APAC AI Execution Infrastructure Guide 2026: E2B, Baseten, and Cerebrium

APAC AI Execution Infrastructure: Sandboxes, Model APIs, and Serverless GPU

APAC AI engineering teams face three distinct execution infrastructure challenges as they move from prototypes to production: running LLM-generated code safely without exposing production systems, deploying trained ML models as reliable inference APIs without managing GPU servers, and accessing flexible GPU compute for variable AI workloads without long-term instance commitments. This guide covers three infrastructure platforms addressing each challenge.

E2B — secure cloud sandboxes for AI agent code execution, providing isolated Python and JavaScript environments where LLM-generated code runs safely without production infrastructure risk.

Baseten — ML model inference deployment platform converting PyTorch and HuggingFace models to auto-scaling production APIs, managing GPU infrastructure for APAC engineering teams.

Cerebrium — serverless GPU cloud with sub-second cold starts for custom Python AI workloads, charging per GPU-second for APAC teams with variable inference and training compute needs.

APAC AI Infrastructure Decision Framework

APAC Requirement                       → Tool        → Why

Run LLM-generated code safely          → E2B          Isolated sandboxes; 150ms startup;
(AI agent code interpreter)            →              safe execution without infra risk

Deploy custom ML model as API          → Baseten      Truss packaging; managed GPU;
(PyTorch/HF model → production API)   →              auto-scaling; TensorRT optimization

Flexible GPU for burst workloads       → Cerebrium    Serverless; sub-second cold start;
(variable inference + training)        →              H100/A100; per-GPU-second billing

High-volume serverless LLM inference   → DeepInfra/   Pre-hosted OSS LLMs; OpenAI-
(Llama/Mistral at scale)               → fal.ai       compatible API; no model mgmt

GPU cloud marketplace (raw instances)  → RunPod       Cheapest $/GPU-hour; community
(max control, own serving stack)       →              templates; H100/RTX options

APAC AI Infrastructure Layer:
  Agent code execution:  E2B (safety-first isolation)
  Custom model serving:  Baseten (managed deployment)
  Flexible GPU burst:    Cerebrium (serverless; sub-second startup)
  Pre-hosted LLMs:       DeepInfra / fal.ai (no model management)
  Raw GPU instances:     RunPod (max control; cheapest $/hour)

E2B: APAC Secure AI Agent Code Execution

E2B APAC sandbox integration for AI coding agent

# APAC: E2B — run LLM-generated code safely in isolated sandbox

import anthropic
from e2b_code_interpreter import Sandbox
import os

apac_claude = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])

async def apac_ai_data_analyst(apac_user_query: str, apac_dataset_path: str) -> str:
    """APAC: AI data analyst that generates and runs Python code safely in E2B sandbox."""

    # APAC: Step 1 — Create isolated sandbox (150ms startup)
    with Sandbox() as apac_sandbox:

        # APAC: Step 2 — Upload APAC dataset to sandbox
        with open(apac_dataset_path, "rb") as apac_data_file:
            apac_sandbox.files.write("/home/user/data.csv", apac_data_file.read())

        # APAC: Step 3 — Generate analysis code with Claude
        apac_code_prompt = f"""
        Write Python code to answer this question about the CSV file at /home/user/data.csv:
        {apac_user_query}

        The dataset contains APAC sales data with columns: date, region, product, revenue_sgd, units.
        Use pandas for analysis. Print results clearly. If creating a chart, save to /home/user/chart.png.
        Return ONLY the Python code, no explanation.
        """

        apac_code_response = apac_claude.messages.create(
            model="claude-sonnet-4-6",
            max_tokens=2048,
            messages=[{"role": "user", "content": apac_code_prompt}],
        )
        apac_generated_code = apac_code_response.content[0].text

        # APAC: Step 4 — Execute generated code in isolated sandbox (safe!)
        apac_execution = apac_sandbox.run_code(apac_generated_code)

        # APAC: Step 5 — Capture output
        apac_stdout = apac_execution.text
        apac_error  = apac_execution.error

        if apac_error:
            # APAC: Code failed — send error back to Claude for self-correction
            apac_fix_response = apac_claude.messages.create(
                model="claude-sonnet-4-6",
                max_tokens=1024,
                messages=[
                    {"role": "user",      "content": apac_code_prompt},
                    {"role": "assistant", "content": apac_generated_code},
                    {"role": "user",      "content": f"Error: {apac_error.value}\nFix the code."},
                ],
            )
            apac_fixed_code = apac_fix_response.content[0].text
            apac_execution  = apac_sandbox.run_code(apac_fixed_code)
            apac_stdout     = apac_execution.text

        # APAC: Step 6 — Download chart if generated
        apac_chart = None
        try:
            apac_chart = apac_sandbox.files.read("/home/user/chart.png")
        except Exception:
            pass  # APAC: no chart generated for this query

        # APAC: Sandbox destroyed automatically at end of `with` block
        # APAC: LLM-generated code never had access to production systems

    return {"analysis": apac_stdout, "chart": apac_chart}

# APAC: Usage examples (all code runs in isolated sandbox)
apac_result = await apac_ai_data_analyst(
    apac_user_query="Which APAC region had the highest revenue growth in Q1 2026?",
    apac_dataset_path="apac_sales_2026.csv",
)
print(apac_result["analysis"])

E2B APAC multi-step agent workflow

# APAC: E2B — persistent sandbox for multi-step data science agent

from e2b_code_interpreter import Sandbox

async def apac_ml_pipeline_agent(apac_instructions: list[str]) -> dict:
    """APAC: Multi-step ML pipeline agent with persistent sandbox state."""

    apac_results = []

    # APAC: Single sandbox persists across all steps (shared state)
    with Sandbox(timeout=300) as apac_sandbox:  # APAC: 5-minute session

        for apac_step, apac_instruction in enumerate(apac_instructions):

            # APAC: Generate step code from instruction
            apac_step_code = await apac_generate_code(apac_instruction)

            # APAC: Execute in persistent sandbox (previous step results available)
            apac_execution = apac_sandbox.run_code(apac_step_code)

            apac_results.append({
                "step": apac_step + 1,
                "instruction": apac_instruction,
                "output": apac_execution.text,
                "error": apac_execution.error.value if apac_execution.error else None,
            })

    return apac_results

# APAC: Multi-step pipeline: each step builds on previous outputs
apac_pipeline_results = await apac_ml_pipeline_agent([
    "Load the APAC credit data from /home/user/credit.parquet and show shape",
    "Split into train/test (80/20) and show class distribution",
    "Train an XGBoost classifier and print validation AUC",
    "Plot feature importances as bar chart and save to /home/user/features.png",
])
# APAC: State persists: variables, files, pip installs carried across steps
# APAC: Entire pipeline ran in isolated sandbox — production DB never touched

Baseten: APAC ML Model Inference Deployment

Baseten APAC Truss model packaging

# APAC: Baseten Truss — package custom PyTorch model for deployment

# File: model/model.py (Truss model class)
import torch
import torch.nn as nn
from typing import Dict

class Model:
    """APAC: Truss model class — wraps PyTorch model for Baseten deployment."""

    def __init__(self, **kwargs):
        self._model = None
        self._device = "cuda" if torch.cuda.is_available() else "cpu"
        # APAC: model_dir contains weights uploaded with truss push
        self._model_dir = kwargs.get("data_dir")

    def load(self):
        """APAC: Called once at startup — load model weights into GPU memory."""
        apac_checkpoint = torch.load(
            f"{self._model_dir}/apac_sentiment_model.pt",
            map_location=self._device,
        )
        self._model = APACSentimentClassifier()
        self._model.load_state_dict(apac_checkpoint["model_state"])
        self._model.to(self._device)
        self._model.train(False)  # APAC: inference mode after loading weights

    def predict(self, request: Dict) -> Dict:
        """APAC: Called per inference request — run model on input."""
        apac_text = request.get("text", "")
        apac_lang = request.get("language", "en")

        # APAC: Tokenize and run inference
        apac_tokens  = apac_tokenize(apac_text, apac_lang)
        apac_tensor  = torch.tensor(apac_tokens).unsqueeze(0).to(self._device)

        with torch.no_grad():
            apac_logits = self._model(apac_tensor)
            apac_probs  = torch.softmax(apac_logits, dim=-1)

        apac_classes = ["negative", "neutral", "positive"]
        apac_pred_idx = apac_probs.argmax().item()

        return {
            "sentiment":   apac_classes[apac_pred_idx],
            "confidence":  float(apac_probs[0][apac_pred_idx]),
            "language":    apac_lang,
            "probabilities": {
                apac_classes[i]: float(apac_probs[0][i]) for i in range(3)
            },
        }

# APAC: Deploy model to Baseten

# APAC: Install Truss and authenticate
pip install truss
truss login  # APAC: authenticate with Baseten API key

# APAC: Package model directory into Truss
truss init apac-sentiment-model

# APAC: Specify GPU requirements in config.yaml
cat > apac-sentiment-model/config.yaml << 'EOF'
model_name: APAC Multilingual Sentiment Classifier
python_version: py311
resources:
  accelerator: A10G    # APAC: A10G for cost-effective inference
  use_gpu: true
environment_variables:
  APAC_MODEL_VERSION: "3.2"
requirements:
  - torch==2.3.0
  - transformers==4.40.0
EOF

# APAC: Push and deploy (uploads model weights + starts inference server)
truss push apac-sentiment-model --publish

# APAC: Inference endpoint live at:
# https://model-{id}.api.baseten.co/production/predict
# APAC: Auto-scales from 0 to N replicas based on request volume

Baseten APAC inference API call

# APAC: Call deployed Baseten model from APAC application

import requests
import os

BASETEN_API_KEY = os.environ["BASETEN_API_KEY"]
APAC_MODEL_ID   = "apac_sentiment_model_v3"

def apac_classify_sentiment(apac_text: str, apac_language: str = "en") -> dict:
    """APAC: Classify customer feedback sentiment via Baseten inference API."""

    apac_response = requests.post(
        f"https://model-{APAC_MODEL_ID}.api.baseten.co/production/predict",
        headers={"Authorization": f"Api-Key {BASETEN_API_KEY}"},
        json={"text": apac_text, "language": apac_language},
    )
    return apac_response.json()

# APAC: Classify customer feedback in multiple APAC languages
apac_feedback_samples = [
    ("服务很差，等了30分钟还没人接听", "zh"),
    ("製品の品質は素晴らしいです", "ja"),
    ("배송이 너무 느려서 실망했습니다", "ko"),
    ("Great service, very responsive team!", "en"),
]

for apac_text, apac_lang in apac_feedback_samples:
    apac_result = apac_classify_sentiment(apac_text, apac_lang)
    print(f"[{apac_lang}] {apac_result['sentiment']} ({apac_result['confidence']:.2f}): {apac_text[:40]}")

Cerebrium: APAC Serverless GPU Compute

Cerebrium APAC deployment

# APAC: Cerebrium — deploy custom AI function as serverless GPU worker

# File: main.py (deployed to Cerebrium)
from cerebrium import app, get_secret

# APAC: Define inference function (runs on GPU on each request)
@app.route("/apac-image-generate", gpu="A100")
def apac_generate_image(prompt: str, width: int = 1024, height: int = 1024) -> dict:
    """APAC: SDXL image generation on serverless A100."""

    import torch
    from diffusers import StableDiffusionXLPipeline

    # APAC: Load model from Cerebrium persistent storage (cached after first load)
    apac_pipe = StableDiffusionXLPipeline.from_pretrained(
        "/persistent/sdxl-base-1.0",
        torch_dtype=torch.float16,
        use_safetensors=True,
    ).to("cuda")
    apac_pipe.enable_model_cpu_offload()

    # APAC: Generate APAC-themed product image
    apac_image = apac_pipe(
        prompt=f"{prompt}, APAC style, professional product photography",
        negative_prompt="blurry, low quality, distorted",
        width=width,
        height=height,
        num_inference_steps=30,
        guidance_scale=7.5,
    ).images[0]

    # APAC: Encode output as base64
    import base64
    from io import BytesIO
    apac_buffer = BytesIO()
    apac_image.save(apac_buffer, format="PNG")
    apac_b64 = base64.b64encode(apac_buffer.getvalue()).decode()

    return {"image_b64": apac_b64, "width": width, "height": height}

# APAC: Deploy to Cerebrium
pip install cerebrium
cerebrium login

# APAC: Deploy function (builds container, pushes to Cerebrium)
cerebrium deploy main.py \
  --name apac-image-generator \
  --gpu A100 \
  --python-version 3.11 \
  --requirements diffusers torch transformers accelerate

# APAC: Endpoint live at: https://api.cerebrium.ai/v4/apac-team/apac-image-generator/apac-generate-image
# APAC: Sub-second cold start — first request after idle period: <1s startup
# APAC: Billing: per GPU-second of actual execution time

APAC AI Infrastructure Cost Comparison

Use case: APAC e-commerce product image generation, 500 images/day

Option A: E2B (if task involves LLM-generated code)
  NOT applicable — E2B is for code execution, not model inference
  Use: AI data analyst, code interpreter agents, automated ML pipelines

Option B: Baseten (managed model deployment)
  Deploy SDXL model to Baseten A10G:
  - Auto-scaling: 0 replicas when idle, 1-3 replicas during business hours
  - Generation time: 8s/image on A10G
  - A10G rate: ~$0.85/hr; active hours/day: ~6h (500 images ÷ 62.5 img/hr)
  - Daily cost: $0.85/hr × 6h = $5.10/day = $153/month
  - Setup: 1 day (Truss packaging + deploy)

Option C: Cerebrium (serverless GPU)
  Deploy SDXL as Cerebrium function on A100:
  - Generation time: 4s/image on A100
  - A100 rate: ~$3.00/hr; active compute: 500 × 4s = 2,000s = 0.56h
  - Daily cost: $3.00 × 0.56h = $1.67/day = $50/month
  - Setup: 2 hours (single file + CLI deploy)
  - Cold start: <1s (pre-warmed pool)

Option D: RunPod (raw GPU instance)
  RTX 4090 spot: ~$0.35/hr × 6h/day = $2.10/day = $63/month
  + serving code, Docker, scaling management
  Total with eng time: $63 + ~$400/month eng overhead = ~$463/month

APAC summary:
  Cerebrium: cheapest for variable workloads ($50/month)
  Baseten: best managed experience, predictable scaling ($153/month)
  RunPod: cheapest for sustained high-volume with own infrastructure team

Related APAC AI Infrastructure Resources

For the GPU cloud platforms (RunPod, DeepInfra, fal.ai) that overlap with Cerebrium and Baseten for serverless GPU inference — providing pre-hosted open-source LLMs and larger GPU marketplaces for APAC teams with different pricing and control trade-offs — see the APAC GPU cloud and serverless inference guide.

For the LLM inference frameworks (vLLM, Ollama, LiteLLM) that run on Baseten or Cerebrium GPU infrastructure to serve open-source LLMs — providing the serving layer that APAC teams deploy on top of GPU cloud platforms — see the APAC LLM inference guides in the APAC AI tools catalog.

For the agentic AI frameworks (LangChain, CrewAI, AutoGen) that use E2B sandboxes as the code execution backend for AI agents that generate and run Python code as part of multi-step reasoning workflows — see the APAC AI agent framework tools in the APAC AI tools catalog.