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Beam vs Runpod

Two fact sheets from our own research, side by side — pricing, platforms, API access and status re-checked by us rather than quoted from the vendors.

The short answer

Free plan
Neither — Beam has paid plans only, Runpod has paid plans only
Public API
Both have one
Higher Trust score
Beam — 93 vs 72 for Runpod
Best fit
Beam: Indie ML developers shipping inference endpoints without DevOps · Runpod: Developers fine-tuning or serving open models on a budget
Status
Both up at our last check — Beam Oct 16, 2026, Runpod Oct 29, 2026

From our own records as of each tool's last check. Anything we have not confirmed is left out rather than guessed. How we verify

BeamServerless GPU cloud with per-second billing for inference endpoints, training jobs and sandboxesRunpodPay-as-you-go GPU cloud for renting pods by the hour and running serverless inference endpoints that scale to zero.

Side by side

FactBeamRunpod
CategoryGPU Cloud & ComputeGPU Cloud & Compute
Pricing modelPaidPaid
Starts at$20/mo—
Public APIYesYes
Trust score93 / 10072 / 100
PlatformsWeb, APIWeb, API
CompanySmartshare, Inc.Runpod Inc.
Launched—2022
Last verifiedOct 16, 2026Oct 29, 2026

What our research says

Beam

Beam suits developers who want serverless GPU endpoints, agent sandboxes and on-demand machines behind one SDK without running their own Kubernetes. The free monthly credit is gone, so even a hobby project now starts at $20 a month, which is credited back as usage; weigh it as a startup-stage vendor whose quotas and prices move faster than the hyperscalers'.

Pros

  • Per-second serverless billing with scale-to-zero
  • Developer plan fee comes back as $20 of monthly usage credit
  • Python, TypeScript and Go SDKs plus REST access
  • BYOC mode lets existing cloud credits pay for the compute

Cons

  • No free plan; the entry plan is $20/month
  • Developer plan caps GPU concurrency at 5 containers
  • Team features gate at $89/month plus usage

Runpod

Runpod is a practical choice for indie developers, researchers and startups who want a GPU in minutes at a lower hourly rate than the hyperscalers. Teams with strict compliance needs should stick to Secure Cloud or a hyperscaler, and anyone forgetful about idle resources should watch storage charges.

Pros

  • Low hourly GPU rates with no commitment
  • Serverless endpoints scale down when idle
  • Wide range of GPUs from consumer cards to data-centre parts
  • API and templates for common AI stacks

Cons

  • Community Cloud runs on third-party hosts with variable availability
  • Storage keeps billing even when pods are stopped
  • Popular GPUs can be out of stock in a given region

Which one fits

Choose Beam if you need

  • Indie ML developers shipping inference endpoints without DevOps
  • Agent builders needing sandboxed code execution or persistent VMs
  • Teams with cloud credits who want Beam to orchestrate inside their own AWS, GCP or Azure account

Choose Runpod if you need

  • Developers fine-tuning or serving open models on a budget
  • Startups running bursty inference on serverless GPUs
  • Researchers who need a specific GPU for a few hours

Beam vs Runpod: the questions people ask

Is Beam cheaper than Runpod?

Beam starts at $20/mo. An entry price for Runpod is not confirmed by us yet, so check its pricing page before you compare.

Which has a public API, Beam or Runpod?

Both ship a public API, so either can be built on; the fact table above shows the platforms each one runs on.

More comparisons

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