| 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
Quick Routing
- Create and manage a standard endpoint
- Lifecycle tuning or troubleshooting
- Deploy a fine-tuned model
- Upload and deploy a custom model
- Hardware and sizing choices
Workflow
- Confirm that the task needs dedicated hosting instead of serverless or containers.
- Verify model eligibility and inspect available hardware.
- Create the endpoint with explicit scaling and timeout settings.
- Wait for readiness before sending inference traffic.
- 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
Official Docs
