# Hugging Face Inference Endpoints vs Hyperbolic

> Hugging Face Inference Endpoints scores 64.5 (B) on agent readiness against Hyperbolic's 48 (D), and leads in 6 of 7 scored categories. Both do compute gpu. Category scores, facts, verdicts and agent notes side by side.

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- Updated: 2026-10-09

Hugging Face Inference Endpoints scores 64.5 (B) on agent readiness against Hyperbolic's 48 (D), and leads in 6 of 7 scored categories. Both do compute gpu.

- Hugging Face Inference Endpoints: grade B, 64.5/100, rank #314 of 842. Markdown https://www.anchorterminal.com/tools/hugging-face-inference-endpoints.md · JSON https://www.anchorterminal.com/api/v1/tools/hugging-face-inference-endpoints.json
- Hyperbolic: grade D, 48/100, rank #730 of 842. Markdown https://www.anchorterminal.com/tools/hyperbolic.md · JSON https://www.anchorterminal.com/api/v1/tools/hyperbolic.json

## Which one, for what

### Hugging Face Inference Endpoints (B)

Good for: Teams whose models already live on the Hugging Face Hub and who want a dedicated endpoint on a named cloud and region with standard open-source engines.

Ahead on:
- Reliability, 63 against 52
- Agent ergonomics, 62 against 55
- Security & auth, 83 against 47
- Payments & pricing, 20 against 15
- Maintenance & community, 80 against 42
- Transparency & trust, 68 against 61

Also in its favour:
- No incidents deducted, where Hyperbolic loses 3 points for them

Watch for: No free tier. The docs require a payment method and credits, and replicas are billed while initialising as well as running

### 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.

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

| Category | Weight | Hugging Face Inference Endpoints | Hyperbolic | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 63 | 52 | Hugging Face Inference Endpoints +11 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 73 | 77 | Hyperbolic +4 |
| Agent ergonomics | 13% (16.2 this run) | 62 | 55 | Hugging Face Inference Endpoints +7 |
| Security & auth | 14% (17.5 this run) | 83 | 47 | Hugging Face Inference Endpoints +36 |
| Payments & pricing | 10% (12.5 this run) | 20 | 15 | Hugging Face Inference Endpoints +5 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 80 | 42 | Hugging Face Inference Endpoints +38 |
| Transparency & trust | 7% (8.8 this run) | 68 | 61 | Hugging Face Inference Endpoints +7 |
| Negative events | ≤15 | 0 | -3 | |
| **Total** | | **64.5 · B** | **48 · D** | |

## Facts side by side

| Fact | Hugging Face Inference Endpoints | Hyperbolic |
| --- | --- | --- |
| Kind | HTTP API | HTTP API |
| Vendor | Hugging Face, Inc. | Hyperbolic Labs, Inc. |
| Hosted endpoint | `https://api.endpoints.huggingface.cloud` | `https://api.hyperbolic.ai` |
| Transports | HTTP | HTTP |
| Auth | OAuth or key | API key |
| Pricing | Pay per use | Pay per use |
| x402 | no | no |
| Licence | Proprietary service under the Hugging Face Terms of Service. The `huggingface_hub` Python client and CLI are Apache-2.0 | Proprietary service under Hyperbolic's Terms of Service. The unmaintained hyperbolic-mcp repository on GitHub is MIT |
| Tools exposed | 19 | none |
| Read-only variant documented | no | no |
| llms.txt | yes | yes |
| Last release | 2026-10-08 | 2026-10-05 |
| Terms last updated | 2022-09-15 | 2025-03-24 |
| Privacy policy last updated | 2023-03-28 | no date given |
| Customer content may train models | not found in the text | not found in the text |
| Terms restrict automated access | not found in the text | yes |
| Terms restrict benchmarking | not found in the text | not found in the text |
| Terms or service can change without notice | yes | yes |
| Arbitration or class-action waiver | not found in the text | yes |
| Popularity | 60M PyPI/wk | none |

## Verdicts

**Hugging Face Inference Endpoints.** OAuth scopes separate reading endpoints from writing them, both OpenAPI documents are public, and the unauthenticated `/v2/provider` route lists every instance with its hourly price. An account needs a payment method and credits before the first deployment, no rate limits or SLA were found for the management API, and the docs price table disagrees with the live list in places.

**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

### Hugging Face Inference Endpoints

1. Call `GET https://api.endpoints.huggingface.cloud/v2/provider` first and pick an instance whose `status` is `available`. The docs table lists types the API marks deprecated or not available
2. Send `X-Scale-Up-Timeout: 600` on requests to an endpoint that scales to zero, or handle 503 while the first replica starts
3. Set `scaleToZeroTimeout` yourself. The docs give a default of 1 hour and the OpenAPI document says 15 minutes
4. Pause or delete an endpoint when the job is done. Billing covers every minute a replica is initialising or running
5. Give the agent a fine-grained token or the `read-endpoints` scope unless it must deploy. Endpoints are private by default and take the same Hugging Face token as a bearer

### 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, Hugging Face Inference Endpoints or Hyperbolic?

Hugging Face Inference Endpoints scores 64.5 (B) on agent readiness against Hyperbolic's 48 (D), and leads in 6 of 7 scored categories.

### Do Hugging Face Inference Endpoints and Hyperbolic need an API key?

Hugging Face Inference Endpoints takes an API key or an OAuth sign-in. Hyperbolic needs an API key.

### Can an agent call Hugging Face Inference Endpoints and Hyperbolic without installing anything?

Yes. Hugging Face Inference Endpoints has a hosted endpoint at https://api.endpoints.huggingface.cloud and Hyperbolic at https://api.hyperbolic.ai.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-hyperbolic.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hugging-face-inference-endpoints-vs-hyperbolic.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "hugging-face-inference-endpoints", "b": "hyperbolic"}`. From a terminal: `anchor compare hugging-face-inference-endpoints hyperbolic`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/hugging-face-inference-endpoints.json and https://www.anchorterminal.com/api/v1/tools/hyperbolic.json

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