# Apertis vs Together AI: gateway governance or inference platform

Compare Apertis's provider-independent control path with Together AI's serverless and dedicated inference products by workload ownership and deployment need.

Canonical: https://apertis.ai/compare/together-ai

## Decision intent

You need production inference and must decide whether the workload calls for a governed multi-provider gateway or Together AI's inference and deployment platform.

## Decision table

| Question | Apertis | Together AI |
| --- | --- | --- |
| Control boundary | **Keys, model policy, quota, routing, and Activity live in one workspace.** | Capacity and deployment choices follow Together AI's current product tiers. |
| Upstream keys | **Apertis holds the upstream provider credentials; your team issues bounded workspace tokens, each with its own quota.** | One Together API key authenticates against Together's own fleet. |
| Routing decision | **Channel selection follows declared policy — user group, model availability, and channel priority.** | Serverless, provisioned throughput, and dedicated inference share one API; the model parameter selects the mode. |
| Verify before migration | **Confirm a model response and matching Activity record.** | Confirm model availability, endpoint type, and current price on Together AI. |
| Spend and limits | Quota is enforced per token, and every call lands in the workspace Activity record. | Pricing is per token, per minute, or per GPU-hour by mode; the pricing page does not state spend or rate limits. |
| Primary job | Govern access and execution across multiple model providers. | **Run models through serverless, dedicated, or other published inference products.** |
| Provider posture | Provider-independent compatible gateway with a shared catalog. | Together AI-operated inference options for its supported model catalog. |

Bold follows the full comparison’s strongest-fit verdict. An unmarked row makes no additional ranking between these two platforms.

## Fit, not winner

### Choose Apertis when

Choose Apertis when you want one compatible control surface across providers, with model access, key boundaries, routing context, and usage records managed together.

### Choose Together AI when

Choose Together AI when its current serverless, dedicated, or provisioned inference path is the deployment product you want. Validate the specific model and capacity option with Together AI.

## Migration path

1. **Classify the deployment need** — Separate simple compatible inference from requirements for dedicated capacity, fine-tuning, or provider diversity.
2. **Verify the exact model surface** — Compare current model IDs, request parameters, context, and endpoint availability on the live catalogs.
3. **Benchmark your request shape** — Use a representative prompt and response constraint; avoid substituting generic latency or quality claims for workload evidence.
4. **Choose the operational owner** — Decide who owns keys, quotas, routing changes, capacity, incident review, and usage reconciliation.

## Primary sources

These links are evidence inputs, not endorsements. The Together AI cells in the decision table were read from them on 2026-09-07; product details can change after that.

- [Together AI models](https://www.together.ai/models): Current supported model catalog and deployment choices.
- [Together AI pricing](https://www.together.ai/pricing): Current inference and product pricing.
- [Together inference overview](https://docs.together.ai/docs/inference/overview): Current inference surface and concepts.

## Next action

Run one representative request against kimi-k3 and inspect the resulting workspace record before migrating more.