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AWS Fine-Tune LLM: SageMaker vs EC2 GPU Pricing

Deploybase · June 23, 2025 · Tutorials

Contents

Overview of Both Services

AWS provides two primary approaches for fine-tuning LLMs: managed SageMaker Training or self-managed EC2 instances. The choice fundamentally impacts cost, operational overhead, and flexibility.

SageMaker Training: AWS-managed machine learning service handling infrastructure, scaling, and monitoring. Developers submit training jobs through SageMaker interfaces; AWS provisions and manages hardware.

EC2 GPU Instances: Raw virtual machines with attached GPUs. Developers manage all aspects including environment setup, scaling, monitoring, and resource cleanup.

SageMaker Training Pricing

SageMaker charges based on instance type and training duration. Training jobs bill per second with one-second minimum increments.

Pricing Structure

SageMaker applies per-instance-second charges for hosted training:

ml.p3.8xlarge (8xV100): $30.65/hour

  • SageMaker overhead: 5-10% additional
  • Estimated SageMaker cost: $32.18/hour

ml.p3dn.24xlarge (8xV100 32GB): $48.48/hour

  • SageMaker overhead: 5-10% additional
  • Estimated SageMaker cost: $50.90/hour

ml.p4d.24xlarge (8xA100 SXM): $24.16/hour

  • SageMaker overhead: 5-10% additional
  • Estimated SageMaker cost: $25.37/hour

Managed Inference Endpoints

Post-training serving through SageMaker Endpoints involves additional costs:

ml.p3.2xlarge (1xV100): $1.938/hour ml.p3.8xlarge (8xV100): $7.752/hour ml.p4d.24xlarge (8xA100): $24.16/hour

Endpoints auto-scale based on demand. Reserved capacity provides 25-30% discounts on multi-month commitments.

EC2 GPU Instance Pricing

EC2 GPU instances offer greater flexibility and cost control for experienced users.

Common Instance Types

g4dn.12xlarge (4xT4): $5.68/hour

  • Cost for 24 hours training: $136.32
  • Storage: $30 standard SSD

p3.8xlarge (8xV100): $24.48/hour

  • Cost for 24 hours training: $587.52
  • Optimal for smaller fine-tuning jobs

p3dn.24xlarge (8xV100 32GB): $38.78/hour

  • Cost for 24 hours training: $930.72
  • Recommended for large model training

p4d.24xlarge (8xA100 SXM): $21.96/hour

  • Cost for 24 hours training: $526.96
  • Highest throughput available

Spot Pricing Discounts

EC2 Spot instances offer 70-90% discounts for flexible workloads:

p3dn.24xlarge Spot: $9.69/hour (75% discount)

  • Cost for 24 hours: $232.56

p4d.24xlarge Spot: $5.49/hour (75% discount)

  • Cost for 24 hours: $131.76

Spot instances can terminate with 2-minute notice. Risk acceptable for jobs saving checkpoints every 10-30 minutes.

Cost Comparison Models

Scenario 1: 24-Hour Fine-Tuning Job (Llama 2 70B)

SageMaker Approach:

  • Instance: ml.p4d.24xlarge (8xA100 SXM)
  • Duration: 24 hours
  • Cost: $25.37/hour × 24 = $608.88
  • Plus storage: $20
  • Plus SageMaker feature charges: $15
  • Total: $643.88

EC2 On-Demand Approach:

  • Instance: p4d.24xlarge
  • Duration: 24 hours
  • Cost: $21.96/hour × 24 = $526.96
  • Plus EBS storage: $20
  • Plus data transfer: $10
  • Total: $556.96

EC2 Spot Approach:

  • Instance: p4d.24xlarge Spot
  • Duration: 24 hours
  • Cost: $5.49/hour × 24 = $131.76
  • Plus storage: $20
  • Plus data transfer: $10
  • Total: $161.76

Winner: EC2 Spot saves 75% compared to SageMaker. On-demand EC2 saves 13%.

Scenario 2: Recurring Monthly Fine-Tuning (20 jobs of 8 hours each)

SageMaker Approach:

  • Monthly training cost: $25.37/hour × 160 hours = $4,059.20
  • Inference endpoint (ml.p4d.24xlarge): $24.16/hour × 730 hours = $17,636.80
  • Monthly total: $21,696

EC2 On-Demand Approach:

  • Monthly training cost: $21.96/hour × 160 hours = $3,513.60
  • Inference instances (similar): $21.96/hour × 730 = $16,030.80
  • Monthly total: $19,544.40

EC2 Spot Approach:

  • Monthly training cost: $5.49/hour × 160 hours = $878.40
  • Inference instances: $21.96/hour × 730 = $16,030.80
  • Monthly total: $16,909.20

Winner: EC2 Spot saves 22% over EC2 on-demand for the combined training+serving workload. Pure training on Spot saves 75% vs SageMaker.

Scenario 3: Continuous Fine-Tuning & Serving

SageMaker:

  • Simplifies management of continuous training
  • Auto-retraining pipelines reduce operational overhead
  • Superior monitoring and rollback capabilities

EC2:

  • Manual orchestration required
  • Requires additional DevOps effort
  • Custom monitoring and alerting

For continuous production fine-tuning, SageMaker's operational advantages partially offset higher costs. Estimated break-even: 50+ fine-tuning jobs monthly.

Performance Considerations

Training Speed

SageMaker and EC2 provide equivalent hardware, so training speeds match identically. Fine-tuning Llama 2 70B takes approximately 20-24 hours on 8xA100 configuration regardless of platform.

Startup Time

SageMaker: 3-5 minutes for job provisioning EC2: 5-10 minutes for instance startup and environment setup

Negligible difference for jobs lasting hours.

Data Access

SageMaker integrates with S3 automatically. EC2 requires manual S3 mounting.

SageMaker data transfer: Free within same region EC2 data transfer: $0.01/GB for S3 to EC2 transfer

For large training datasets (>100GB), SageMaker's free transfer saves $1,000+ monthly.

Operational Complexity

SageMaker Advantages

  • Infrastructure management eliminated
  • Automatic scaling without configuration
  • Built-in monitoring and logging
  • Simplified multi-GPU orchestration
  • Integrated with SageMaker Pipelines for workflows

EC2 Advantages

  • Complete control over environment
  • Custom optimization possible
  • No proprietary tool lock-in
  • Better for experimental configurations
  • Simpler role-based access control

Hybrid Approach

Many teams use SageMaker for production fine-tuning while developing on EC2 instances. Development reduces cost through spot instances; production uses SageMaker's reliability.

FAQ

Should I use Spot instances for important fine-tuning? Only if jobs save checkpoints every 10-30 minutes. Spot termination interrupts training; checkpointing enables resume. Most modern frameworks support checkpointing natively.

Does SageMaker support custom fine-tuning scripts? Yes. SageMaker accepts any training script via Docker containers or Python entry points. Flexibility matches EC2 while maintaining managed service benefits.

Can I use SageMaker for inference after EC2 fine-tuning? Yes. Export trained models to S3, then create SageMaker endpoints. This approach combines EC2's training cost savings with SageMaker's inference reliability.

What's included in SageMaker's 5-10% overhead? Primarily API calls, logging, and monitoring infrastructure. For large jobs, this overhead becomes negligible. For small jobs (1-2 hours), overhead percentages increase to 10-15%.

Can I optimize SageMaker costs through Reserved Instances? Partially. SageMaker supports Savings Plans providing 25-30% discounts on training instances. Reserved Instances work but require capacity commitments.

What if my fine-tuning job fails partway? SageMaker handles failures automatically with configurable retry policies. EC2 requires manual recovery. This operational advantage partially justifies SageMaker's premium pricing.

Explore broader fine-tuning concepts in RLHF fine-tuning single H100 for reinforcement learning techniques. Review best GPU for Stable Diffusion for GPU selection methodology.

Learn specific implementation details with fine-tune Llama 3 guide. Understand broader GPU pricing guide.

Sources