Benchmarking t3code's Streaming AI Completions: TTFT, Token Throughput, and Connection Stability Under Load

When evaluating real-time collaborative code editors with AI streaming, the metrics that matter aren't just "does it work" but "does it hold up under realistic concurrent load." I spent the past week stress-testing pingdotgg/t3code's streaming completion architecture to answer: at what user count does the SSE connection model start to degrade?

Test Topology

Harness : k6 with custom WebSocket + SSE scenario

Environment : AWS t3.xlarge (4 vCPU, 16GB RAM)

Baseline LLM Gateway : B-Lost relay at https://b-lost.com/v1 (OpenAI-compatible endpoint)

Load Profile : Ramp from 10 → 50 → 100 concurrent users over 5 minutes, each triggering streaming completion requests every 8-12 seconds

Measurement Focus :

Time-to-first-token (TTFT)

Sustained token throughput (tokens/sec per connection)

SSE connection drop rate

Memory overhead on the Next.js server

Observed Metrics

Concurrent Users Median TTFT (ms)…