results — bottleneck discovery

  • Date: 2026-07-13
  • Environment: local-dev (macOS, uvicorn single worker, Redis + Neo4j via Docker)
  • Tool: k6 (grafana/k6 Docker image), constant-arrival-rate executor
  • Backend flags: RATE_LIMIT_DEFAULT=100000/minute (raised for benchmark), RAG_PERF_HEADERS=true, RAG_SUGGESTIONS_DEFERRED=true

Local numbers are for bottleneck discovery and sizing, not public production-capacity claims (see load-tests/README.md).

Headline results

Scenario Load Requests Error rate p50 p95 p99 Max
API health 20 req/s × 1m 1,201 0.00% 3.9 ms 5.2 ms 6.7 ms 14.1 ms
API health 200 req/s × 1m 12,000 0.00% 1.9 ms 3.2 ms 4.1 ms 12.7 ms
Internal agent loop (real LLM) 1 req/s × 30s 29 0.00% 1.21 s 2.0 s 2.6 s 2.77 s

Portfolio-ready claims

  • Sustains 200 req/s with 0% errors and p95 latency of 3.2 ms on the API layer (single uvicorn worker, local).
  • Zero failed requests across 13,230 total requests in all scenarios.
  • Real agent-loop turns (LLM call included, tools unbound) complete with median 1.2 s / p95 2.0 s at 1 req/s sustained.
  • Built-in per-IP rate limiting (60/min default) verified under load: at 20 req/s the limiter correctly rejected 90% of over-quota requests with HTTP 429 before it was raised for the benchmark — a resilience feature, confirmed empirically.

Observations

  • API latency dropped from 20 → 200 req/s (p95 5.2 → 3.2 ms): connection reuse across more concurrent VUs amortizes setup; the app layer is nowhere near saturation at 200 req/s.
  • Agent-loop latency is dominated by the LLM provider call (~1.2 s median), not the framework — per k6 bottleneck hints and x-rag-perf phase headers.
  • Peak k6 VUs: 50 preallocated, ≤3 used for the agent loop; 1 dropped iteration at 1 req/s (VU briefly busy on a 2.7 s turn).

Per-scenario reports

Reproduce

# backend
RATE_LIMIT_DEFAULT=100000/minute INTERNAL_DISPATCH_SECRET=bench-secret \
INNGEST_DEV=1 INNGEST_SIGNING_KEY=local-benchmark-signing-key \
uv run uvicorn src.api.endpoints:app --host 0.0.0.0 --port 8010

# health @ 200 rps
docker run --rm -v "$PWD:/work" -w /work \
  -e BENCHMARK_BASE_URL=http://host.docker.internal:8010 \
  -e BENCHMARK_RATE=200 -e BENCHMARK_PREALLOCATED_VUS=50 -e BENCHMARK_MAX_VUS=200 \
  grafana/k6 run --summary-export reports/benchmarks/health-200rps-summary.json load-tests/health.js

# agent loop (real LLM calls)
docker run --rm -v "$PWD:/work" -w /work \
  -e BENCHMARK_BASE_URL=http://host.docker.internal:8010 \
  -e BENCHMARK_INTERNAL_SECRET=bench-secret -e BENCHMARK_BIND_TOOLS=false \
  grafana/k6 run --summary-export reports/benchmarks/internal-agent-loop-summary.json load-tests/internal_agent_loop.js

# render markdown
uv run python scripts/render_k6_report.py --summary-json <summary.json> --scenario <name> \
  --environment local-dev --target "<rate x duration>" --output <report.md>

Next steps for stronger claims

  1. Re-run against the Cloud Run production deployment — done, see production section above.
  2. Authenticated agent_chat.js run (needs BENCHMARK_BEARER_TOKEN + BENCHMARK_AGENT_ID) to quantify the full RAG chat path under load.
  3. 10-minute soak at 200 req/s against production (run during a maintenance window) to claim sustained stability, and a ramp test to find the actual saturation point.