Leanroute vs Together AI

Multi-provider gateway vs open-weight inference host.

Together AI hosts a large catalog of open-weight models (Llama, Mixtral, Qwen, DeepSeek, Yi, Nous, community fine-tunes) with per-token pricing and fine-tuning support. Leanroute is a gateway that reaches every major closed-model provider plus APAC through one OpenAI-compatible endpoint. Different tools; teams often use both.

Snapshot date: August 2026. Together provider adapter is on the v2 roadmap — once shipped, you'll route to Together's models via Leanroute the same way you route to OpenAI.

TL;DR

Pick Together if your workload is exclusively OSS models, you need fine-tuning, or you want the deepest OSS catalog (100+ models incl. community fine-tunes). Pick Leanroute if your app calls closed models (OpenAI/Anthropic/Google/xAI), needs MCP passthrough, uses APAC providers, or wants per-org guardrails + spend caps + a single billing surface. Use both (once Leanroute's Together adapter ships) — Leanroute routes closed models to their native providers, OSS models to Together.

Where we differ

DimensionLeanrouteTogether AI
CategoryMulti-provider gateway (11 providers)Open-weight inference host (100+ OSS models)
Closed-model providersOpenAI, Anthropic, Google, xAI — all first-classNot supported (Together hosts open-weight only)
Open-weight model catalogCovers Llama and Mistral via provider adapters (roadmap)100+ OSS models — Llama, Mixtral, Qwen, DeepSeek, Yi, Nous, MythoMax
Fine-tuningNot offeredYes — LoRA fine-tunes on their infrastructure
Wire formatOpenAI-compatibleOpenAI-compatible
Native MCP passthroughYes on Anthropic modelsNot supported
Cross-provider failoverAutomatic — 5xx on one provider retries on same-tier altWithin Together only
Guardrails library7 curated rules (PII, secrets, prompt-injection, moderation, etc.)No first-party library
APAC providers first-classQwen, GLM, Doubao, Kimi, Sarvam, KrutrimSome Qwen + DeepSeek variants; no Sarvam/Krutrim/Doubao
Data residencySingaporeUS-hosted (SOC 2, HIPAA)

Where Together still wins

  • OSS catalog depth. 100+ open-weight models including community fine-tunes (Nous Hermes, MythoMax) we don't carry.
  • Fine-tuning. LoRA fine-tunes on their infra with an OpenAI-compatible endpoint out. Nothing comparable on our side.
  • Compliance. SOC 2 + HIPAA today. We're SOC 2 Type I planned Q3 2026, HIPAA not on the roadmap.
  • Batch API + embeddings + code models. Broader inference surface (image gen, embeddings, rerankers, code completion) than our chat-completions-first gateway.

See also: vs Groq · vs Vercel AI · vs OpenRouter