Streaming, Latency, and the Myth of the Proxy Tax
"We can't add a proxy. Latency is already tight."
Every AI infrastructure vendor hears this. It is a reasonable concern backed by a false premise: that the routing layer is the slow part.
In practice, model inference dominates end-to-end latency by orders of magnitude. A well-built gateway adds milliseconds. A smaller, routed model can subtract hundreds of milliseconds compared to a flagship on the same task.
Where latency actually comes from
Break down a typical chat completion:
- Client → gateway network. Usually single-digit to low tens of ms.
- Gateway processing. Auth, classification, routing, policy checks.
- Gateway → model provider network. Same order as step 1.
- Model inference. Hundreds of ms to many seconds, depending on model size and output length.
- Streaming delivery. First token (TTFT) then chunk cadence.
Steps 1–3 are the "proxy tax." Step 4 is the bill.
Open-source gateway benchmarks show overhead as low as 11 microseconds per request at 5,000 RPS (Bifrost, cited in Maxim.ai's 2026 gateway comparison). Production teams report ~3–4ms added latency as typical for managed gateways (TrueFoundry customer benchmarks).
Compare that to 500ms–3s+ for frontier inference on long outputs.
When routing is faster, not slower
Counterintuitive but common: routing to a smaller, faster model reduces total time even after adding a gateway hop.
| Scenario | All-flagship | Routed to fast tier |
|---|---|---|
| Simple classification | 800ms TTFT on large model | 150ms TTFT on small model + sub-15ms gateway |
| Long agent chain (5 steps) | 5 × flagship latency | 4 × fast + 1 × strong |
Autark runs inference on dedicated infrastructure optimized for throughput. The goal is making private, routed AI feel instant, not like a compliance tax.
Our internal benchmarks on the Benchmarks & ROI page measure Autark vs direct API paths. Processing overhead is consistently under 15ms p95 for the gateway path, and routed smaller models often beat direct flagship total time-to-first-token on short tasks.
Streaming works the same way
Autark supports standard OpenAI streaming: pass "stream": true, read Server-Sent Events, handle delta.content chunks exactly as you do today.
We do not buffer full responses before streaming unless a policy requires it. Chunks flow through as the backend produces them.
Test streaming in staging. It is the code path most teams forget until production.
Example (Python):
stream = client.chat.completions.create(
model="auto",
messages=[{"role": "user", "content": "Write one sentence about latency."}],
stream=True,
)
for chunk in stream:
if chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="", flush=True)
No custom SSE parser. No proprietary chunk format.
What to measure (and what not to)
Measure:
- End-to-end p50 / p95 latency per endpoint
- Time to first token (TTFT) for streaming UIs
- Error rate and timeout rate under load
- Cost per request at the same quality bar
Do not optimize prematurely:
- Gateway hop in isolation ( meaningless without model time )
- Lab prompts that do not match production token lengths
Objection handling for your team
"Our SLA is 200ms."
Is that TTFT or full completion? For full completion on 500-token outputs, model choice dominates. Routing helps.
"We can't add failure points."
Gateways add failover: if provider A is down, route to B. That reduces tail latency from retries your app would do anyway.
"We already cache responses."
Good. Routing and caching are complementary. Cache hits avoid inference entirely; routing optimizes misses.
The bottom line
The proxy tax is real. It is also tiny next to model inference. Obsessing over gateway milliseconds while sending every request to the slowest, most expensive model is backwards.
Measure end-to-end. Route by task. Stream the same way you always have.
If your routing layer is adding seconds, something is broken. If it is adding single-digit milliseconds and cutting model time in half, that is the point.