deploybase

LLM Hosting Providers Compared: Pricing, Latency, and Features

Deploybase · July 16, 2025 · LLM Pricing

Contents

LLM Hosting Providers: Overview

LLM Hosting Providers is the focus of this guide. Pick a hosting provider based on cost, performance, and scale needs. No single winner.

Key evaluation criteria include:

  • Per-token latency and throughput
  • Hardware availability and allocation speed
  • Billing granularity and commitment requirements
  • API compatibility and model support
  • Geographic distribution and redundancy options
  • Support for fine-tuning and custom deployments

Major Providers Comparison

RunPod

RunPod offers spot and on-demand instances with transparent pricing. The platform provides direct access to H100, H200, and B200 GPUs as of March 2026.

Strengths:

  • Competitive hourly rates
  • Flexible reservation options
  • Support for distributed inference

Considerations:

  • Spot instance availability fluctuates
  • Regional limitations in some areas
  • Custom configuration required for optimization

See GPU pricing across providers for current rates.

Lambda Labs

Lambda Labs specializes in AI workload hosting with consistent availability. Their managed service reduces operational overhead.

Strengths:

  • Dedicated customer support
  • Pre-optimized environments
  • Predictable performance

Considerations:

  • Premium pricing compared to spot alternatives
  • Less granular resource customization
  • Longer provisioning for specialized requests

Check Lambda GPU pricing for current offers.

CoreWeave

CoreWeave focuses on GPU-native infrastructure with bare metal options. Their 8xH100 configuration targets high-throughput scenarios.

Strengths:

  • Production SLA options
  • Flexible billing periods
  • Strong spot price stability

Considerations:

  • Minimum commitment requirements
  • Smaller model ecosystem compared to cloud platforms
  • Regional availability concentrated in specific zones

Review CoreWeave pricing for production packages.

AWS

AWS provides GPU instances through EC2 with broad ecosystem integration. P5 instances deliver top-tier performance scaling.

Strengths:

  • Integration with existing AWS services
  • Managed scaling and load balancing
  • Reserved instance discounts available

Considerations:

  • Higher baseline costs than specialized providers
  • Complex pricing tiers require careful calculation
  • Cold start latency for serverless options

Explore AWS GPU pricing and available instance types.

Pricing Analysis

Hourly Rates by GPU

Pricing structures vary significantly based on GPU generation and instance configuration. Spot pricing typically offers 40-60% discounts compared to on-demand rates.

H100 Single GPU:

  • RunPod on-demand: $2.69/hour (SXM), $1.99/hour (PCIe)
  • Lambda Labs: $3.78/hour (SXM), $2.86/hour (PCIe)
  • Spot pricing (RunPod): ~$1.20/hour

H200 Performance:

  • RunPod: $3.59/hour
  • Enables higher sequence lengths
  • Better for batched inference

B200 Latest Generation:

  • RunPod: $5.98/hour
  • Lambda: $6.08/hour
  • Premium positioning due to capacity constraints

Multi-GPU Configurations:

  • CoreWeave 8xH100: $49.24/hour
  • Ideal for large batch processing
  • Distributed model serving scenarios

Total cost depends on utilization patterns. Continuous workloads benefit from reserved instances. Batch processing gains from spot pricing flexibility.

Latency and Performance

Time to First Token

Time to first token (TTFT) varies by provider and configuration. GPU memory bandwidth and model quantization significantly impact this metric.

Typical ranges (batched inference):

  • Dedicated instances: 40-80ms
  • Shared environments: 100-150ms
  • Spot instances: variable, 50-120ms

Batching request volume reduces per-request overhead. Single request latency increases during peak utilization periods.

Throughput Capacity

Throughput depends on memory availability and batch size. H100 with 80GB memory supports larger batches than consumer-grade options.

Estimated tokens per second (llama-7b quantized):

  • Single H100: 500-800 tokens/second
  • Single H200: 700-1000 tokens/second
  • 8xH100 cluster: 4000-6000 tokens/second

Network latency between GPUs affects distributed inference. Providers with low-latency interconnects achieve better scaling.

Cold Start Time

Container startup overhead varies between 2-10 seconds. Providers offering keep-alive mechanisms reduce this impact for sustained workloads.

Comparison:

  • Serverless platforms: 5-10 second cold start
  • Managed containers: 2-3 second cold start
  • Reserved instances: negligible cold start

Feature Comparison

Model Ecosystem Support

All major providers support common frameworks: PyTorch, TensorFlow, ONNX, and Hugging Face models. API compatibility varies.

OpenAI-compatible APIs:

  • Lambda Labs: full compatibility
  • RunPod: community implementations available
  • CoreWeave: custom implementations required
  • AWS: SageMaker required for managed APIs

See LLM API pricing comparison for hosted alternatives.

Fine-tuning Capabilities

Fine-tuning workflows require persistent storage and checkpoint management.

Provider approaches:

  • RunPod: direct data volume access
  • Lambda: managed training environments
  • CoreWeave: bare metal flexibility
  • AWS: integrated SageMaker training

Monitoring and Observability

Real-time monitoring helps identify bottlenecks.

Available tools:

  • GPU utilization metrics
  • Memory usage tracking
  • Network bandwidth monitoring
  • Cost attribution by workload

CoreWeave and AWS provide the most comprehensive monitoring dashboards. RunPod offers basic metrics through the web interface.

FAQ

Q: Which provider offers the lowest latency for real-time inference?

A: Lambda Labs and CoreWeave deliver consistent sub-100ms latency. RunPod competitive pricing comes with higher variance. Your workload's batching strategy matters more than provider choice.

Q: What's the cost difference between on-demand and spot pricing?

A: Spot pricing typically saves 40-60% compared to on-demand rates. The tradeoff involves potential interruption risk. Spot works well for batch processing and non-critical inference.

Q: Can I migrate between providers easily?

A: Docker containers port across providers with minimal changes. Model artifacts and data require transfer time. API-based services require client application updates.

Q: Which provider scales best for distributed inference?

A: CoreWeave specializes in bare metal configurations with excellent interconnect speeds. AWS provides managed auto-scaling but at higher cost. RunPod requires manual multi-instance orchestration.

Q: How do I handle GPU memory constraints?

A: Quantization reduces memory footprint by 50-75%. Model parallelism distributes across multiple GPUs. Smaller model variants trade quality for efficiency.

Q: Are there cost savings for long-term commitments?

A: Reserved instances on AWS offer 30-50% discounts annually. RunPod and Lambda provide volume pricing for prepaid accounts. CoreWeave monthly commitments reduce hourly rates 10-20%.

Sources

  • RunPod pricing documentation, March 2026
  • Lambda Labs GPU instance specifications
  • CoreWeave production offerings
  • AWS EC2 pricing calculator
  • Industry latency benchmarks from MLPerf