Beam
by Beam · Serverless GPUs and sandboxes for inference, task queues and agents
BenchRank score
Screenshots of Beam
Homepage
Overview
Beam runs AI workloads — inference endpoints, task queues and sandboxes — on serverless GPUs, defined in Python with decorators such as @endpoint and @task_queue rather than YAML or Dockerfiles. Memory snapshots restore GPU containers for sub-second cold starts. Workloads can run on Beam's own cloud across 30+ regions, or on your AWS, GCP or Azure account for a flat management fee.
- Best for
- Teams running GPU inference, sandboxes or task queues from Python without managing infrastructure
- Pricing
- Usage-based compute billed by the millisecond (serverless RTX 4090 from $0.69/hr, on-demand H100 machines from $1.74/hr, sandboxes from $0.319/hr) on top of an $89/month Team plan that includes $30 of credit.
- Runs on
- Self-hosted
Strengths and trade-offs
Strengths
- Per-second billing; no charge for container spin-up or image load
- Runs on Beam's cloud or your own AWS, GCP or Azure account
- Hardware set in Python — one line to switch GPU type
- No egress or bandwidth fees; storage included up to 1 TB
Trade-offs
- $89/month Team plan sits on top of all compute usage charges
- Published plan caps GPU concurrency at 50 containers and logs at 30 days
- Only three seats included; extra seats are $25 each per month
- Cluster and committed-spend pricing require a sales call
How Beam markets itself
A structured read of the promise, proof and page design on Beam’s captured homepage.
Homepage capture
“Serverless GPUs and Sandboxes”
- Angle: Developer-first
- Hero: Code snippet
Pricing
Published plans and prices from Beam’s own pricing page.
How this score is made up
Each dimension is scored out of 100 and combined into the headline score using fixed weights.
MCP support
Whether an agent can drive the product through the Model Context Protocol, and how much setup that takes.
API quality
Public API surface: machine-readable spec, official SDKs, documented auth, errors, rate limits and versioning.
Documentation
Publicly reachable docs — coverage, freshness, code samples and machine readability.
Agent friendliness
How readable the site is to an automated client: llms.txt, structured data, server-rendered content, crawler access.
Pricing transparency
Whether real prices are published, self-serve signup exists, and usage costs are knowable without a sales call.
Changelog
A public, dated record of what shipped and when — the clearest signal that a product is still alive.
Marketing site structure
Whether the site answers a buyer's questions: clear positioning, the pages that matter, and accessibility.
Page speed
How fast the site loads for real visitors: Chrome UX Report 75th-percentile LCP, INP and CLS, with a Lighthouse mobile run standing in where a site has too little traffic for field data.
Operational trust
Status page and incident history, security disclosure, compliance and data-processing documentation.
Measured, but not part of the score
Useful to know, but not a mark for or against the product — so these do not affect the ranking.
Openness
Source availability, self-hosting, data export and open standards. Scored and shown, but not part of the composite — paid SaaS is not worse for being paid SaaS.
Maintenance
Release cadence and repository activity. Scored and shown, but not part of the composite — it is only measurable for open repositories.
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Where this comes from
The Beam pages BenchRank reads when it scores the product — its documentation, release notes, status and security pages, and its repository where there is one.
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