LLM API latency benchmark: US vs EU vs APAC

If your users are spread across the globe, the region you call for LLM inference decides whether your chat UI feels snappy or broken. This head-to-head looks at LLM API latency US EU APAC deployments of equivalent model sizes, and the tradeoffs you actually hit when shipping to production.

Methodology: how we profiled regional endpoints

We placed lightweight clients in three cloud regions—us-east-1, eu-west-1, ap-southeast-1—and issued identical chat completion requests to region-local and cross-region endpoints serving the same model family. The goal was to isolate network-induced latency from model compute, not to rank providers.

We measured time-to-first-token (TTFT) because that is the delay a user perceives as “typing lag”. Throughput was captured as tokens/sec after the first token. All calls used streaming to avoid waiting for full completion.

import time, requests

def measure_ttft(base_url, api_key, prompt, model="mistral-7b"):
    start = time.perf_counter()
    resp = requests.post(
        f"{base_url}/v1/chat/completions",
        headers={"Authorization": f"Bearer {api_key}"},
        json={"model": model, "messages": [{"role": "user", "content": prompt}], "stream": True},
        stream=True,
    )
    for line in resp.iter_lines():
        if line and b"content" in line:
            return time.perf_counter() - start
    return None

Each client ran 200 requests at concurrency 1 against three endpoint locations. We discarded the first 10 as warmup. The numbers below are ranges observed across multiple OpenAI-compatible servers; they are defensible as order-of-magnitude, not vendor benchmarks.

Capabilities: which models live where

Model availability is the first regional split. US regions get immediate access to new proprietary weights (GPT-4o, Claude 3.5) and the widest open-weight selection on high-end GPUs. EU endpoints often lag by days or weeks—sovereign clouds must clear compliance and allocate limited H100s. APAC deployments are the sparsest: many global providers only mirror smaller open models there, while local clouds push their own (Qwen, Yi, GLM).

Concrete gaps

• US: every model, every size, fastest fleet turnover.
• EU: Llama-3, Mixtral, some Claude via Ireland; GPT-4 class often available but with stricter data handling.
• APAC: Qwen-72B, Yi-34B, local Llama ports; Western closed models sparse or via global proxy.

If your feature depends on a specific frontier model, assume US-only until proven otherwise.

Price and cost model

Token prices are frequently region-parity from the same provider, but the hidden cost is egress and GPU spot variance. US-east has the cheapest compute due to saturated accelerator supply. EU carries a 5–15% premium from energy and compliance overhead. APAC (Tokyo, Singapore) is often 10–20% above US for equivalent instance types, though serving local users cuts downstream CDN egress.

A per-token metering layer simplifies accounting. n4n.ai, for example, exposes a single OpenAI-compatible endpoint with per-token usage metering across 240+ models, so you see regional cost as line items rather than reconciling separate cloud bills. But the underlying regional price delta remains.

Self-hosted? Budget for cross-region bandwidth: a US API serving APAC users still pays AWS inter-region transfer (~$0.02/GB), which silently eats margin on high-volume streams.

Latency and throughput

Physics sets the floor. Fiber round-trip from US to EU is ~80–100ms; US to APAC ~160–200ms. Add TLS, LB, and prefill, and you get stable gaps.

From a US client to a 70B-class model at low concurrency:

• US endpoint: 300–500ms p50 TTFT
• EU endpoint: +80–150ms
• APAC endpoint: +160–250ms

EU client local: 350–550ms. APAC client local: 450–700ms. Cross-region penalties are symmetric-ish but APAC↔EU is worst (~220ms).

Throughput (inter-token latency) is less region-sensitive—once the stream starts, bandwidth dominates. We saw 40–90 tok/s for 7B models and 20–40 tok/s for 70B regardless of region, provided the provider wasn’t saturated.

Why TTFT matters more than throughput

Users abort if nothing appears in <800ms. A 200ms cross-region penalty can push you past that cliff. Optimize by streaming and rendering partial deltas immediately.

Ergonomics: selecting a region

Most providers expose region via base URL. Azure OpenAI requires a per-region resource:

curl https://my-eu-resource.openai.azure.com/openai/deployments/mistral/chat/completions?api-version=2024-02-15-preview \
  -H "api-key: $KEY" -d '{"messages":[{"role":"user","content":"hi"}]}'

OpenAI’s own API is global—no pinning. Inference gateways may accept a routing directive header so the client expresses preference without swapping URLs:

curl https://gateway.example.com/v1/chat/completions \
  -H "Authorization: Bearer $KEY" \
  -H "x-route-region: apac" \
  -d '{"model":"mistral-7b","messages":[{"role":"user","content":"hi"}]}'

In the OpenAI SDK, just swap base_url:

from openai import OpenAI
eu_client = OpenAI(base_url="https://eu-west-1.proxy.dev/v1", api_key="x")

If you use a gateway that honors client routing directives and forwards provider cache-control hints, you can keep one client instance and toggle region per request. That is the cleanest ergonomic win for multi-region apps.

Ecosystem and limits

US has the deepest ecosystem: every SDK, every eval tool, every fine-tune pipeline. EU ecosystem is solid but some US-only beta features (assistants v2, certain vision modes) slip. APAC ecosystem is fragmented—Alibaba and Tencent have OpenAI-incompatible auth, forcing shim layers.

Rate limits scale with region capacity:

• US: highest TPM/RPM tiers, fastest quota increases.
• EU: medium; sometimes lower default limits due to smaller fleets.
• APAC: often throttled during local peak (evening JST), and quota increases need local business entities.

Compliance is decisive: EU gives GDPR residency; US gives none; APAC varies (Singapore PDPA, Japan APPI). If data cannot leave a jurisdiction, your region choice is made for you.

Head-to-head comparison

Which to choose

EU-facing regulated app: Use EU endpoint. Accept ~100ms higher TTFT vs US for local users, but avoid transatlantic transfer. Deploy open-weight models if proprietary isn’t cleared. Set base_url to EU proxy and cache prefill via provider cache-control.

Global consumer app, Americas-heavy: US baseline. Route APAC users to an APAC region only if you can mirror models; otherwise accept cross-pacific delay and mask it with optimistic UI and streaming. Don’t pay EU premium unnecessarily.

APAC-local enterprise with low-latency mandate: Pick APAC region with Qwen/Yi or a gateway with fallback. Forcing US-only closed models will blow your latency budget unless you have edge caching.

Cost-sensitive batch processing: US-east only. Latency irrelevant; use cheapest tokens and highest limits. No reason to pay EU/APAC uplift for offline summarization.

Multi-region with automatic failover: Put a routing layer that honors client region and degrades when a provider is rate-limited. The LLM API latency US EU APAC gaps above are the baseline you can’t compress; fallback adds resilience when a zone degrades.

Measure from your own client locations before committing, but the regional splits are stable enough to design around. The LLM API latency US EU APAC differences are not bugs—they are geography, and your architecture should respect them.