Choose your AI API platform.
Each page states the decision intent, the control boundary, and the migration path — fit first, not a ranking claim. Verify the other side on its own current pages before you choose.
The six questions every one of these comparisons answers.
Each competitor column summarises that company’s own current pages, linked in full on its comparison page. The seventh question is specific to each comparison and lives there.
| Question | Apertis | OpenRouter | Together AI | LiteLLM |
|---|---|---|---|---|
| Control boundary | Strongest fit for this question. Virtual keys, model policy, quota, and Activity records share one workspace context. | Budgets, routing controls, and management keys are documented platform features. | Capacity and deployment choices follow Together AI's current product tiers. | Virtual keys, budgets, fallback, and logging are published proxy capabilities. |
| Upstream keys | Strongest fit for this question. Apertis holds the upstream provider credentials; your team issues bounded workspace tokens, each with its own quota. | One OpenRouter key authenticates the request and OpenRouter reaches the providers; bring-your-own-key is free up to a monthly allowance, then carries a fee. | One Together API key authenticates against Together's own fleet. | You configure each upstream provider key in the proxy you run, and front them with virtual keys. |
| Routing decision | Channel selection follows declared policy — user group, model availability, and channel priority. | Strongest fit for this question. Fallbacks are on by default and weighted by price and uptime; per-request provider order, allow-lists, zero-data-retention and data-collection controls are documented, and Business and Enterprise plans add EU/US in-region routing. | Serverless, provisioned throughput, and dedicated inference share one API; the model parameter selects the mode. | Routing is whatever your own proxy configuration declares. |
| Verify before migration | Strongest fit for this question. Run one workload and match the response to its Activity record. | Review the current pricing, routing, and provider documentation for the chosen model. | Confirm model availability, endpoint type, and current price on Together AI. | Test deployment, database, secrets, upgrades, and observability in your own environment. |
| Spend and limits | Quota is enforced per token, and every call lands in the workspace Activity record. | Strongest fit for this question. Their pricing page lists budgets and spend controls, activity logs with export, and rate limits that differ by plan. | Pricing is per token, per minute, or per GPU-hour by mode; the pricing page does not state spend or rate limits. | Budgets and rate limits per virtual key or user, with spend tracking and an admin UI. |
| Primary job | Operate model access, keys, policy, and usage in one governed workspace. | Reach and route across a broad provider marketplace through one API. | Strongest fit for this question. Run models through serverless, dedicated, or other published inference products. | Run or deploy a compatible proxy across supported providers. |
Competitor cells read from those sources on 2026-09-07. They are other companies’ current pages and they move — verify the side you are moving away from before you choose.
Pick the comparison that matches your workload.
Apertis vs OpenRouterYou want one compatible API across many models and need to decide whether marketplace-style routing or a governed workspace better matches the workload.Model decision · OpenRouterModel pages expose current provider and pricing options for selection.Apertis vs Together AIYou need production inference and must decide whether the workload calls for a governed multi-provider gateway or Together AI's inference and deployment platform.Provider posture · Together AITogether AI-operated inference options for its supported model catalog.Apertis vs LiteLLMYou want a compatible multi-provider layer and need to decide whether your team should operate the proxy or consume a managed gateway workspace.Infrastructure owner · LiteLLMYour team can self-host the open-source proxy or evaluate LiteLLM enterprise options.