together-dedicated-endpoints

name	together-dedicated-endpoints
description	Single-tenant GPU endpoints on Together AI with autoscaling and no rate limits. Deploy fine-tuned or uploaded models, size hardware, and manage endpoint lifecycle. Reach for it whenever the user needs predictable always-on hosting rather than serverless inference, custom containers, or raw clusters.

Together Dedicated Endpoints

Overview

Use dedicated endpoints for managed single-tenant model hosting with predictable performance and no shared serverless pool.

Typical fits:

• production inference with stable latency
• fine-tuned model hosting
• uploaded custom model hosting
• autoscaled model APIs

When This Skill Wins

• The user needs always-on or single-tenant hosting
• The model is supported for dedicated deployment
• Fine-tuned or uploaded models must be served as endpoints
• Hardware, scaling, or idle-time settings need explicit control

Hand Off To Another Skill

• Use together-chat-completions for serverless chat inference
• Use together-dedicated-containers for custom runtimes or nonstandard inference pipelines
• Use together-gpu-clusters for raw infrastructure or cluster orchestration
• For production stock-model workloads that need a defined SLA (committed throughput and reliability) without managing hardware, point users to Together's provisioned throughput tier (reserved PTU capacity, one-month minimum, contact sales). Use dedicated endpoints instead when the user needs to serve a fine-tuned or uploaded model, or wants direct control over hardware, latency, and throughput.

Quick Routing

• Create and manage a standard endpoint
  • Start with scripts/manage_endpoint.py or scripts/manage_endpoint.ts
  • Read references/api-reference.md
• Lifecycle tuning or troubleshooting
  • Read references/api-reference.md
• Deploy a fine-tuned model
  • Start with scripts/deploy_finetuned.py
  • Read references/dedicated-models.md
• Upload and deploy a custom model
  • Start with scripts/upload_custom_model.py
  • Read references/dedicated-models.md
• Hardware and sizing choices
  • Read references/hardware-options.md

Workflow

1. Confirm that the task needs dedicated hosting instead of serverless or containers.
2. Verify model eligibility and inspect available hardware.
3. Create the endpoint with explicit scaling and timeout settings.
4. Wait for readiness before sending inference traffic.
5. Stop or delete the endpoint when the workload no longer needs to run.

High-Signal Rules

• Python scripts require the Together v2 SDK (together>=2.0.0). If the user is on an older version, they must upgrade first: uv pip install --upgrade "together>=2.0.0".
• Model eligibility and hardware availability are gating constraints; check them early.
• Endpoint management uses endpoint IDs, while inference usually uses the endpoint name as model.
• Autoscaling, auto-shutdown, prompt caching, and speculative decoding materially affect operations and cost.
• For custom or fine-tuned models, do not skip the intermediate verification steps before deployment.

Resource Map

• API reference: references/api-reference.md
• Operational controls and troubleshooting: references/api-reference.md
• Dedicated model guide: references/dedicated-models.md
• Hardware guide: references/hardware-options.md
• Python endpoint lifecycle: scripts/manage_endpoint.py
• TypeScript endpoint lifecycle: scripts/manage_endpoint.ts
• Fine-tuned deployment: scripts/deploy_finetuned.py
• Custom model upload and deployment: scripts/upload_custom_model.py

Official Docs

• Dedicated Endpoints
• Endpoints API
• Upload and Deploy Custom Models