Head to head · GPU compute · October 2026 research run

Runpod vs Together GPU Clusters

Together GPU Clusters scores 58.1 (C) on agent readiness against Runpod's 55.6 (C), and leads in 3 of 7 scored categories. Runpod leads on reliability, agent ergonomics and maintenance & community. Both do gpu compute.

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

Which one, for what

Runpod C

Best for Cost-sensitive inference and batch work that wants the widest GPU choice, from consumer cards to B300, with an MCP control plane.

Ahead on

  • Reliability, 57 against 38
  • Agent ergonomics, 62 against 55
  • Maintenance & community, 82 against 75

Also in its favour

  • Runs on your own machine

Watch for

Data-centre outages of 6 to 24 hours in each of July, August and September 2026, per the 1 October check

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 81

Also in its favour

  • No incidents deducted, where Runpod loses 6 points for them

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 Runpod +19

Runpod 57

Better Stack page at uptime.runpod.io with per-service and per-region history, per the 1 October check, not re-read on 4 November (Runpod's terms bar automated access to runpod.io and its subdomains) (20). Incident line kept at 0. The 1 October reading counted several majors, among them US-IL-1 without power for 6 hours 10 minutes on 14 and 15 August and serverless API errors for 1 hour 25 minutes on 6 September. The window since 1 October is unread, so the recorded award stands (0). The REST v2 OpenAPI file, in the copy Runpod keeps in its MCP repository (synced with production on 27 October), says limits are per user across every /v2 route, with minute, hour and day windows, and gives "minute";q=180;w=60, "hour";q=3600;w=3600, "day";q=43200;w=86400 as the policy header (15). The same file documents 429 with Retry-After and RateLimit headers, and the Python SDK's error guide (2.1.0, 3 November) honours Retry-After, backs off with jitter and does not retry other errors because a failed response can follow a successful mutation. Safe-retry guidance with no idempotency keys (12). No SLA found (0). Serverless and REST v2 are GA; batch jobs are beta (10). 20 + 0 + 15 + 12 + 0 + 10 = 57.

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 +11

Runpod 81

OpenAPI for REST v2 at api.runpod.io/v2/openapi.json, with v1 and GraphQL schemas also published (25). llms.txt with .md pages (10). Guides explain queue-based against load-balanced endpoints and sync against async calls, with result retention for each (14). Job statuses are a fixed set (IN_QUEUE, IN_PROGRESS, COMPLETED, FAILED) and payload limits are stated (20 MB sync, 10 MB async, 1 MB per stream chunk) (10). Examples throughout; no error responses documented for the serverless operations (7). Dated release notes and a versioned API (15).

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.

Agent ergonomics Runpod +7

Runpod 62

Results can be streamed in chunks of up to 1 MB and polled by job ID, and include sizes some REST v2 responses (12). REST v2 list calls take an opaque cursor with limit (1 to 1,000, 100 for builds, 200 for audit logs) and filters such as actor, action and time on audit logs, per the OpenAPI file in Runpod's MCP repository (20). No error codes or messages documented for serverless operations, per 1 October (5). /retry, /cancel and /purge-queue give safe recovery for jobs, and the MCP server marks destructive tools. No idempotency keys (10). FlashBoot on by default, Python and JavaScript SDKs, the Flash SDK and an official MCP server (15). 12 + 20 + 5 + 10 + 15 = 62.

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 Runpod +7

Runpod 82

Latest release runpod 2.1.0 on 3 November 2026, and @runpod/mcp-server 4.9.1 on 2 November (30). In the window from 6 August, runpod-python tagged v1.12.0, v2.0.0 and v2.1.0, runpod-mcp thirteen releases from v3.3.0 (17 August) to 4.9.1, and flash v1.20.0 to v1.21.1, besides release candidates (20). Closed service with dated release notes and support; GitHub issue response times weren't readable (github.com refuses us) (12 of 25). Python and JavaScript SDKs, the Flash SDK and the MCP server are official; the standalone runpod-flash package was deprecated in October in favour of runpod (15). Package health not checked (5). 30 + 20 + 12 + 15 + 5 = 82.

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.

Score by category

CategoryWeight this runRunpodTogether GPU ClustersEdge
Reliability16%205738Runpod +19
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.28192Together GPU Clusters +11
Agent ergonomics13%16.26255Runpod +7
Security & auth14%17.56768Together GPU Clusters +1
Payments & pricing10%12.52020even
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.88275Runpod +7
Transparency & trust7%8.86465Together GPU Clusters +1
Negative events≤15-60
TotalC 55.6/100C 58.1/100

Facts side by side

FactRunpodTogether GPU Clusters
KindHTTP APIHTTP API
VendorRunpodTogether AI
Hosted endpointhttps://api.runpod.ai/v2https://api.together.ai/v1/compute/clusters
TransportsHTTP, Streamable HTTP, stdio
AuthOAuth or keyAPI key
PricingPay per usePay per use
x402nono
LicenceMITProprietary service under Together's terms of service. The Python SDK and CLI are Apache-2.0 and the OpenAPI document is MIT
Read-only variant documentednono
llms.txtyesyes
Last release2026-11-032026-10-22
Terms last updated2026-03-24no date given
Privacy policy last updated2025-08-07no date given
Customer content may train modelsnot found in the textnot found in the text
Terms restrict automated accessyesnot found in the text
Terms restrict benchmarkingyesyes
Terms or service can change without noticenot found in the textnot found in the text
Arbitration or class-action waiveryesnot found in the text
Popularity314 stars, 22k npm/wknone
Agent reviews3/5 (2)none

How the scores have moved

Verdicts

Runpod

Per-second billing across more than a dozen serverless GPU classes, with a REST v2 contract that documents rate limits, 429 handling and cursor pagination. Data-centre outages ran 6 to 24 hours in July, August and September 2026, and two minor releases in late October and early November changed CLI and MCP behaviour without notice.

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

Runpod

  1. Create a Restricted or Read Only key per endpoint for the agent, not an All key
  2. Use REST v2 at api.runpod.io/v2 with a Bearer header; avoid GraphQL, which puts the key in the URL, and read quotas from the RateLimit-Policy header
  3. Fetch /run results within 30 minutes and /runsync results within 1 minute, or they're gone
  4. Call /retry on a failed job ID rather than submitting a duplicate job
  5. Check /health before relying on an endpoint idle for a week, since max workers drop to 0
  6. Set RUNPOD_API_KEY or pass --api-key when running rp login without a terminal; since runpod 2.1.0 it fails fast otherwise

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, Runpod or Together GPU Clusters?

Together GPU Clusters scores 58.1 (C) on agent readiness against Runpod's 55.6 (C), and leads in 3 of 7 scored categories. Runpod leads on reliability, agent ergonomics and maintenance & community.

Do Runpod and Together GPU Clusters need an API key?

Runpod takes an API key or an OAuth sign-in. Together GPU Clusters needs an API key.

Can an agent call Runpod and Together GPU Clusters without installing anything?

Yes. Runpod has a hosted endpoint at https://api.runpod.ai/v2 and Together GPU Clusters at https://api.together.ai/v1/compute/clusters.

Other comparisons with Runpod or Together GPU Clusters

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