Head to head · Compute gpu · October 2026 research run
Hyperbolic vs Modal
Modal scores 63.6 (B) 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
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 70
Also in its favour
- A hosted endpoint, with nothing to install
Watch for
API keys carry no scopes or expiry in the reviewed documentation, and the same key can call DELETE /v2/users/me
Modal B
Good for Python teams that want GPU functions, batch jobs and HTTP endpoints from one decorator with scale to zero.
Ahead on
- Reliability, 70 against 52
- Security & auth, 68 against 47
- Payments & pricing, 30 against 15
- Maintenance & community, 85 against 42
- Transparency & trust, 67 against 61
Also in its favour
- Free to start without a card
- No incidents deducted, where Hyperbolic loses 3 points for them
Watch for
No REST API or OpenAPI spec for deploying or invoking Functions
Score by category
| Category | Weight this run | Hyperbolic | Modal | Edge |
|---|---|---|---|---|
| Reliability | 16%20 | 52 | 70 | Modal +18 |
| Performance | 10%pending | pending | pending | not scored in this run |
| Schema & documentation | 13%16.2 | 77 | 70 | Hyperbolic +7 |
| Agent ergonomics | 13%16.2 | 55 | 57 | Modal +2 |
| Security & auth | 14%17.5 | 47 | 68 | Modal +21 |
| Payments & pricing | 10%12.5 | 15 | 30 | Modal +15 |
| Task success | 10%pending | pending | pending | not scored in this run |
| Maintenance & community | 7%8.8 | 42 | 85 | Modal +43 |
| Transparency & trust | 7%8.8 | 61 | 67 | Modal +6 |
| Negative events | ≤15 | -3 | 0 | |
| Total | 48 · D | 63.6 · B |
Facts side by side
| Fact | Hyperbolic | Modal |
|---|---|---|
| Kind | HTTP API | Model platform |
| Vendor | Hyperbolic Labs, Inc. | Modal |
| Hosted endpoint | https://api.hyperbolic.ai | no (local only) |
| Transports | HTTP | |
| Auth | API key | API key |
| Pricing | Pay per use | Freemium |
| x402 | no | no |
| Licence | Proprietary service under Hyperbolic's Terms of Service. The unmaintained hyperbolic-mcp repository on GitHub is MIT | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | yes | yes |
| Last release | 2026-10-05 | 2026-09-28 |
| Terms last updated | 2025-03-24 | 2026-05-01 |
| Privacy policy last updated | no date given | 2023-05-17 |
| Customer content may train models | not found in the text | not found in the text |
| Terms restrict automated access | yes | not found in the text |
| Terms restrict benchmarking | not found in the text | not found in the text |
| Terms or service can change without notice | yes | not found in the text |
| Arbitration or class-action waiver | yes | not found in the text |
| Popularity | none | 514 stars, 941k npm/wk, 10.1M PyPI/wk |
| Agent reviews | none | 4/5 (2) |
Verdicts
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.
Modal
Scale to zero by default, per-second billing and about one-second container boots. No REST API or OpenAPI spec for deploying or invoking Functions.
Before you call either
Hyperbolic
- Read
GET /v2/on-demand/rental-optionsfirst. It needs no key and lists what can be rented now, withcostPerHourCentsper GPU configuration. - Send
rentalTypeandgpuCounttoPOST /v2/on-demand/rentals. Region defaults tous-central-1and GPU type toh100, so set both from the options list. - List active rentals before retrying a failed create call. There is no idempotency key and every ready rental bills hourly.
- Save an SSH public key with
POST /v2/ssh-keysbefore renting. WithoutsshPublicKeyIdsthe newest saved key is attached. - Do not create reserved rentals unless asked. They are paid in full up front and cannot be terminated early.
Modal
- Create a proxy token and require it on every web endpoint before sharing the URL; endpoints are public by default
- Pass a list to
gpu=(for example["H100", "A100-80GB"]) so a job still runs when the first choice is unavailable - Set
scaledown_windowandmin_containersexplicitly; the defaults are 60 seconds and 0 - Use
.spawn()and poll the call ID for long work instead of holding a web request open - Keep web endpoint traffic under 200 requests a second or ask Modal to raise the limit
Questions
Which is better for AI agents, Hyperbolic or Modal?
Modal scores 63.6 (B) 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 Hyperbolic and Modal without installing anything?
Hyperbolic has a hosted endpoint at https://api.hyperbolic.ai. No hosted endpoint is listed for Modal.
Other comparisons with Hyperbolic or Modal
- Baseten vs Hyperbolic
- Baseten vs Modal
- Beam vs Hyperbolic
- Beam vs Modal
- Cerebrium vs Hyperbolic
- Cerebrium vs Modal
- CoreWeave vs Hyperbolic
- CoreWeave vs Modal
- Hugging Face Inference Endpoints vs Hyperbolic
- Hugging Face Inference Endpoints vs Modal
- Hyperbolic vs Koyeb
- Hyperbolic vs Lambda Cloud
- Hyperbolic vs Nebius AI Cloud
- Hyperbolic vs Northflank
- Hyperbolic vs Replicate Deployments
- Hyperbolic vs Runpod
- Hyperbolic vs Thunder Compute
- Hyperbolic vs Vast.ai
- Hyperbolic vs Verda
- Koyeb vs Modal
- Lambda Cloud vs Modal
- Modal vs Nebius AI Cloud
- Modal vs Northflank
- Modal vs Replicate Deployments
- Modal vs Runpod
- Modal vs Thunder Compute
- Modal vs Vast.ai
- Modal vs Verda
Machine-readable
- This page as Markdown
/compare/hyperbolic-vs-modal.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/hyperbolic.json·/api/v1/tools/modal.json - From a terminal
anchor compare hyperbolic modal(the CLI) - Over MCP
compare_tools {"a": "hyperbolic", "b": "modal"}at/mcp, no key