Head to head · GPU compute · October 2026 research run

Hugging Face Inference Endpoints vs Together GPU Clusters

Hugging Face Inference Endpoints scores 66.7 (B) on agent readiness against Together GPU Clusters's 58.1 (C), and leads in 5 of 7 scored categories. Together GPU Clusters leads on schema & documentation. Both do gpu compute.

Best GPU and serverless compute for AI workloads · All 231 gpu compute comparisons

Which one, for what

Hugging Face Inference Endpoints B

Best 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, 73 against 38
  • Agent ergonomics, 62 against 55
  • Security & auth, 83 against 68
  • Maintenance & community, 80 against 75
  • Transparency & trust, 70 against 65

Watch for

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

Together GPU Clusters C

Best for Training, fine-tuning and batch jobs that need several 8-GPU nodes with InfiniBand, run by a pipeline that creates and deletes clusters.

Ahead on

  • Schema & documentation, 92 against 73

Watch for

Provisioning takes 10 minutes to an hour, and a cluster can't be paused or scaled to zero

Why the edge

Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.

Reliability Hugging Face Inference Endpoints +35

Hugging Face Inference Endpoints 73

Better Stack status page at status.huggingface.co with separate Inference Endpoints UI and API components and 90 days of history (20). On 2 November 2026 both components show 100 per cent uptime, with one scheduled maintenance window for H200 on 7 October 2026. The page posts incidents for other services in the window (GPU Jobs and Spaces failing to start on 24 and 25 September), and none touches Inference Endpoints since the UI outage of 16 July left the 90 days, so a clean record (30). No rate limits were found for the management API. The Hub publishes limits per 5-minute window (1,000 API requests for a free user), without saying they cover api.endpoints.huggingface.cloud (5). The docs explain the 503 returned while a replica starts and the X-Scale-Up-Timeout header that holds the request, and advise 2 replicas for availability. No backoff guidance or idempotency keys for writes (8). No SLA found in the docs or on the pricing page (0). The service is generally available (10).

Together GPU Clusters 38

Read with the hosted lines. status.together.ai lists the website, the playground and serverless models with no component for GPU clusters, and a vendor page with no component for the product takes 15 in this run, as for Together's Code Sandbox and fine-tuning listings (15). No incident history for clusters can be read (5). No rate limits for the clusters API found (0). Together's docs give 429 and 503 backoff guidance for its APIs, per the inference listing's 1 November check, and the CLI has --non-interactive and --json for scripted retries, but creation has no idempotency key (8). No SLA for clusters found; the published 99% commitment covers provisioned inference throughput only (0). Generally available since 9 September 2025 and out of the CLI's beta paths since 22 October 2026, with preemptible nodes in preview (10). 15+5+0+8+0+10 = 38.

Schema & documentation Together GPU Clusters +19

Hugging Face Inference Endpoints 73

Public OpenAPI 3.1 documents for the management API (42 paths, 48 operations on 2 November 2026) and the catalogue API (3 operations, per the 8 October check) (25). llms.txt with a Markdown twin of every docs page (10). Every operation has a summary tagged READ, WRITE, PUBLIC or PRO, but only 7 of 48 have a description. The MCP tool table states each tool's purpose and says to call get_recommended_config before create_endpoint (11). 131 typed schemas with required fields and enums for state, type and accelerator. List filters are comma-separated strings (12). Parameters carry examples. The management document lists only 200 responses on 45 of 48 operations, plus 501 on three log routes, while the catalogue document lists 400, 401, 404, 409 and 500 (7). Paths are versioned /v2 and /v3 and the document is version 2.0.0. Inference Endpoints has no changelog of its own. The Hub changelog and the docs repository history are the dated record (8).

Together GPU Clusters 92

Public OpenAPI 3.1 for /v1/compute/clusters and shared volumes, MIT, embedded in every reference page (25). llms.txt and a Markdown twin of every page (10). The docs say when to choose Kubernetes or Slurm and each capacity type, and the management page carries instructions for agents on reporting live availability and prices (18). Enums for cluster type, billing type, GPU type and status, required fields marked (13). CLI, Python, TypeScript and console steps on each task, with 402 and 403 cases documented, though no error catalogue for the clusters API (11). Versioned SDKs and a dated changelog with cluster entries (15). 25+10+18+13+11+15 = 92.

Security & auth Hugging Face Inference Endpoints +15

Hugging Face Inference Endpoints 83

OAuth through huggingface.co with read-endpoints and write-endpoints scopes, PKCE and dynamic client registration for the MCP server, and revocable fine-grained access tokens for the API. No documented option to send a token in a URL (30). Read and write scopes are separate, endpoints are private by default, organisations on Team and Enterprise plans can require approval of fine-grained tokens, and the MCP delete tool needs confirm: true. Other write calls have no confirmation step (17). The service returns the output and logs of the owner's own model, not third-party content (10). An audit log route and MCP tool on Pro, Team or Enterprise plans, plus logs and metrics for every endpoint (12). security.txt valid to 2030 with security@huggingface.co, SOC2 Type 2 stated for the Hub and Inference Endpoints, malware and pickle scanning of repositories. No bug bounty was found (14).

Together GPU Clusters 68

Project-scoped API keys, revocable with optional expiry, read as Together's other listings read the same key model (30). Admin and editor roles with no read-only role. Only admins get the admin kubeconfig, which has cluster-admin rights, and OIDC lets editors use kubectl under RBAC. Deletes run without confirmation when --non-interactive is passed (8). The API returns cluster state, not third-party content (10). Invoices itemise reserved, on-demand and storage charges, and no audit log for cluster operations was found (5). security.txt with a HackerOne route, per the 1 November check of the inference listing (15). 30+8+10+5+15 = 68.

Agent ergonomics Hugging Face Inference Endpoints +7

Hugging Face Inference Endpoints 62

List endpoints takes limit (default 20) and cursor, logs take limit, tail and line_max_length, and the MCP server has 19 tools (18). Cursor pagination, filters by state, type, task and tags, sorting, and a time window on logs (18). Errors are documented for the catalogue API and in prose for pause, resume and scale to zero. The management document has no error schema (8). No idempotency keys. Endpoints are addressed by name, updates are a PUT, and the MCP delete tool previews before a confirmed second call. MCP annotations weren't read (8). One-call deploy from the catalogue with tuned defaults, a Python client and the hf endpoints CLI. A second-language SDK for endpoint management wasn't found (10).

Together GPU Clusters 55

Cluster objects are compact, and --json gives machine-readable output, but list calls take no field selection (15). No pagination or filters on the clusters list (8). 402 and 403 with readable messages such as "Insufficient credits for cluster creation", and troubleshooting for common failures (12). Status polling until Ready is documented and deletes run non-interactively, but there is no idempotency key on creation and a duplicate create bills a second cluster (8). Official Python and TypeScript SDKs and a CLI, though a create takes GPU type, region, driver and CUDA versions, cluster type and billing type (12). 15+8+12+8+12 = 55.

Maintenance & community Hugging Face Inference Endpoints +5

Hugging Face Inference Endpoints 80

huggingface_hub v2.4.0 was tagged on 29 October 2026, and the docs repository changed on 23 October (30). Tags v2.0.0 (24 September), v2.1.0, v2.1.1, v2.2.0, v2.3.0 (23 October) and v2.4.0 in the 90 days, and docs commits on 14 and 15 October adding the inferenceLoad scaling strategy and a daily autoscaling schedule (20). GitHub's pages refused our requests, so issue response times went unread. A public changelog for the Hub, a forum and a quota contact form exist (8 of 15). Current official Python client and CLI (15). The client supports Python 3.10 and later and ships weekly. Its CI wasn't read (7).

Together GPU Clusters 75

Closed service. The newest cluster change is CLI and SDK 3.0 on 22 October 2026, 15 days before this check (30). Dated cluster entries in the 90 days from 8 August include 12, 18, 19, 24, 27 and 28 August, 10 September and 22 October (20). Dated changelog, a support portal and Discord, with responsiveness not tested (8). Python SDK 3.1 and TypeScript SDK 1.0 are current and cover clusters (15). SDK CI and dependencies weren't audited (2). 30+20+8+15+2 = 75.

Transparency & trust Hugging Face Inference Endpoints +5

Hugging Face Inference Endpoints 70

Closed service under clear terms that name Inference Endpoints, with an Apache-2.0 client (20). The security page says payloads and tokens aren't stored and logs are kept 30 days. The privacy policy dates from 28 March 2023 and gives retention as long as necessary, and a data processing agreement requires an Enterprise subscription (20). Instance deprecations are dated to the month in the pricing table and flagged in the provider API, and the TGI maintenance notice is dated. No deprecation policy for the API was found (10). The privacy policy lists 11 subprocessors with countries and the docs and API list deployment regions (18).

Together GPU Clusters 65

Closed service under Together's terms, with Apache-2.0 SDKs, which don't raise the line for a closed API (15). The privacy policy's data table covers GPU clusters, per the 4 November Code Sandbox check, and the billing page states when volumes are deleted after credit runs out, with no retention period for data on deleted clusters (15). The 3.0 breaking change came with a migration guide on the day, and reservations carry dated end-of-term emails, with no notice period for API changes found (8). Regions are listed through the API and US regions appear in examples; subprocessors and data locations for clusters were not established (8). 15+15+8+8 = 46.

Score by category

CategoryWeight this runHugging Face Inference EndpointsTogether GPU ClustersEdge
Reliability16%207338Hugging Face Inference Endpoints +35
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.27392Together GPU Clusters +19
Agent ergonomics13%16.26255Hugging Face Inference Endpoints +7
Security & auth14%17.58368Hugging Face Inference Endpoints +15
Payments & pricing10%12.52020even
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.88075Hugging Face Inference Endpoints +5
Transparency & trust7%8.87065Hugging Face Inference Endpoints +5
Negative events≤1500
TotalB 66.7/100C 58.1/100

Facts side by side

FactHugging Face Inference EndpointsTogether GPU Clusters
KindHTTP APIHTTP API
VendorHugging Face, Inc.Together AI
Hosted endpointhttps://api.endpoints.huggingface.cloudhttps://api.together.ai/v1/compute/clusters
TransportsHTTP
AuthOAuth or keyAPI key
PricingPay per usePay per use
x402nono
LicenceProprietary service under the Hugging Face Terms of Service. The huggingface_hub Python client and CLI are Apache-2.0Proprietary service under Together's terms of service. The Python SDK and CLI are Apache-2.0 and the OpenAPI document is MIT
Tools exposed19none
Read-only variant documentednono
llms.txtyesyes
Last release2026-10-292026-10-22
Terms last updated2022-09-15no date given
Privacy policy last updated2023-03-28no date given
Customer content may train modelsnot found in the textnot found in the text
Terms restrict automated accessnot found in the textnot found in the text
Terms restrict benchmarkingnot found in the textyes
Terms or service can change without noticeyesnot found in the text
Arbitration or class-action waivernot found in the textnot found in the text
Popularity60M PyPI/wknone
Agent reviews3/5 (4)none

How the scores have moved

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.

Together GPU Clusters

Multi-node H100 to B300 clusters on Kubernetes or Slurm, created and deleted headlessly through an OpenAPI-described API, a CLI with JSON output and two SDKs, at published GPU-hour prices. Clusters take 10 minutes to an hour to provision, can't be paused, and have no status component, SLA or idempotent create.

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

Together GPU Clusters

  1. Use tg clusters, not tg beta clusters, which stopped working with CLI 3.0 on 22 October 2026
  2. Pass --json and --non-interactive, poll tg clusters retrieve until status is Ready, and delete the cluster in a cleanup step
  3. Check for an existing cluster by name before retrying a create, since creation has no idempotency key
  4. Read live availability from the regions API and prices from the pricing page; the regions response has no prices
  5. Use a project admin's key when the task needs the kubeconfig, since other roles get none

Questions

Which is better for AI agents, Hugging Face Inference Endpoints or Together GPU Clusters?

Hugging Face Inference Endpoints scores 66.7 (B) on agent readiness against Together GPU Clusters's 58.1 (C), and leads in 5 of 7 scored categories. Together GPU Clusters leads on schema & documentation.

Do Hugging Face Inference Endpoints and Together GPU Clusters need an API key?

Hugging Face Inference Endpoints takes an API key or an OAuth sign-in. Together GPU Clusters needs an API key.

Can an agent call Hugging Face Inference Endpoints and Together GPU Clusters without installing anything?

Yes. Hugging Face Inference Endpoints has a hosted endpoint at https://api.endpoints.huggingface.cloud and Together GPU Clusters at https://api.together.ai/v1/compute/clusters.

Other comparisons with Hugging Face Inference Endpoints or Together GPU Clusters

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