# AWS ECRsimalabs/simabit:aws-cuda11.8-ubuntu20.04# Google Container Registrygcr.io/simalabs/simabit:gcp-cuda11.8-ubuntu20.04# Azure Container Registrysimalabs.azurecr.io/simabit:azure-cuda11.8-ubuntu20.04

{  "Version": "2012-10-17",  "Statement": [    {      "Effect": "Allow",      "Action": [        "s3:GetObject",        "s3:PutObject",        "s3:DeleteObject"      ],      "Resource": "arn:aws:s3:::your-video-bucket/*"    },    {      "Effect": "Allow",      "Action": [        "mediaconvert:CreateJob",        "mediaconvert:GetJob",        "mediaconvert:ListJobs"      ],      "Resource": "*"    }  ]}

# Provider configurationterraform {  required_providers {    aws = {      source  = "hashicorp/aws"      version = "~> 5.0"    }  }}provider "aws" {  region = var.aws_region}# VPC and networkingresource "aws_vpc" "simabit_vpc" {  cidr_block           = "10.0.0.0/16"  enable_dns_hostnames = true  enable_dns_support   = true  tags = {    Name = "simabit-encoding-vpc"  }}resource "aws_subnet" "simabit_subnet" {  vpc_id                  = aws_vpc.simabit_vpc.id  cidr_block              = "10.0.1.0/24"  availability_zone       = data.aws_availability_zones.available.names[0]  map_public_ip_on_launch = true  tags = {    Name = "simabit-encoding-subnet"  }}# Security group for SimaBit instancesresource "aws_security_group" "simabit_sg" {  name_prefix = "simabit-encoding-"  vpc_id      = aws_vpc.simabit_vpc.id  ingress {    from_port   = 8080    to_port     = 8080    protocol    = "tcp"    cidr_blocks = ["10.0.0.0/16"]  }  egress {    from_port   = 0    to_port     = 0    protocol    = "-1"    cidr_blocks = ["0.0.0.0/0"]  }}# Launch template for GPU instancesresource "aws_launch_template" "simabit_template" {  name_prefix   = "simabit-encoding-"  image_id      = data.aws_ami.gpu_optimized.id  instance_type = "g4dn.xlarge"  key_name      = var.key_pair_name  vpc_security_group_ids = [aws_security_group.simabit_sg.id]  user_data = base64encode(templatefile("${path.module}/user_data.sh", {    simabit_license_key = var.simabit_license_key    s3_bucket          = aws_s3_bucket.video_processing.bucket  }))  tag_specifications {    resource_type = "instance"    tags = {      Name = "simabit-encoder"    }  }}# Auto Scaling Groupresource "aws_autoscaling_group" "simabit_asg" {  name                = "simabit-encoding-asg"  vpc_zone_identifier = [aws_subnet.simabit_subnet.id]  target_group_arns   = [aws_lb_target_group.simabit_tg.arn]  health_check_type   = "ELB"  min_size            = 1  max_size            = 10  desired_capacity    = 2  launch_template {    id      = aws_launch_template.simabit_template.id    version = "$Latest"  }  tag {    key                 = "Name"    value               = "simabit-encoder"    propagate_at_launch = true  }}

resource "aws_media_convert_queue" "simabit_queue" {  name = "simabit-preprocessing-queue"    pricing_plan = "ON_DEMAND"    tags = {    Environment = "production"    Purpose     = "simabit-preprocessing"  }}resource "aws_media_convert_job_template" "simabit_template" {  name = "simabit-h264-template"    settings_json = jsonencode({    OutputGroups = [{      Name = "File Group"      OutputGroupSettings = {        Type = "FILE_GROUP_SETTINGS"        FileGroupSettings = {          Destination = "s3://${aws_s3_bucket.processed_video.bucket}/"        }      }      Outputs = [{        NameModifier = "_simabit_optimized"        VideoDescription = {          CodecSettings = {            Codec = "H_264"            H264Settings = {              RateControlMode = "QVBR"              QvbrSettings = {                QvbrQualityLevel = 8              }            }          }        }      }]    }]  })}

import jsonimport boto3import osfrom typing import Dict, Anydef lambda_handler(event: Dict[str, Any], context: Any) -> Dict[str, Any]:    """    Orchestrates SimaBit preprocessing and MediaConvert encoding    """        s3_client = boto3.client('s3')    mediaconvert_client = boto3.client('mediaconvert')        # Extract S3 event details    bucket = event['Records'][0]['s3']['bucket']['name']    key = event['Records'][0]['s3']['object']['key']        # Trigger SimaBit preprocessing    preprocessing_job = trigger_simabit_preprocessing(bucket, key)        # Wait for preprocessing completion    wait_for_preprocessing(preprocessing_job['JobId'])        # Create MediaConvert job with preprocessed input    encoding_job = create_mediaconvert_job(        input_bucket=bucket,        input_key=f"preprocessed/{key}",        output_bucket=os.environ['OUTPUT_BUCKET']    )        return {        'statusCode': 200,        'body': json.dumps({            'preprocessing_job': preprocessing_job['JobId'],            'encoding_job': encoding_job['Job']['Id']        })    }def trigger_simabit_preprocessing(bucket: str, key: str) -> Dict[str, Any]:    """    Triggers SimaBit preprocessing via API call    """    import requests        simabit_endpoint = os.environ['SIMABIT_API_ENDPOINT']        payload = {        'input_s3_uri': f's3://{bucket}/{key}',        'output_s3_uri': f's3://{bucket}/preprocessed/{key}',        'quality_preset': 'high_efficiency',        'target_bitrate_reduction': 0.22    }        response = requests.post(        f'{simabit_endpoint}/preprocess',        json=payload,        headers={'Authorization': f'Bearer {os.environ["SIMABIT_API_KEY"]}'}    )        return response.json()

apiVersion: apps/v1kind: Deploymentmetadata:  name: simabit-preprocessor  namespace: video-processingspec:  replicas: 3  selector:    matchLabels:      app: simabit-preprocessor  template:    metadata:      labels:        app: simabit-preprocessor    spec:      nodeSelector:        cloud.google.com/gke-accelerator: nvidia-tesla-t4      containers:      - name: simabit        image: gcr.io/simalabs/simabit:gcp-cuda11.8-ubuntu20.04        resources:          requests:            nvidia.com/gpu: 1            memory: "8Gi"            cpu: "4"          limits:            nvidia.com/gpu: 1            memory: "16Gi"            cpu: "8"        env:        - name: SIMABIT_LICENSE_KEY          valueFrom:            secretKeyRef:              name: simabit-secrets              key: license-key        - name: GCS_BUCKET          value: "your-video-processing-bucket"        ports:        - containerPort: 8080          name: http        livenessProbe:          httpGet:            path: /health            port: 8080          initialDelaySeconds: 30          periodSeconds: 10        readinessProbe:          httpGet:            path: /ready            port: 8080          initialDelaySeconds: 5          periodSeconds: 5---apiVersion: v1kind: Servicemetadata:  name: simabit-service  namespace: video-processingspec:  selector:    app: simabit-preprocessor  ports:  - port: 80    targetPort: 8080    name: http  type: LoadBalancer

import functions_frameworkfrom google.cloud import storagefrom google.cloud import pubsub_v1import jsonimport requestsimport os@functions_framework.cloud_eventdef process_video_upload(cloud_event):    """    Triggered by Cloud Storage object creation    Initiates SimaBit preprocessing workflow    """        # Extract file details from event    bucket_name = cloud_event.data['bucket']    file_name = cloud_event.data['name']        # Skip if not a video file    if not is_video_file(file_name):        return        # Trigger SimaBit preprocessing    preprocessing_result = trigger_simabit_preprocessing(        bucket_name,         file_name    )        # Publish to Pub/Sub for downstream processing    publisher = pubsub_v1.PublisherClient()    topic_path = publisher.topic_path(        os.environ['GCP_PROJECT'],         'video-preprocessing-complete'    )        message_data = json.dumps({        'original_file': f'gs://{bucket_name}/{file_name}',        'preprocessed_file': preprocessing_result['output_uri'],        'bitrate_reduction': preprocessing_result['bitrate_reduction'],        'quality_score': preprocessing_result['vmaf_score']    }).encode('utf-8')        publisher.publish(topic_path, message_data)        return f'Preprocessing initiated for {file_name}'def trigger_simabit_preprocessing(bucket: str, filename: str) -> dict:    """    Calls SimaBit API to start preprocessing    """    simabit_endpoint = os.environ['SIMABIT_API_ENDPOINT']        payload = {        'input_gcs_uri': f'gs://{bucket}/{filename}',        'output_gcs_uri': f'gs://{bucket}/preprocessed/{filename}',        'optimization_level': 'balanced',        'target_reduction': 0.22    }        response = requests.post(        f'{simabit_endpoint}/gcp/preprocess',        json=payload,        headers={            'Authorization': f'Bearer {os.environ["SIMABIT_API_KEY"]}',            'Content-Type': 'application/json'        }    )        return response.json()

apiVersion: 2019-12-01location: eastusname: simabit-preprocessing-groupproperties:  containers:  - name: simabit-preprocessor    properties:      image: simalabs.azurecr.io/simabit:azure-cuda11.8-ubuntu20.04      resources:        requests:          cpu: 4          memoryInGb: 16          gpu:            count: 1            sku: V100      environmentVariables:      - name: SIMABIT_LICENSE_KEY        secureValue: your-license-key-here      - name: AZURE_STORAGE_ACCOUNT        value: yourstorageaccount      - name: AZURE_STORAGE_CONTAINER        value: video-processing      ports:      - port: 8080        protocol: TCP  osType: Linux  restartPolicy: Always  ipAddress:    type: Public    ports:    - protocol: TCP      port: 8080type: Microsoft.ContainerInstance/containerGroups

using Microsoft.Azure.Management.Media;using Microsoft.Azure.Management.Media.Models;using System.Threading.Tasks;public class SimaBitAzureMediaServices{    private readonly IAzureMediaServicesClient _client;    private readonly string _resourceGroupName;    private readonly string _accountName;        public async Task<Job> CreateSimaBitEncodingJob(        string inputAssetName,         string outputAssetName)    {        // Create transform with SimaBit preprocessing        var transformName = "SimaBitH264Transform";                var transform = await _client.Transforms.CreateOrUpdateAsync(            _resourceGroupName,            _accountName,            transformName,            new Transform## Frequently Asked Questions### How does SimaBit reduce CDN costs by 22% in cloud-native encoding pipelines?SimaBit leverages advanced AI-powered video compression techniques to significantly reduce bitrates while maintaining visual quality. By optimizing encoding parameters and utilizing perceptual optimization methods, SimaBit can compress video content more efficiently than traditional codecs. This reduction in file sizes directly translates to lower CDN bandwidth usage and storage costs, achieving the documented 22% cost reduction through improved compression ratios and reduced data transfer requirements.### What are the key challenges with high-resolution video streaming costs in 2025?The growing demand for UltraHD and 4K content requires significantly higher bitrates, making it challenging to provide high-resolution content at scale. CDN bills that once represented 15-20% of operational expenses now consume 30-40% of budgets due to increased bandwidth requirements. Additionally, limitations in last-mile bandwidth and CDN storage capacity create bottlenecks, while lower bitrates often result in visually perceptible coding artifacts that degrade user experience.### How does AI-powered video codec technology improve bandwidth reduction for streaming?AI-powered video codecs like SimaBit use machine learning algorithms to analyze video content and optimize compression in real-time. These systems employ advanced techniques such as rate-perception optimized preprocessing and adaptive encoding parameters to maintain essential high-frequency components while reducing overall bitrate. The AI algorithms can identify which parts of the video are most important to human perception and allocate bits more efficiently, resulting in superior compression performance compared to traditional encoding methods.### What encoding optimizations work best for high-motion sports videos at low bitrates?High-motion sports videos require specialized encoding techniques due to their complex temporal characteristics. Effective optimizations include joint backward and forward temporal masking for perceptually optimized encoding, adaptive bitrate streaming to handle poor network connectivity, and advanced motion estimation algorithms. These techniques help maintain visual quality even at reduced bitrates, which is crucial since sports streaming is expected to grow from an $18 billion market in 2020 to $87 billion by 2028.### How can cloud-native architectures improve video encoding pipeline efficiency?Cloud-native video encoding pipelines leverage containerization, microservices, and auto-scaling to optimize resource utilization and reduce costs. By deploying encoding workloads using technologies like Kubernetes and Terraform, organizations can dynamically scale processing capacity based on demand. This approach enables efficient handling of variable workloads, reduces infrastructure overhead, and allows for better integration with CDN services and storage systems, ultimately improving the overall cost-effectiveness of video delivery.### What role do modern video codecs like SVT-AV1 play in reducing streaming costs?Modern codecs like SVT-AV1 2.0.0 offer significant improvements in compression efficiency compared to older standards. These next-generation codecs provide better rate-distortion performance, meaning they can achieve the same visual quality at lower bitrates or better quality at the same bitrates. The latest updates include improved API functionality, better encoding modes, and enhanced performance optimizations that make them more suitable for production environments, directly contributing to reduced bandwidth costs and improved streaming economics.## Sources1. [https://arxiv.org/pdf/2207.05798.pdf](https://arxiv.org/pdf/2207.05798.pdf)2. [https://dev.to/dvsharma/deploying-a-microservices-stock-trading-application-on-aws-with-terraform-kubernetes-helm-3idn](https://dev.to/dvsharma/deploying-a-microservices-stock-trading-application-on-aws-with-terraform-kubernetes-helm-3idn)3. [https://github.com/HandBrake/HandBrake/pull/5858](https://github.com/HandBrake/HandBrake/pull/5858)4. [https://github.com/singularbit/sandbox-aws-api](https://github.com/singularbit/sandbox-aws-api)5. [https://www.sima.live/blog/understanding-bandwidth-reduction-for-streaming-with-ai-video-codec](https://www.sima.live/blog/understanding-bandwidth-reduction-for-streaming-with-ai-video-codec)6. [https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12226/1222602/Joint-backward-and-forward-temporal-masking-for-perceptually-optimized-x265/10.1117/12.2624774.short?SSO=1](https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12226/1222602/Joint-backward-and-forward-temporal-masking-for-perceptually-optimized-x265/10.1117/12.2624774.short?SSO=1)