AWS Lambda vs Lambda Labs vs RunPod: Serverless GPU Comparison

By Serverless Servants AI Team
•
June 22, 2025
•
8 min read

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Serverless GPU providers like AWS Lambda, Lambda Labs, and RunPod offer on-demand access to powerful computing resources without infrastructure management. This comparison examines their performance, pricing, and suitability for AI training, inference, and machine learning workloads.

Serverless GPU Performance Comparison Chart

Understanding Serverless GPU Providers

Serverless GPU platforms provide instant access to NVIDIA GPUs without managing servers. Key features include:

  • Pay-per-second billing
  • Automatic scaling
  • Pre-configured ML environments
  • Cold start management
  • Integrated developer tools

Serverless GPUs Explained Simply

Imagine needing a powerful gaming computer:

  • AWS Lambda: Like renting a gaming console by the minute – quick access but limited power
  • Lambda Labs: Like a gaming cafe with high-end PCs – powerful options at reasonable prices
  • RunPod: Like a custom-built gaming rig – maximum power and flexibility when you need it

All deliver gaming power without buying expensive equipment!

Head-to-Head Comparison

FeatureAWS LambdaLambda LabsRunPod
Max GPU Memory10GB (G4dn)80GB (A100)80GB (A100)
Max vCPUs6 vCPUs96 vCPUs128 vCPUs
Cold Start Time2-10 seconds15-60 seconds30-90 seconds
Pricing (A100/hr)$2.50+$1.10$0.99
Persistent StorageLimited (10GB)1TB+ optionsUnlimited (S3-like)
Prebuilt TemplatesBasicExtensive ML stackCustomizable
Max Runtime15 minutesUnlimitedUnlimited
Free Tier1M requests$10 credit$15 credit

Provider Deep Dive

AWS Lambda (GPU Support)

Best for: Short-duration inference, event-driven AI applications

GPU Options: NVIDIA T4 (AWS Graviton), up to 10GB VRAM

Key Features:

  • Tight integration with AWS ecosystem (S3, API Gateway)
  • Sub-second billing increments
  • Provisionsed Concurrency for cold start reduction
  • Automatic scaling based on requests

Limitations: 15-minute max execution time, limited GPU memory, no multi-GPU support

Learn more about AWS Lambda GPU setup

Lambda Labs

Best for: AI research, training medium-sized models, GPU-intensive workloads

GPU Options: A100 (40GB/80GB), H100, RTX 6000/8000

Key Features:

  • 1-click Jupyter Notebook environments
  • Spot instances for 70% cost reduction
  • Persistent storage options
  • Team collaboration features

Limitations: Less enterprise-grade security, limited global regions

Top Open Source Tools To Monitor Serverless GPU Workloads – Serverless Saviants

RunPod

Best for: Large model training, batch processing, long-running workloads

GPU Options: A100 (40GB/80GB), A6000, H100, multi-GPU clusters

Key Features:

  • Custom container support
  • Unlimited runtime duration
  • Community templates marketplace
  • Webhooks and API triggers
  • Dedicated GPU instances

Limitations: Steeper learning curve, longer cold starts

Guide to training models with serverless GPUs

Performance Benchmarks

ResNet-50 Inference (Images/sec)

  • AWS Lambda: 78 images/sec (T4 GPU)
  • Lambda Labs: 210 images/sec (A100 40GB)
  • RunPod: 225 images/sec (A100 80GB)

GPT-2 Fine-Tuning (Time to complete)

  • AWS Lambda: Not feasible (15-min limit)
  • Lambda Labs: 42 minutes (A100 40GB)
  • RunPod: 38 minutes (A100 80GB)

Cold Start Latency

  • AWS Lambda: 1.8s (with provisioned concurrency)
  • Lambda Labs: 23s (average)
  • RunPod: 45s (average)

Pricing Comparison

GPU TypeAWS LambdaLambda LabsRunPod
T4 (16GB)$0.000231/sec$0.60/hr$0.55/hr
A100 (40GB)N/A$1.10/hr$0.99/hr
A100 (80GB)N/A$1.50/hr$1.35/hr
H100 (80GB)N/A$4.50/hr$3.99/hr
Storage (per GB/month)$0.10$0.05$0.03

Note: AWS Lambda charges per request and duration, while Lambda Labs and RunPod charge per second of GPU time. For bursty workloads, Lambda can be more cost-effective, while for sustained workloads, Lambda Labs and RunPod offer better value.

Ideal Use Cases

🤖

Real-time Inference

AWS Lambda excels for low-latency AI predictions with its quick cold starts and integration with API Gateway.

🧠

Model Training

Lambda Labs and RunPod are better for training tasks requiring hours of GPU time and large memory.

🖼️

Image/Video Processing

RunPod’s persistent storage and powerful GPUs handle batch media processing efficiently.

🔬

Research & Development

Lambda Labs’ Jupyter environment is ideal for experimental AI research workflows.

Recommendations

Choose AWS Lambda If:

  • You need sub-second response times
  • Your workload fits within 15-minute limits
  • You’re already invested in AWS ecosystem
  • You need automatic scaling

Choose Lambda Labs If:

  • You need powerful GPUs at low cost
  • You prefer pre-configured ML environments
  • You need medium-duration workloads (hours)
  • Collaboration features are important

Choose RunPod If:

  • You need multi-GPU support
  • You have long-running workloads (days+)
  • You require custom containers
  • Cost optimization is critical

Getting Started Tips

For AWS Lambda

# Sample serverless.yml for GPU Lambda
functions:
  infer:
    handler: handler.predict
    timeout: 900 # 15 minutes max
    memorySize: 10240 # Required for GPU
    environment:
      CUDA_VISIBLE_DEVICES: 0

For Lambda Labs

  • Use spot instances for 70% savings on non-urgent jobs
  • Leverage their TensorFlow/PyTorch templates
  • Mount cloud storage for datasets

For RunPod

  • Start with community templates
  • Use their CLI for automation
  • Enable auto-scaling for batch jobs

The Future of Serverless GPUs

Emerging trends in serverless GPU space:

  • Cold start improvements: Predictive pre-warming
  • Multi-cloud deployments: Avoiding vendor lock-in
  • Specialized hardware: TPU and AI accelerator support
  • Edge GPU deployments: Low-latency inference

By 2026, serverless GPU adoption is expected to grow 300% as more AI workloads shift from traditional cloud instances to on-demand serverless platforms.

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