Last Updated: November 6, 2025

Overview

Migrating from AWS Batch to SaladCloud enables you to reduce batch processing costs by up to 90% while maintaining robust job orchestration and scaling capabilities. If you’re currently running batch jobs on AWS Batch compute environments, you’ll find that SaladCloud offers similar patterns for job queuing, automatic scaling, and distributed processing — but at a fraction of the cost. What Stays Exactly the Same:

Your application code and processing logic remain unchanged
Same containerized workloads (Docker/ECS task definitions convert easily)
Job submission and monitoring patterns
Automatic retry logic for failed jobs
Queue-based job distribution

What Gets Simpler:

No complex compute environment configuration
Simplified job definitions (just containers and resources)
Straightforward pricing without EC2/Fargate complexity
Built-in global distribution without multi-region setup

Key Differences to Consider:

SaladCloud uses distributed consumer GPUs instead of EC2/Fargate
Job processing through HTTP endpoints rather than AWS Batch agents
Cloud storage patterns instead of EBS volumes
Slower cold starts but dramatically lower costs

The migration primarily involves adapting your AWS Batch job definitions to SaladCloud’s container-based job processing model while preserving your existing batch processing workflows.

💡 New to SaladCloud? Check out our getting started guide for an introduction to deploying on SaladCloud, or explore our job queue documentation to understand how SaladCloud handles batch processing.

Why Migrate from AWS Batch to SaladCloud?

AWS Batch has served as a reliable batch processing solution, but its costs can quickly escalate, especially for GPU-intensive workloads. SaladCloud offers a compelling alternative that addresses common AWS Batch pain points: Cost Advantages:

90% Lower Compute Costs: GPU + CPU + RAM combined cost a fraction of EC2 instances
- RTX 4090 setup: $0.30/hr at high priority vs P3.2xlarge: $3.06/hr
Transparent Component Pricing:
- **GPU Containers:**GPU hourly rate
- CPU-only Containers: - $0.004/vCPU/hour + $0.001/GB RAM/hour
Per-Second Billing: Hourly rates tracked per second for running containers
No Hidden Costs: No charges for VPC endpoints, NAT gateways, or data transfer between AZs

Operational Benefits:

Simplified Management: No compute environment configuration or AMI management
Automatic Global Distribution: Access to 11,000+ GPUs worldwide without multi-region complexity
Built-in Resilience: Automatic failover and retry logic included
No Infrastructure Overhead: Focus on your batch jobs, not EC2 fleet management

When SaladCloud Excels:

Long-running batch jobs where startup time is less critical
GPU-intensive workloads (ML training, rendering, simulations)
Cost-sensitive batch processing
Non-time-critical workloads (batch priority adds 40-50% savings on top of base 90% savings)
Globally distributed data processing
Development and testing environments

Trade-offs to Consider:

Cold Start Times: Container startup takes minutes vs. seconds on pre-warmed EC2 instances
Storage Model: No EBS volumes; use cloud storage APIs instead
Service Integration: Fewer native AWS service integrations
Job Complexity: Better suited for containerized workloads than complex multi-step pipelines

Product Comparison: AWS Batch vs. SaladCloud

Core Component Mapping

AWS Batch Component	SaladCloud Equivalent	Key Differences
Compute Environment	Container Groups	No EC2 configuration needed; automatic GPU provisioning
Job Queues	Salad Job Queues	HTTP-based job distribution instead of agent-based
Job Definitions	Container Configuration	Simpler format; no need for vCPU/memory registration
Array Jobs	Multiple Job Submissions	Submit individual jobs; same parallelization benefits
Job Dependencies	Application-Level Logic	Handle dependencies in your code or orchestration layer
CloudWatch Logs	Portal Logs/External Logging	Built-in logs or integrate with Datadog, Axiom, etc.
Step Functions	External Orchestrators	Use Airflow, Temporal, or similar for complex workflows

Feature Comparison

Feature	AWS Batch	SaladCloud
Job Scheduling	Priority-based with fair share	FIFO queue processing
Auto Scaling	Based on queue depth	Queue-based or custom metrics
Spot/On-Demand Mix	Configurable compute environments	4 priority tiers (“Lowest” adds 40-50% to savings)
GPU Support	Accelerated Computing instances	Consumer GPUs (RTX 4090, 5090, etc.)
Container Runtime	ECS or EKS	Docker containers
Job Retries	Configurable retry attempts	Automatic 3 retries (4 total attempts)
Job Timeouts	Configurable per job	Container-level configuration
Long-Running Jobs	Supported with spot instance risks	Use Kelpie for checkpointing/resumption
Multi-Step Jobs	Via Step Functions	Single container jobs (orchestrate externally)
Storage	EBS volumes, EFS	S3-compatible cloud storage (e.g., R2)
Networking	VPC, Security Groups	No networking config needed with Job Queues
Monitoring	CloudWatch Metrics/Logs	Portal metrics, external monitoring tools
Cost Model	EC2/Fargate pricing + Batch overhead	Simple hourly rates (billed per second)

Migration Requirements

Technical Requirements

Containerization: Jobs must run in Docker containers (you likely already have this with ECS task definitions)
HTTP Interface: Jobs receive work via HTTP endpoints instead of AWS Batch job parameters
Cloud Storage: Replace EBS/EFS with S3-compatible storage (Cloudflare R2 recommended for no egress fees)
Queue Worker: Add the Salad Job Queue Worker binary to your container (handles job distribution)

Architectural Shifts

From Agent-Based to Queue Worker: AWS Batch agents pull jobs; SaladCloud Queue Worker receives and forwards jobs locally
From EC2 Fleets to Distributed Nodes: No direct control over compute instances
From VPC Networking to No Networking: Job Queues eliminate networking configuration entirely
From IAM Roles to API Keys: Different authentication model

Before You Begin: Key Concepts

Understanding the Job Processing Model

AWS Batch Model:

Job Queue → Compute Environment → EC2 Instance → Batch Agent → Container

SaladCloud Model:

Job Queue → Container Group → Distributed Nodes → Queue Worker → Your App

The key difference is that SaladCloud uses an HTTP-based job distribution model where the Salad Job Queue Worker (a lightweight binary you add to your container) receives jobs from the queue and forwards them to your application via localhost HTTP calls. This means your application doesn’t need IPv6 binding or external network access.

Container Startup Behavior

AWS Batch: Containers start when jobs are assigned, run the job, then terminate. SaladCloud: Containers run continuously and process multiple jobs. You can use Job Queue Autoscaling to automatically scale to zero when you have no jobs left to process. Your application should:

Start an HTTP server to receive jobs
Process jobs when received
Return results via HTTP response
Stay running to process more jobs

Storage Patterns

Since SaladCloud doesn’t support mounted volumes, you’ll need to adapt your storage strategy. Important: Use Egress-Free Storage We strongly recommend using egress-free storage providers like Cloudflare R2 instead of AWS S3. SaladCloud’s distributed nodes are not in datacenters, so egress fees from traditional cloud storage can add up quickly.

# AWS Batch pattern with EBS
def process_job(job_params):
    input_file = f"/mnt/efs/inputs/{job_params['file_id']}"
    output_file = f"/mnt/efs/outputs/{job_params['file_id']}.result"

    data = load_from_disk(input_file)
    result = process_data(data)
    save_to_disk(result, output_file)

# SaladCloud pattern with Cloudflare R2 (recommended) or S3
import boto3

# For Cloudflare R2 (no egress fees)
s3 = boto3.client('s3',
    endpoint_url='https://your-account.r2.cloudflarestorage.com',
    aws_access_key_id='your-r2-access-key',
    aws_secret_access_key='your-r2-secret'
)

# Or for AWS S3 (will incur egress charges)
# s3 = boto3.client('s3')

def process_job(job_params):
    # Download from S3
    input_data = s3.get_object(
        Bucket=job_params['input_bucket'],
        Key=job_params['input_key']
    )['Body'].read()

    # Process in memory or temp storage
    result = process_data(input_data)

    # Upload to S3
    s3.put_object(
        Bucket=job_params['output_bucket'],
        Key=job_params['output_key'],
        Body=result
    )

Step-by-Step Migration Process

Step 1: Prepare Your SaladCloud Environment

Account Setup

Create account at portal.salad.com
Set up organization and project
Generate API key for programmatic access

Install SaladCloud SDK (Optional)

# Python
pip install salad-cloud-sdk

# Node.js
npm install @saladtechnologies/salad-cloud-sdk

Step 2: Convert AWS Batch Job Definitions

Transform ECS Task Definitions

AWS Batch Job Definition:

{
  "jobDefinitionName": "image-processing",
  "type": "container",
  "containerProperties": {
    "image": "my-ecr-repo/processor:latest",
    "vcpus": 4,
    "memory": 8192,
    "jobRoleArn": "arn:aws:iam::123456789012:role/BatchJobRole",
    "environment": [{ "name": "PROCESSING_MODE", "value": "batch" }],
    "resourceRequirements": [{ "type": "GPU", "value": "1" }]
  }
}

SaladCloud Container Configuration:

# Dockerfile with Salad Job Queue Worker
FROM my-ecr-repo/processor:latest

# Download the Salad Job Queue Worker binary
ADD https://github.com/SaladTechnologies/salad-cloud-job-queue-worker/releases/latest/download/salad-job-queue-worker-linux-amd64 /usr/local/bin/salad-job-queue-worker
RUN chmod +x /usr/local/bin/salad-job-queue-worker

# Your existing application setup
WORKDIR /app
COPY . .

# You'll need to manage both processes - your app and the queue worker
# See /container-engine/how-to-guides/job-processing/queue-worker for s6-overlay or wrapper approaches
# The queue worker will forward jobs to your app on localhost:8080
# Your app does NOT need IPv6 binding when using job queues

Adapt Job Input/Output Patterns

AWS Batch Job Script:

import os
import json

def main():
    # AWS Batch provides job parameters via environment variables
    job_params = json.loads(os.environ.get('BATCH_JOB_PARAMETERS', '{}'))
    input_file = job_params['inputFile']
    output_location = job_params['outputLocation']

    # Process the job
    result = process_file(input_file)

    # Save results
    save_to_s3(result, output_location)

if __name__ == "__main__":
    main()

SaladCloud HTTP Handler:

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel

app = FastAPI()

class JobRequest(BaseModel):
    inputFile: str
    outputLocation: str

@app.post("/process")
async def process_job(request: JobRequest):
    try:
        # Process the job (same logic as before)
        result = process_file(request.inputFile)
        save_to_s3(result, request.outputLocation)

        return {
            "status": "success",
            "output": request.outputLocation
        }
    except Exception as e:
        # Return 500 to trigger retry
        raise HTTPException(status_code=500, detail=str(e))

# When using Job Queues, bind to localhost (queue worker handles external access)
if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8080)  # No IPv6 needed with job queues!

Step 3: Choose Your Job Queue Solution

These patterns can be implemented with any job queue, including those not on the Salad platform, but these two have platform integration with SaladCloud.

Salad Job Queues vs. Kelpie

SaladCloud offers two job queue solutions, each optimized for different use cases: Salad Job Queues (Recommended for most AWS Batch migrations):

Best for jobs that complete in minutes to a few hours
Built-in retry logic (3 retries, 4 total attempts)
Simple HTTP-based job distribution
Native autoscaling based on queue depth
No additional setup required

Salad Kelpie (For long-running or interruptible workloads):

Designed for jobs running many hours or days (ML training, simulations)
Built-in checkpointing and resumption capabilities
Automatic cloud storage integration for progress saves
Handles node interruptions gracefully
Ideal for workloads that need to survive node failures

When to use Kelpie instead of Job Queues:

Jobs that run longer than 30 minutes
ML model training or fine-tuning
Molecular dynamics simulations
Any workload where losing progress would be costly
Jobs that need to save and resume from checkpoints

For this guide, we’ll use Salad Job Queues as they’re the closest match to AWS Batch for most use cases. If you have long-running workloads, see our Kelpie documentation.

Create a Salad Job Queue

Job Queues can only be created via the API (not available in the portal):

curl -X POST "https://api.salad.com/api/public/organizations/$ORG/projects/$PROJECT/queues" \
  -H "Salad-Api-Key: $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "batch-processing-queue",
    "display_name": "Batch Processing Queue",
    "description": "Queue for batch processing jobs migrated from AWS Batch"
  }'

Or via Python SDK:

from salad_cloud_sdk import SaladCloudSdk

sdk = SaladCloudSdk(api_key="YOUR_API_KEY")

queue = sdk.queues.create_queue(
    organization_name="your-org",
    project_name="your-project",
    request_body={
        "name": "batch-processing-queue",
        "display_name": "Batch Processing Queue"
    }
)

Step 4: Deploy Container Group with Queue

Container Group Configuration

from salad_cloud_sdk import SaladCloudSdk

sdk = SaladCloudSdk(api_key="YOUR_API_KEY")

# Create container group connected to job queue
# Note: Container Gateway is NOT needed when using Job Queues
container_group = sdk.container_groups.create_container_group(
    organization_name="your-org",
    project_name="your-project",
    request_body={
        "name": "batch-processor",
        "container": {
            "image": "your-registry/batch-processor:latest",
            "resources": {
                "cpu": 4,
                "memory": 8192,
                "gpu_classes": ["ed563892-aacd-40f5-80b7-90c9be6c759b"]  # RTX 4090 (24 GB)
            },
            "environment_variables": {
                "PROCESSING_MODE": "batch",
                "AWS_REGION": "us-east-1"  # For S3 access
            }
        },
        "queue_connection": {
            "queue_name": "batch-processing-queue",
            "port": 8080  # Port where your app listens locally
        },
        "replicas": 3,  # Start with desired capacity
        "autostart_policy": True,
        "restart_policy": "always"
        # No networking/gateway configuration needed!
    }
)

Step 5: Submit and Monitor Jobs

Job Submission

AWS Batch Pattern:

import boto3

batch = boto3.client('batch')

response = batch.submit_job(
    jobName='process-image-001',
    jobQueue='my-job-queue',
    jobDefinition='image-processing',
    parameters={
        'inputFile': 's3://bucket/input/image.jpg',
        'outputLocation': 's3://bucket/output/'
    }
)
job_id = response['jobId']

SaladCloud Pattern:

from salad_cloud_sdk import SaladCloudSdk

sdk = SaladCloudSdk(api_key="YOUR_API_KEY")

# Submit job to queue
job = sdk.queues.create_job(
    organization_name="your-org",
    project_name="your-project",
    queue_name="batch-processing-queue",
    request_body={
        "input": {
            "inputFile": "s3://bucket/input/image.jpg",
            "outputLocation": "s3://bucket/output/"
        }
    }
)
job_id = job.id

Job Monitoring

# Check job status
job_status = sdk.queues.get_job(
    organization_name="your-org",
    project_name="your-project",
    queue_name="batch-processing-queue",
    job_id=job_id
)

print(f"Job Status: {job_status.status}")
if job_status.status == "completed":
    print(f"Results: {job_status.output}")
elif job_status.status == "failed":
    print(f"Error: {job_status.error}")

Step 6: Implement Autoscaling

Queue-Based Autoscaling

# Configure autoscaling based on queue depth
sdk.container_groups.update_container_group(
    organization_name="your-org",
    project_name="your-project",
    container_group_name="batch-processor",
    request={
        "queue_autoscaler": {
            "min_replicas": 0,  # Scale to zero when idle
            "max_replicas": 50,  # Maximum capacity
            "desired_queue_length": 2,  # Target 2 jobs per instance
            "polling_period": 30  # Check every 30 seconds
        }
    }
)

Custom Metrics Autoscaling

import time
from datetime import datetime

def scale_based_on_time():
    """Scale up during business hours"""
    sdk = SaladCloudSdk(api_key="YOUR_API_KEY")

    while True:
        hour = datetime.now().hour

        # Scale up during business hours (9 AM - 6 PM)
        if 9 <= hour < 18:
            target_replicas = 10
        else:
            target_replicas = 2

        sdk.container_groups.update_container_group(
            organization_name="your-org",
            project_name="your-project",
            container_group_name="batch-processor",
            request={"replicas": target_replicas}
        )

        time.sleep(300)  # Check every 5 minutes

Migration Patterns for Common AWS Batch Scenarios

Pattern 1: Simple Batch Processing

AWS Batch Approach:

Submit jobs with parameters
Process in container
Write results to S3

SaladCloud Migration:

# 1. Container with HTTP endpoint
@app.post("/process")
async def process_batch(job: dict):
    # Same processing logic
    result = your_existing_function(job['input'])
    return {"output": result}

# 2. Submit jobs to queue
for item in batch_items:
    sdk.queues.create_job(
        organization_name="your-org",
        project_name="your-project",
        queue_name="batch-queue",
        request_body={"input": item}
    )

Pattern 2: Array Jobs

AWS Batch Array Jobs:

aws batch submit-job \
  --array-properties size=100 \
  --job-name array-job \
  --job-queue my-queue

SaladCloud Equivalent:

# Submit multiple jobs to achieve same parallelization
jobs = []
for i in range(100):
    job = sdk.queues.create_job(
        organization_name="your-org",
        project_name="your-project",
        queue_name="batch-queue",
        request_body={
            "input": {
                "index": i,
                "total": 100,
                "data": f"s3://bucket/data/chunk_{i}.json"
            }
        }
    )
    jobs.append(job.id)

# Monitor all jobs
for job_id in jobs:
    status = sdk.queues.get_job(
        organization_name="your-org",
        project_name="your-project",
        queue_name="batch-queue",
        job_id=job_id
    )
    print(f"Job {job_id}: {status.status}")

Pattern 3: Long-Running Jobs with Kelpie

AWS Batch Long-Running Jobs:

Multi-hour ML training jobs
Risk of spot instance termination
Manual checkpointing required

SaladCloud with Kelpie:

# Add Kelpie to your container
FROM pytorch/pytorch:2.7.1-cuda12.6-cudnn9-runtime

# Add the Kelpie binary
ARG KELPIE_VERSION=0.6.0
ADD https://github.com/SaladTechnologies/kelpie/releases/download/${KELPIE_VERSION}/kelpie /kelpie
RUN chmod +x /kelpie

# Your training code
COPY train.py /app/train.py
WORKDIR /app

# Kelpie handles job execution and checkpointing
CMD ["/kelpie"]

Benefits of Kelpie for long jobs:

Automatic checkpoint upload to S3-compatible storage
Resume from last checkpoint after interruption
No data loss from node failures
Built-in integration with SaladCloud

See the Kelpie guide for detailed setup.

Pattern 4: GPU-Accelerated ML Training

AWS Batch with GPU:

{
  "resourceRequirements": [
    { "type": "GPU", "value": "1" },
    { "type": "MEMORY", "value": "32768" },
    { "type": "VCPU", "value": "8" }
  ]
}

SaladCloud GPU Configuration:

container_group = {
    "container": {
        "image": "your-ml-training:latest",
        "resources": {
            "cpu": 8,
            "memory": 32768,
            "gpu_classes": [
                "ed563892-aacd-40f5-80b7-90c9be6c759b",  # RTX 4090 (24 GB)
                "a5db5c50-cbcb-4596-ae80-6a0c8090d80f"   # RTX 3090 (24 GB)
            ]
        }
    }
}

Pattern 5: Dependent Jobs

AWS Batch with Dependencies:

job1 = batch.submit_job(jobName="preprocess")
job2 = batch.submit_job(
    jobName="process",
    dependsOn=[{"jobId": job1['jobId']}]
)

SaladCloud Pattern:

# Implement dependency logic in your application
@app.post("/process")
async def process_with_dependencies(job: dict):
    # Check if prerequisites are complete
    if job.get('depends_on'):
        for dep_id in job['depends_on']:
            dep_status = sdk.queues.get_job(
                organization_name="your-org",
                project_name="your-project",
                queue_name="batch-queue",
                job_id=dep_id
            )
            if dep_status.status != "completed":
                # Return 503 to retry later
                raise HTTPException(status_code=503, detail="Dependencies not ready")

    # Process the job
    result = process_data(job['input'])

    # Trigger dependent jobs if needed
    if job.get('triggers'):
        for next_job in job['triggers']:
            sdk.queues.create_job(
                organization_name="your-org",
                project_name="your-project",
                queue_name="batch-queue",
                request_body=next_job
            )

    return {"output": result}

Monitoring and Logging

Replace CloudWatch with External Logging

Configure Axiom Logging (Recommended):

# In your container group configuration
container_group = {
    "container": {
        "logging": {
            "axiom": {
                "dataset": "salad-batch-jobs",
                "token": "YOUR_AXIOM_TOKEN",
                "url": "https://cloud.axiom.co"
            }
        }
    }
}

Application-Level Logging:

import logging
import json

# Configure structured logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

@app.post("/process")
async def process_job(job: dict):
    job_id = job.get('id', 'unknown')

    logger.info(json.dumps({
        "event": "job_started",
        "job_id": job_id,
        "input": job['input']
    }))

    try:
        result = process_data(job['input'])

        logger.info(json.dumps({
            "event": "job_completed",
            "job_id": job_id,
            "duration": processing_time
        }))

        return {"output": result}

    except Exception as e:
        logger.error(json.dumps({
            "event": "job_failed",
            "job_id": job_id,
            "error": str(e)
        }))
        raise

Cost Optimization Strategies

1. Use Batch Priority for Non-Time-Sensitive Workloads

SaladCloud offers four priority tiers, with each tier offering additional savings on top of our already competitive base pricing (which is typically 80-90% less than AWS). For batch processing that isn’t time-critical, the “batch” priority tier offers the deepest discounts:

Priority	Use Case	Additional Savings vs Salad High Priority	Availability
High	Production, time-critical	Baseline (already ~90% less than AWS)	Highest
Medium	Standard workloads	~15-20% additional savings	Very Good
Low	Flexible deadlines	~25-35% additional savings	Good
Batch	Non-urgent processing	~40-50% additional savings	Variable

Example pricing for comparable GPU workload (24 GB VRAM, 8 vCPU, 32 GB RAM): AWS P4d.24xlarge (8x A100 40GB):

Total: ~$32.77/hour
Per GPU: ~$4.10/hour
Includes: 96 vCPUs, 1152 GB RAM (massive overkill for most batch jobs)

AWS P3.2xlarge (1x V100 16GB):

Total: ~$3.06/hour
Per GPU: $3.06/hour
Includes: 8 vCPUs, 61 GB RAM

SaladCloud RTX 4090 (24 GB):

High Priority: $0.30/hour GPU + $0.032/hour (8 vCPU) + $0.032/hour (32 GB RAM) = $0.364/hour total (88% less than P3.2xlarge)
Medium: $0.26 + $0.032 + $0.032 = $0.324/hour
Low: $0.22 + $0.032 + $0.032 = $0.284/hour
Batch: $0.18 + $0.032 + $0.032 = $0.244/hour (92% less than P3.2xlarge!)

# Configure container group with batch priority for maximum savings
# This gives you an additional 40% off SaladCloud's already low prices
container_group = sdk.container_groups.create_container_group(
    organization_name="your-org",
    project_name="your-project",
    request_body={
        "name": "batch-processor",
        "container": {
            # ... container config
        },
        "priority": "batch",  # Additional 40-50% savings on top of base savings
        "replicas": 10
    }
)

2. Scale to Zero During Off-Hours

# Configure minimum replicas to 0 for idle periods
queue_autoscaler = {
    "min_replicas": 0,  # Scale to zero when no jobs
    "max_replicas": 100,
    "desired_queue_length": 1
}

3. Optimize Container Size

# Use multi-stage builds to minimize image size
FROM python:3.9 AS builder
COPY requirements.txt .
RUN pip install --user -r requirements.txt

FROM python:3.9-slim
COPY --from=builder /root/.local /root/.local
COPY . /app
WORKDIR /app

4. Batch Small Jobs

@app.post("/process")
async def process_batch(request: dict):
    # Process multiple items in one job
    results = []
    for item in request['batch']:
        result = process_item(item)
        results.append(result)

    return {"outputs": results}

Migration Checklist

Pre-Migration

Inventory AWS Batch job definitions and compute environments
Identify storage dependencies (EBS, EFS volumes)
Document job dependencies and workflows
Review IAM roles and permissions needed
Estimate monthly job volumes and compute requirements

Container Preparation

Convert job scripts to HTTP endpoints
Add Salad Queue Worker to containers
Update to use cloud storage instead of mounted volumes
Test containers locally with IPv6 binding
Push containers to accessible registry

SaladCloud Setup

Create SaladCloud account and organization
Generate API keys
Create job queues
Deploy container groups with queue connections
Configure autoscaling policies

Testing

Submit test jobs to queues
Verify job processing and retries
Test autoscaling behavior
Validate logging and monitoring
Compare performance with AWS Batch baseline

Production Migration

Migrate batch jobs gradually (start with non-critical)
Monitor costs and performance
Adjust autoscaling based on actual usage
Update job submission scripts/applications
Decommission AWS Batch resources once stable

Common Challenges and Solutions

Challenge: No Step Functions Equivalent

Solution: Use external workflow orchestrators

# Apache Airflow DAG example
from airflow import DAG
from airflow.operators.python_operator import PythonOperator

def submit_salad_job(**context):
    sdk = SaladCloudSdk(api_key="YOUR_API_KEY")
    job = sdk.queues.create_job(
        organization_name="your-org",
        project_name="your-project",
        queue_name="batch-queue",
        request_body=context['params']
    )
    return job.id

dag = DAG('batch_workflow', default_args=default_args)

preprocess = PythonOperator(
    task_id='preprocess',
    python_callable=submit_salad_job,
    params={'input': 'preprocess_config'}
)

process = PythonOperator(
    task_id='process',
    python_callable=submit_salad_job,
    params={'input': 'process_config'}
)

preprocess >> process  # Define dependencies

Challenge: Job Scheduling

Solution: Implement cron-based job submission

from apscheduler.schedulers.blocking import BlockingScheduler

scheduler = BlockingScheduler()

@scheduler.scheduled_job('cron', hour=2)  # Run at 2 AM daily
def submit_nightly_batch():
    sdk = SaladCloudSdk(api_key="YOUR_API_KEY")

    # Submit batch jobs
    for job_config in nightly_jobs:
        sdk.queues.create_job(
            organization_name="your-org",
            project_name="your-project",
            queue_name="batch-queue",
            request_body=job_config
        )

scheduler.start()

Challenge: Large Data Transfer

Solution: Use pre-signed URLs and streaming

import boto3
from io import BytesIO

@app.post("/process")
async def process_large_file(job: dict):
    s3 = boto3.client('s3')

    # Stream large file from S3
    response = s3.get_object(
        Bucket=job['bucket'],
        Key=job['key']
    )

    # Process in chunks to avoid memory issues
    for chunk in response['Body'].iter_chunks(chunk_size=1024*1024):
        process_chunk(chunk)

    # Upload results with pre-signed URL
    presigned_url = s3.generate_presigned_url(
        'put_object',
        Params={'Bucket': job['output_bucket'], 'Key': job['output_key']},
        ExpiresIn=3600
    )

    return {"upload_url": presigned_url}

Performance Optimization

Minimize Cold Starts

# Keep containers warm with minimal replicas
container_group = {
    "replicas": 2,  # Always keep 2 instances running
    "queue_autoscaler": {
        "min_replicas": 2,  # Never scale below 2
        "max_replicas": 100
    }
}

Optimize Job Distribution

# Process multiple small jobs per container invocation
@app.post("/process")
async def process_job_batch(request: dict):
    # Check if this is a batch request
    if 'batch_size' in request:
        # Pull multiple jobs from queue
        jobs = []
        for _ in range(request['batch_size']):
            job = await get_next_job()  # Your queue logic
            if job:
                jobs.append(job)

        # Process all jobs
        results = [process_single_job(job) for job in jobs]
        return {"results": results}
    else:
        # Single job processing
        return process_single_job(request)

What You’ll Gain

Migrating from AWS Batch to SaladCloud provides:

Immediate Benefits

90% Cost Reduction: Dramatically lower compute costs for batch processing
Simplified Operations: No compute environment or AMI management
Global Scale: Access to 11,000+ GPUs worldwide
Transparent Pricing: Simple per-second billing without complex EC2 pricing tiers

Operational Improvements

Automatic Failover: Built-in retry logic and node replacement
Flexible Scaling: Scale to zero or thousands of instances
No Infrastructure Management: Focus on your batch jobs, not EC2 fleets
Unified Job Processing: Same patterns for CPU and GPU workloads

Trade-offs Accepted

Longer cold start times (minutes vs. seconds)
Different storage patterns (cloud APIs vs. mounted volumes)
Fewer AWS service integrations
HTTP-based job distribution instead of agent-based

Getting Help

SaladCloud Resources

Documentation: docs.salad.com
API Reference: SaladCloud API Documentation
Portal: portal.salad.com
Support: Contact cloud@salad.com

Migration Support

Job Processing Patterns

Job Queue Overview
Kelpie for Long-Running Jobs - Checkpointing and resumption
SQS Integration - For existing SQS workflows
Long-Running Tasks
Build Redis Queue

Migration Guides

GPU Workloads

Ready to start saving on your batch processing costs? Create your SaladCloud account and begin migrating your AWS Batch workloads today!

Reallocate Replica From Lambda (lambda.ai)

⌘I

​Overview

​Why Migrate from AWS Batch to SaladCloud?

​Product Comparison: AWS Batch vs. SaladCloud

​Core Component Mapping

​Feature Comparison

​Migration Requirements

​Technical Requirements

​Architectural Shifts

​Before You Begin: Key Concepts

​Understanding the Job Processing Model

​Container Startup Behavior

​Storage Patterns

​Step-by-Step Migration Process

​Step 1: Prepare Your SaladCloud Environment

​Account Setup

​Install SaladCloud SDK (Optional)

​Step 2: Convert AWS Batch Job Definitions

​Transform ECS Task Definitions

​Adapt Job Input/Output Patterns

​Step 3: Choose Your Job Queue Solution

​Salad Job Queues vs. Kelpie

​Create a Salad Job Queue

​Step 4: Deploy Container Group with Queue

​Container Group Configuration

​Step 5: Submit and Monitor Jobs

​Job Submission

​Job Monitoring

​Step 6: Implement Autoscaling

​Queue-Based Autoscaling

​Custom Metrics Autoscaling

​Migration Patterns for Common AWS Batch Scenarios

​Pattern 1: Simple Batch Processing

​Pattern 2: Array Jobs

​Pattern 3: Long-Running Jobs with Kelpie

​Pattern 4: GPU-Accelerated ML Training

​Pattern 5: Dependent Jobs

​Monitoring and Logging

​Replace CloudWatch with External Logging

​Cost Optimization Strategies

​1. Use Batch Priority for Non-Time-Sensitive Workloads

​2. Scale to Zero During Off-Hours

​3. Optimize Container Size

​4. Batch Small Jobs

​Migration Checklist

​Pre-Migration

​Container Preparation

​SaladCloud Setup

​Testing

​Production Migration

​Common Challenges and Solutions

​Challenge: No Step Functions Equivalent

​Challenge: Job Scheduling

​Challenge: Large Data Transfer

​Performance Optimization

​Minimize Cold Starts

​Optimize Job Distribution

​What You’ll Gain

​Immediate Benefits

​Operational Improvements

​Trade-offs Accepted

​Getting Help

​SaladCloud Resources

​Migration Support

​Related Resources

​Job Processing Patterns

​Migration Guides

​GPU Workloads

Overview

Why Migrate from AWS Batch to SaladCloud?

Product Comparison: AWS Batch vs. SaladCloud

Core Component Mapping

Feature Comparison

Migration Requirements

Technical Requirements

Architectural Shifts

Before You Begin: Key Concepts

Understanding the Job Processing Model

Container Startup Behavior

Storage Patterns

Step-by-Step Migration Process

Step 1: Prepare Your SaladCloud Environment

Account Setup

Install SaladCloud SDK (Optional)

Step 2: Convert AWS Batch Job Definitions

Transform ECS Task Definitions

Adapt Job Input/Output Patterns

Step 3: Choose Your Job Queue Solution

Salad Job Queues vs. Kelpie

Create a Salad Job Queue

Step 4: Deploy Container Group with Queue

Container Group Configuration

Step 5: Submit and Monitor Jobs

Job Submission

Job Monitoring

Step 6: Implement Autoscaling

Queue-Based Autoscaling

Custom Metrics Autoscaling

Migration Patterns for Common AWS Batch Scenarios

Pattern 1: Simple Batch Processing

Pattern 2: Array Jobs

Pattern 3: Long-Running Jobs with Kelpie

Pattern 4: GPU-Accelerated ML Training

Pattern 5: Dependent Jobs

Monitoring and Logging

Replace CloudWatch with External Logging

Cost Optimization Strategies

1. Use Batch Priority for Non-Time-Sensitive Workloads

2. Scale to Zero During Off-Hours

3. Optimize Container Size

4. Batch Small Jobs

Migration Checklist

Pre-Migration

Container Preparation

SaladCloud Setup

Testing

Production Migration

Common Challenges and Solutions

Challenge: No Step Functions Equivalent

Challenge: Job Scheduling

Challenge: Large Data Transfer

Performance Optimization

Minimize Cold Starts

Optimize Job Distribution

What You’ll Gain

Immediate Benefits

Operational Improvements

Trade-offs Accepted

Getting Help

SaladCloud Resources

Migration Support

Related Resources

Job Processing Patterns

Migration Guides

GPU Workloads