COMPUTE & SANDBOXES · FOR AI AGENTS
Together AI
Call hosted models on managed inference capacity for agent workloads.
Use Together APIs and official SDKs to run supported chat, structured-output and other model tasks without operating the accelerator and model-serving infrastructure.
Your integration checklist
- Capabilities
- What you need from your owner
- Create an account and project, generate an API key, configure billing and choose the exact model and serving mode. Define the request budget and validate the model on the intended task.
- Authentication
- Store a project API key on a trusted backend. The SDK reads TOGETHER_API_KEY; REST requests use Authorization: Bearer and the API base https://api.together.ai/v1.
- Your first task
- Send a bounded request to the selected model and validate the response against the task’s required schema and content checks.
- Billing & payment
- Inference pricing depends on the selected model and workload. Text models have input and output token rates; provisioned throughput, dedicated infrastructure and training use separate pricing. Check the current model row before estimating spend.
- Agent payment support
- Not verified. Programmatic usage does not establish autonomous checkout support.
- Operational limits
- Serverless capacity is best-effort and demand may produce 429 or 503 errors. Some models have additional restrictions. Provisioned throughput is a separate option for committed capacity; generated output still needs application validation.
ORIGINAL AI AGENT STORE INTEGRATION PLAYBOOK
Choose the serving mode before committing to a latency-sensitive workflow
Editorial recommendation · Reviewed 2026-10-23
When to choose this service
Choose Together when a workflow requires a particular externally hosted model or additional inference capacity, and can explicitly account for model behavior and serving availability.
Managed accelerators and access to separately served model weights supply an external execution resource. An agent’s existing reasoning and ordinary code do not reproduce that model or reserve serving capacity.
Suggested workflow
- Select the exact model and verify the required input and output features. Set token, time and spend budgets for a representative task.
- Validate normal and malformed responses before using outputs downstream. Keep any external action behind application permissions and argument checks.
- Measure usage and response time under expected concurrency. Choose committed capacity if best-effort serverless behavior cannot meet the workflow requirements.
Acceptance checks before real work
- Verify model identity and supported response format.
- Treat syntactically valid output as needing task-specific validation.
- Keep completed external actions recorded across model retries.
If the workflow fails
Reduce bursts and apply bounded exponential backoff for 429 or 503 responses. Preserve the job and action history; a regenerated response must not repeat a completed mutation.