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DeepInfra vs RunPod

DeepInfraRunPod

Bottom line: DeepInfra for cost-sensitive developers; RunPod for mL engineers serving models.

DeepInfraDeepInfra

Cheapest serverless inference for open-source LLMs, pay per token

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RunPodRunPod

GPU cloud for training and serverless AI inference with zero egress fees

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Votes00
PricingPaidPaid
CategoryAi InfrastructureAi Infrastructure
Tags
serverless-inferencellm-apiopen-source-modelsgpu-rentalpay-per-token
gpu-cloudserverless-gpuinferencemodel-trainingcompute
Best for
  • Cost-sensitive developers
  • Startups scaling inference
  • Batch workloads
  • ML engineers serving models
  • Cost-conscious training workloads
  • Startups needing on-demand GPUs
Pros
  • Among the lowest per-token prices
  • Pay only for tokens, no idle charges
  • OpenAI-compatible API for easy migration
  • Discounted batch inference
  • Latency tiers to trade cost vs speed
  • Wide GPU selection from RTX 4090 to H100
  • Serverless endpoints scale to zero
  • Per-second billing for active execution
  • No data ingress or egress fees
  • Sub-200ms serverless cold starts
Cons
  • Focused on open-source, not proprietary models
  • No free plan
  • Latency and reliability vary by tier
  • Fewer enterprise features than large clouds
  • No self-hosting
  • Pure pay-as-you-go with no free tier
  • Spot capacity can be interrupted
  • Availability of specific GPUs varies by region
  • Requires familiarity with Docker and ML tooling
  • No managed model catalog like some competitors

Comparison generated from each tool's listing. Add or remove tools above to change it.