APAC LLM Inference and Observability Guide 2026: Lepton AI, Coroot, and Braintrust

APAC AI Infrastructure: Serverless Inference, eBPF Observability, and LLM Experiment Tracking

APAC AI teams operating in production face three infrastructure gaps that standard tools do not address: deploying open-source LLMs as production APIs without GPU cluster management, understanding microservice behavior without instrumenting every service, and systematically tracking LLM experiment quality across prompt versions and model changes. This guide covers three tools that fill these APAC operational gaps.

Lepton AI — serverless GPU platform for deploying open-source LLMs and custom APAC models as production API endpoints with sub-second cold starts.

Coroot — open-source eBPF-based observability that automatically discovers APAC service topology and detects performance anomalies without code instrumentation.

Braintrust — LLM experiment tracking and prompt management platform for APAC AI teams systematically improving LLM application quality.

APAC Tool Selection Framework

APAC Scenario                         → Tool          → Why

Open-source LLM as production API     → Lepton AI     Serverless GPU; no K8s;
(no infrastructure team)              →               pay-per-GPU-second

Sporadic APAC LLM inference jobs      → Modal Labs     Task-based; best for
(not persistent API)                  →               batch + scheduled runs

Always-on APAC LLM service            → Anyscale      Ray Serve; persistent;
(guaranteed latency SLA)              →               autoscale + HA replicas

APAC K8s service mesh visibility      → Coroot         eBPF; no code changes;
(legacy + uninstrumented services)    →               auto service map

Full APAC distributed tracing         → Jaeger/OTel    Span-level; requires
(span-level debugging)                →               instrumentation

APAC LLM experiment comparison        → Braintrust     Prompt versioning +
(prompt iteration workflow)           →               multi-scorer evaluation

APAC LLM production tracing           → Langfuse       Open-source; self-hosted;
(data sovereignty required)           →               session + cost tracking

Lepton AI: APAC Serverless LLM Inference

Lepton AI APAC deployment

# APAC: Lepton AI — deploy open-source LLM as serverless API endpoint

import leptonai
from leptonai.photon import Photon

# APAC: Define inference endpoint as Python class
class ApacQwenEndpoint(Photon):
    """APAC: Qwen 2.5 7B inference endpoint for CJK language tasks."""

    # APAC: Lepton downloads and caches model on first cold start
    requirement_dependency = ["vllm>=0.4.0", "transformers>=4.40.0"]

    def init(self):
        from vllm import LLM, SamplingParams
        # APAC: Qwen for Chinese/Japanese/Korean + English multilingual tasks
        self.llm = LLM(
            model="Qwen/Qwen2.5-7B-Instruct",
            dtype="float16",
            gpu_memory_utilization=0.90,
        )
        self.params = SamplingParams(temperature=0.7, max_tokens=512)

    @Photon.handler
    def apac_generate(self, prompt: str, system: str = "") -> str:
        """Generate APAC LLM response for given prompt."""
        apac_messages = []
        if system:
            apac_messages.append({"role": "system", "content": system})
        apac_messages.append({"role": "user", "content": prompt})

        apac_formatted = self.llm.tokenizer.apply_chat_template(
            apac_messages, tokenize=False, add_generation_prompt=True
        )
        apac_outputs = self.llm.generate([apac_formatted], self.params)
        return apac_outputs[0].outputs[0].text

# APAC: Deploy to Lepton AI cloud (single command)
# lepton photon run -n apac-qwen-endpoint -e ApacQwenEndpoint --resource-shape gpu.a10

# APAC: Call deployed APAC endpoint
import requests

apac_response = requests.post(
    "https://apac-qwen-endpoint.lepton.ai/apac_generate",
    headers={"Authorization": f"Bearer {LEPTON_API_KEY}"},
    json={
        "prompt": "新加坡MAS对AI治理有哪些要求？",  # Chinese: "MAS AI governance requirements?"
        "system": "You are an APAC regulatory compliance expert.",
    }
)
print(apac_response.json())
# APAC: Response in Chinese — Qwen handles CJK natively without translation

Lepton AI APAC cost comparison

APAC Inference Cost Comparison (Qwen 2.5 7B, 1M tokens/day):

Provider         GPU Type    Cost/Day    Cold Start    Data Sovereignty
Lepton AI        A10G        ~$8-12      <1 second     APAC regions available
Modal Labs       A10G        ~$10-15     2-3 seconds   US/EU only
Anyscale         A10G        ~$15-20     On-demand     APAC regions available
Self-hosted K8s  A10G        ~$20-25     N/A (always)  Full control
OpenAI API       N/A         ~$5-8       N/A           US data centers

APAC Decision:
  If APAC data sovereignty matters → Self-hosted K8s or Lepton (APAC region)
  If cost-sensitivity matters → OpenAI API or Lepton (serverless, variable)
  If latency SLA matters → Self-hosted K8s (always-on, no cold start)
  If ML experimentation pace matters → Lepton or Modal (fast deployment)

Coroot: APAC eBPF Service Observability

Coroot APAC Kubernetes deployment

# APAC: Coroot — deploy on existing APAC Kubernetes cluster
# No code changes required — eBPF captures data at kernel level

# APAC: Install Coroot via Helm
helm repo add coroot https://coroot.github.io/helm-charts
helm repo update

helm install coroot coroot/coroot \
  --namespace apac-monitoring --create-namespace \
  --set corootNode.enabled=true \
  --set prometheus.enabled=true  # APAC: or use existing Prometheus

# APAC: Coroot agent (coroot-node-agent) runs as DaemonSet on every node
# Uses eBPF to capture:
#   - Network connections between APAC pods
#   - HTTP/gRPC request latency and error rates at network layer
#   - CPU/memory/disk/network resource utilization per APAC pod
#   - Database query performance (MySQL, PostgreSQL, Redis) via eBPF

# APAC: No changes required to:
#   - APAC application code (no OpenTelemetry SDK)
#   - APAC Kubernetes manifests (no sidecar injection)
#   - APAC service configuration (no tracing headers)

Coroot APAC anomaly detection

APAC: Coroot automatic anomaly detection example

Service: apac-payment-service
Anomaly detected: 2026-05-17 14:32 SGT

Root cause surfaced by Coroot:
  → apac-payment-service → apac-database (PostgreSQL)
  → Query latency: normal 8ms → current 340ms (42x degradation)
  → CPU utilization on db-node-02: 95% (baseline: 30%)

Correlated signals:
  → apac-database disk IO wait: 87% (baseline: 5%)
  → apac-order-service error rate: 12% (baseline: 0.1%)
  → apac-payment-service p99 latency: 4200ms (baseline: 180ms)

Probable cause: APAC database disk IO saturation
  → Coroot shows this is a new pattern starting 14:28 SGT
  → Correlates with apac-analytics-service batch job started 14:25 SGT

APAC team action: throttle apac-analytics batch job or move to off-peak
  → Identified in 8 minutes vs typical 45-minute APAC incident investigation

Braintrust: APAC LLM Experiment Tracking

Braintrust APAC SDK instrumentation

# APAC: Braintrust — LLM experiment logging with lightweight SDK

import braintrust
from braintrust import init_logger, current_span

# APAC: Initialize Braintrust logger for production monitoring
braintrust.init(api_key=os.environ["BRAINTRUST_API_KEY"])

# APAC: Log LLM experiments — wraps any LLM call
@braintrust.traced
def apac_rag_answer(query: str) -> str:
    """APAC RAG pipeline with automatic Braintrust experiment logging."""
    # APAC: Trace retrieval step
    with current_span().start_span("apac_retrieve") as span:
        apac_docs = vector_search(query, top_k=5)
        span.log(input=query, output=[d["text"][:100] for d in apac_docs])

    # APAC: Trace generation step
    with current_span().start_span("apac_generate") as span:
        apac_context = "\n".join([d["text"] for d in apac_docs])
        apac_response = call_llm(
            system=f"Answer based on context: {apac_context}",
            user=query,
        )
        span.log(input={"query": query, "context": apac_context}, output=apac_response)

    return apac_response

# APAC: Run experiment — all calls logged to Braintrust
with braintrust.init_experiment("APAC RAG v2.3 — Qwen context"):
    for apac_query in apac_test_queries:
        apac_answer = apac_rag_answer(apac_query)

Braintrust APAC prompt version management

# APAC: Braintrust — manage APAC system prompts as versioned artifacts

# APAC: Fetch live prompt from Braintrust (no code deployment needed to update)
apac_prompt = braintrust.load_prompt(
    project="APAC Enterprise Chatbot",
    slug="apac-system-prompt",
)

# APAC: Use prompt in LLM call — version tracked automatically
apac_response = call_llm(
    system=apac_prompt.build(
        # APAC: Template variables filled at runtime
        market="Singapore",
        regulation="MAS FEAT",
        language="English",
    )["system"],
    user=user_query,
)

# APAC: Non-engineer APAC stakeholders iterate prompt in Braintrust UI:
#   1. Edit system prompt in Braintrust playground
#   2. Test against APAC golden test case dataset
#   3. Compare output scores against current production version
#   4. Promote to production — zero code deployment required
#   5. APAC engineering team sees version change in experiment history

Related APAC AI Infrastructure Resources

For the LLM observability platforms (Arize Phoenix, AgentOps) that complement Braintrust's experiment tracking with production trace visualization, APAC session replay, and real-time cost attribution per LLM call in production traffic, see the APAC LLM observability guide.

For the serverless AI compute platforms (Modal Labs, E2B) that complement Lepton AI for APAC workloads beyond persistent LLM endpoints — batch inference jobs, AI agent code execution sandboxes, and scheduled ML pipeline tasks — see the APAC serverless AI compute guide.

For the Kubernetes observability tools (eBPF-based Hubble, Pixie) that operate at the same kernel level as Coroot but focus specifically on APAC network security policy enforcement and pod-to-pod traffic visualization in Cilium-managed clusters, see the APAC eBPF observability guide.