J asked for the comparison before locking in primary line. This
report documents what's actually structurally different vs
implementation-level different, and what to do about each.
Key findings:
1. Python sidecar is the single biggest architectural lever
- Rust: gateway → HTTP → Python sidecar :3200 → HTTP → Ollama
- Go: gateway → HTTP → embedd → HTTP → Ollama (no Python)
- Sidecar adds zero compute over Ollama (just pydantic + httpx)
- 63× perf gap (8,119 vs 128 RPS) driven by sidecar + cache absence
2. Process model: Rust 1 mega-binary (14.9G RSS), Go 11 daemons
- Rust: simpler ops at small scale, panic blast radius = whole system
- Go: per-daemon scale + crash isolation, more config surface
3. Code volume: Go 15,128 lines vs Rust 35,447 + 1,237 sidecar
- Go is 43% the size doing similar work
- Gap concentrated in vectord (Rust 11k lines, Go 804 — Lance + benchmarking)
4. Distillation pipeline asymmetry
- Audit/observation: BOTH sides parallel-mature
- Production: Rust-only (materializer + replay + RAG/pref export)
- Go can READ everything but can't PRODUCE evidence
5. Production validators (FillValidator/EmailValidator/'/v1/validate')
- Rust has them (1,286 lines, 12 tests each)
- Go doesn't — matrix gate covers role bleed but not structural validation
Cross-cutting abstracts to address regardless of which wins:
- Drop Python sidecar from Rust (call Ollama directly)
- Add LRU embed cache to Rust aibridge
- Port materializer + replay + validators to Go
- Pin shared JSONL schemas as canonical (both runtimes consume same spec)
- Decide on Lance backend (defer until corpus > 5M rows)
If keeping Go primary: port materializer first, validators second,
skip Lance. If keeping Rust primary: drop Python + add cache,
port chatd 5-provider dispatcher + cross-role gate from Go.
Bottom line: substrate is parallel-mature on observation; producer
side is Rust-only; performance structurally favors Go ~60× on warm
workloads; operations favors Go on isolation; production deployment
favors Rust today.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
