John Cyriac

Technical Architect specializing in Kubernetes, distributed databases, and cloud infrastructure. 13k+ hours of consulting experience via Upwork.

6 minute read

Recently, I deployed the same AI model (ByteDance’s LatentSync) on both Paperspace Gradient and Google Cloud Platform to compare these platforms for production ML workloads. This post shares my real-world experience, including platform limitations I encountered and the workarounds I developed.

The Challenge: Deploying LatentSync at Scale

LatentSync is a GPU-intensive lip-sync model that requires:

  • GPU Resources: NVIDIA A100 or L4 GPUs for reasonable performance
  • Model Storage: 5GB+ model weights that need efficient loading
  • File Processing: Input/output handling for video and audio files
  • API Interface: RESTful endpoints for production integration

I wanted to compare how two different platforms handle these requirements.

Platform Overview

Paperspace Gradient

Paperspace specializes in GPU-accelerated infrastructure for AI/ML workloads. Gradient is their managed ML platform - think AWS SageMaker but with a focus on simplicity and GPU accessibility.

Positioning:

  • Specialized AI infrastructure provider
  • Developer-friendly GPU access
  • Limited geographical availability (vs AWS/GCP)
  • Simpler pricing model

Google Cloud Platform

GCP offers comprehensive cloud services with strong AI/ML capabilities through Vertex AI and Cloud Run.

Positioning:

  • Full-service cloud provider
  • Global infrastructure
  • Enterprise-grade reliability
  • Complex but flexible pricing

Platform Limitations Discovered

Paperspace: Infrastructure Tooling Gaps

Terraform Provider Issues:

# The official Terraform provider hasn't been updated in 2+ years
# This makes Infrastructure as Code challenging
terraform {
  required_providers {
    paperspace = {
      source = "Paperspace/paperspace"
      # Last update: 2022 - many features missing
    }
  }
}

CLI Deprecation:

# The gradient CLI is deprecated
gradient deployments create --help
# Command not found or deprecated

# New paperspace CLI is disconnected from documentation
paperspace deployments create --help  
# Features don't match what's documented

Volume Management:

  • No visible way to add volumes to deployments via UI
  • HuggingFace model integration creates read-only volumes
  • Can’t download additional models at runtime with read-only mounts

GCP: Complexity but Flexibility

Terraform State Management:

# More setup required but much more reliable
export TF_STATE_BUCKET="${PROJECT_ID}-terraform-state"
gsutil mb -l ${REGION} gs://${TF_STATE_BUCKET}
gsutil versioning set on gs://${TF_STATE_BUCKET}

Resource Quotas:

# GPU quota requests take time but are predictable
# Need to plan ahead for production scaling
gcloud compute project-info describe --project=${PROJECT_ID}

Workaround Strategy: Public Storage Integration

Since Paperspace’s volume management was limited, I developed a strategy using public GCS buckets:

Storage Architecture

#!/bin/bash
# Create public buckets for Paperspace integration
PROJECT_ID="your-project-id"

# Input bucket (public read)
gcloud storage buckets create gs://$PROJECT_ID-latentsync-pspace-in \
  --uniform-bucket-level-access

# Output bucket (public read/write)  
gcloud storage buckets create gs://$PROJECT_ID-latentsync-pspace-out \
  --uniform-bucket-level-access

# Make buckets publicly accessible
gcloud storage buckets add-iam-policy-binding gs://$PROJECT_ID-latentsync-pspace-in \
  --member=allUsers \
  --role=roles/storage.objectViewer

gcloud storage buckets add-iam-policy-binding gs://$PROJECT_ID-latentsync-pspace-out \
  --member=allUsers \
  --role=roles/storage.objectViewer

File Upload Automation

#!/bin/bash
# Upload files and generate URLs for processing
VIDEO_FILE="$1"
AUDIO_FILE="$2"
JOB_ID="job-$(date +%Y%m%d-%H%M%S)"

# Upload to GCS
gsutil cp "${VIDEO_FILE}" "gs://${INPUT_BUCKET}/${JOB_ID}/$(basename ${VIDEO_FILE})"
gsutil cp "${AUDIO_FILE}" "gs://${INPUT_BUCKET}/${JOB_ID}/$(basename ${AUDIO_FILE})"

# Generate public URLs
VIDEO_URL="https://storage.googleapis.com/${INPUT_BUCKET}/${JOB_ID}/$(basename ${VIDEO_FILE})"
AUDIO_URL="https://storage.googleapis.com/${INPUT_BUCKET}/${JOB_ID}/$(basename ${AUDIO_FILE})"
OUTPUT_URL="https://storage.googleapis.com/${OUTPUT_BUCKET}/${JOB_ID}/output.mp4"

Application Architecture Comparison

Paperspace: FastAPI with Job Queue

Since Paperspace deployments have limitations, I built a more sophisticated wrapper:

from fastapi import FastAPI, BackgroundTasks
import uuid
import tempfile
import requests

app = FastAPI(title="LatentSync API (Paperspace Version)")

@app.post("/jobs", status_code=202)
async def create_job(
    background_tasks: BackgroundTasks,
    job_request: GcsJobRequest
):
    """Create job with URL-based file handling"""
    job_id = str(uuid.uuid4())
    
    # Queue background processing
    background_tasks.add_task(
        process_gcs_job,
        job_id,
        job_request.video_in,
        job_request.audio_in,
        job_request.out
    )
    
    return {"job_id": job_id, "status": "processing"}

async def process_gcs_job(job_id: str, video_in: str, audio_in: str, out_path: str):
    """Download from HTTP URLs, process, upload results"""
    
    with tempfile.TemporaryDirectory() as temp_dir:
        # Download inputs via HTTP
        video_response = requests.get(video_in, stream=True)
        audio_response = requests.get(audio_in, stream=True)
        
        # Process with LatentSync
        cmd = [
            "python", "-m", "scripts.inference",
            "--video_path", local_video_path,
            "--audio_path", local_audio_path,
            "--video_out_path", local_output_path
        ]
        subprocess.run(cmd, check=True)
        
        # Upload result via HTTP PUT
        with open(local_output_path, 'rb') as f:
            requests.put(out_path, data=f, headers={'Content-Type': 'video/mp4'})

GCP: Simpler Integration

from google.cloud import storage
import tempfile

def handle_job(job_data: Dict[str, Any]) -> Dict[str, Any]:
    """Direct GCS integration with service account auth"""
    
    with tempfile.TemporaryDirectory() as temp_dir:
        # Download from GCS with native client
        download_from_gcs(job_data["video_in"], local_video_path)
        download_from_gcs(job_data["audio_in"], local_audio_path)
        
        # Process with LatentSync (same)
        process_video(local_video_path, local_audio_path, local_output_path)
        
        # Upload to GCS with native client
        upload_to_gcs(local_output_path, job_data["out"])

Deployment Process Comparison

Paperspace: Manual UI Process

# Paperspace Dockerfile
FROM nvidia/cuda:12.4.0-runtime-ubuntu22.04

WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .

# Install additional dependencies for HTTP handling
RUN pip install fastapi==0.115.12 uvicorn[standard]==0.34.2

# Embed model weights in container (no volume support)
RUN mkdir -p /app/checkpoints/whisper
ENV DATA_DIR=/app/data
ENV WEIGHTS_DIR=/app/checkpoints
ENV PORT=8080

CMD ["python", "main.py"]

Deployment Steps:

  1. Build and push Docker image manually
  2. Use Paperspace web UI to create deployment
  3. Select GPU type (A100)
  4. Configure scaling settings
  5. Set port to 8080
  6. Deploy and wait

GCP: Infrastructure as Code

# Terraform configuration
resource "google_cloud_run_v2_service" "latentsync" {
  name     = "latentsync-production"
  location = var.region

  template {
    scaling {
      min_instance_count = 0
      max_instance_count = 10
    }
    
    containers {
      image = var.container_image
      
      resources {
        limits = {
          cpu    = "4"
          memory = "16Gi"
          "nvidia.com/gpu" = "1"
        }
      }
    }
  }
}

Deployment Steps:

# Automated deployment
terraform init
terraform plan
terraform apply

Performance and Cost Analysis

GPU Performance Comparison

Paperspace A100:

  • Performance: ~60 seconds per video
  • Availability: Good in supported regions
  • Cold Start: ~45 seconds (container + model loading)

GCP A100-40G:

  • Performance: ~90 seconds per video
  • Availability: Excellent globally
  • Cold Start: ~30 seconds (faster storage)

GCP L4:

  • Performance: ~180 seconds per video
  • Availability: Excellent globally
  • Cold Start: ~25 seconds

Cost Analysis (Approximate)

Paperspace:

  • A100 GPU: ~$2.30/hour
  • No additional storage costs (embedded in container)
  • Simple hourly billing

GCP:

  • A100-40G: ~$3.67/hour (Cloud Run)
  • L4: ~$0.73/hour (Cloud Run)
  • Additional costs: Storage, networking, logging
  • Pay-per-second billing

Cost per Video (Processing + Overhead):

  • Paperspace A100: ~$0.06/video
  • GCP A100: ~$0.12/video
  • GCP L4: ~$0.05/video

Production Readiness Assessment

Paperspace Strengths

✅ Simplicity: Easy GPU access for developers
✅ Cost: Competitive GPU pricing
✅ Focus: AI/ML optimized platform

Paperspace Limitations

❌ Infrastructure as Code: Limited Terraform support
❌ Tooling: CLI deprecation, UI-only workflows
❌ Storage: Volume management constraints
❌ Scaling: Less flexible auto-scaling options
❌ Monitoring: Basic observability features

GCP Strengths

✅ Infrastructure as Code: Mature Terraform support
✅ Global Scale: Worldwide availability
✅ Enterprise Features: Comprehensive monitoring, logging, security
✅ Integration: Native storage, networking, ML services
✅ Auto-scaling: Sophisticated scaling policies

GCP Limitations

❌ Complexity: Steeper learning curve
❌ Cost: Can be more expensive with full feature set
❌ GPU Availability: Quota requests required

API Usage Examples

Paperspace Deployment

# Submit job to Paperspace deployment
curl -X POST https://some-id.paperspacegradient.com/jobs \
  -H "Content-Type: application/json" \
  -d '{
    "video_in": "https://storage.googleapis.com/bucket/demo_video.mp4",
    "audio_in": "https://storage.googleapis.com/bucket/demo_audio.wav", 
    "out": "https://storage.googleapis.com/bucket/output.mp4",
    "guidance_scale": 2.0,
    "inference_steps": 20
  }'

# Response
{
  "job_id": "ffd0de73-54a4-45f9-b8a6-af2310052b41",
  "status": "processing",
  "created_at": "2025-05-21T07:44:21.206160",
  "_links": {
    "self": "/jobs/ffd0de73-54a4-45f9-b8a6-af2310052b41",
    "log": "/jobs/ffd0de73-54a4-45f9-b8a6-af2310052b41/log"
  }
}

GCP Cloud Run

# Submit job to GCP Cloud Run
TOKEN=$(gcloud auth print-identity-token)
curl -X POST https://latentsync-service-url/process \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "video_in": "gs://bucket/demo_video.mp4",
    "audio_in": "gs://bucket/demo_audio.wav",
    "out": "gs://bucket/output.mp4"
  }'

Decision Framework

Choose Paperspace When:

  • Prototyping and experimentation phase
  • Simple deployments without complex infrastructure needs
  • Cost sensitivity for GPU compute
  • Small team without DevOps expertise
  • Short-term projects with manual management acceptable

Choose GCP When:

  • Production deployments requiring enterprise features
  • Infrastructure as Code is mandatory
  • Global availability needed
  • Complex integrations with other cloud services
  • Team expertise in cloud-native technologies
  • Long-term scalability and maintenance considerations

Lessons Learned

1. Platform Maturity Matters

Paperspace’s focus on AI/ML is appealing, but gaps in infrastructure tooling create operational challenges at scale.

2. Workarounds Have Costs

My public GCS bucket workaround for Paperspace added complexity and potential security concerns.

3. Developer Experience vs Production Needs

Paperspace excels at developer experience but falls short on production operational requirements.

4. Total Cost of Ownership

While Paperspace has lower compute costs, operational overhead can increase total project costs.

5. Lock-in Considerations

GCP’s comprehensive tooling creates more lock-in but also provides more capabilities.

Conclusion

Both platforms have their place in the ML deployment ecosystem:

Paperspace is excellent for research, prototyping, and simple production deployments where developer velocity matters more than operational sophistication.

GCP is better for enterprise production deployments requiring comprehensive infrastructure management, global scale, and integration with broader cloud ecosystems.

For my LatentSync deployment, I ultimately chose GCP for production due to:

  • Superior Infrastructure as Code support
  • More sophisticated auto-scaling and monitoring
  • Global availability and enterprise-grade reliability
  • Comprehensive cost management tools

However, I continue to use Paperspace for rapid experimentation and proof-of-concept work where its simplicity shines.

The complete implementation code for both platforms is available in my repositories:

Evaluating cloud platforms for AI model deployment? I’m available for MLOps consulting through Upwork and can help you choose the right platform for your specific requirements.

Updated:

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