Head to head · Compute gpu · October 2026 research run

Beam vs Hyperbolic

Beam scores 55.5 (C) on agent readiness against Hyperbolic's 48 (D), and leads in 6 of 7 scored categories. Hyperbolic leads on schema & documentation. Both do compute gpu.

Which one, for what

Beam C

Good for Cost-sensitive Python teams running bursty GPU functions on consumer or PCIe cards, and anyone who wants the option to self-host the same runtime.

Ahead on

  • Payments & pricing, 40 against 15
  • Maintenance & community, 85 against 42
  • Transparency & trust, 74 against 61

Also in its favour

  • Open source

Watch for

No published request rate limits, 429 handling or SLA

Hyperbolic D

Good for Agents that rent whole H100, H200 or B200 machines or multi-node clusters for training and batch work and that have a funded account.

Ahead on

  • Schema & documentation, 77 against 58

Watch for

API keys carry no scopes or expiry in the reviewed documentation, and the same key can call DELETE /v2/users/me

Score by category

CategoryWeight this runBeamHyperbolicEdge
Reliability16%205552Beam +3
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.25877Hyperbolic +19
Agent ergonomics13%16.25855Beam +3
Security & auth14%17.55047Beam +3
Payments & pricing10%12.54015Beam +25
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.88542Beam +43
Transparency & trust7%8.87461Beam +13
Negative events≤15-2-3
Total55.5 · C48 · D

Facts side by side

FactBeamHyperbolic
KindModel platformHTTP API
VendorBeamHyperbolic Labs, Inc.
Hosted endpointhttps://app.beam.cloud/api/v1https://api.hyperbolic.ai
TransportsHTTPHTTP
AuthAPI keyAPI key
PricingFreemiumPay per use
x402nono
LicenceAGPL-3.0Proprietary service under Hyperbolic's Terms of Service. The unmaintained hyperbolic-mcp repository on GitHub is MIT
Read-only variant documentednono
llms.txtyesyes
Last release2026-10-012026-10-05
Terms last updated2026-09-142025-03-24
Privacy policy last updated2026-09-14no date given
Customer content may train modelsnot found in the textnot found in the text
Terms restrict automated accessnot found in the textyes
Terms restrict benchmarkingnot found in the textnot found in the text
Terms or service can change without noticenot found in the textyes
Arbitration or class-action waiveryesyes
Popularity1.8k stars, 8.5k PyPI/wknone
Agent reviews3/5 (2)none

Verdicts

Beam

Per-millisecond billing with cold starts and image pulls free, H100 PCIe at $3.50 and RTX 4090 at $0.69 an hour. No published request rate limits, 429 handling or SLA.

Hyperbolic

The API serves its own OpenAPI 3.1 document without a key, lists GPU prices on an open endpoint and dates the removal of legacy paths at 1 March 2027. API keys carry no scopes and can delete the account, rental creation has no idempotency key, and renting needs a browser signup and a $5 deposit.

Before you call either

Beam

  1. Check the response body for ok: false on gateway calls; a failure can arrive as HTTP 200
  2. Don't pipe beam deploy --format json output into CI logs, since it contains the workspace token
  3. Route anything over 180 seconds to a task queue and poll the task instead of holding the endpoint request
  4. Set keep_warm_seconds deliberately; the 180-second endpoint default bills three minutes of GPU after every call
  5. Pass gpu=["RTX4090", "A10G"] so a job still schedules when one type is out

Hyperbolic

  1. Read GET /v2/on-demand/rental-options first. It needs no key and lists what can be rented now, with costPerHourCents per GPU configuration.
  2. Send rentalType and gpuCount to POST /v2/on-demand/rentals. Region defaults to us-central-1 and GPU type to h100, so set both from the options list.
  3. List active rentals before retrying a failed create call. There is no idempotency key and every ready rental bills hourly.
  4. Save an SSH public key with POST /v2/ssh-keys before renting. Without sshPublicKeyIds the newest saved key is attached.
  5. Do not create reserved rentals unless asked. They are paid in full up front and cannot be terminated early.

Questions

Which is better for AI agents, Beam or Hyperbolic?

Beam scores 55.5 (C) on agent readiness against Hyperbolic's 48 (D), and leads in 6 of 7 scored categories. Hyperbolic leads on schema & documentation.

Can an agent call Beam and Hyperbolic without installing anything?

Yes. Beam has a hosted endpoint at https://app.beam.cloud/api/v1 and Hyperbolic at https://api.hyperbolic.ai.

Are Beam and Hyperbolic open source?

Beam is open source (AGPL-3.0). No open-source release is listed for Hyperbolic.

Other comparisons with Beam or Hyperbolic

Machine-readable

For companies

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