n4n vs Together AI: which supports more open models?

When you’re picking an inference layer, the raw count of open weights you can call matters less than how those models are exposed and what happens when a provider falls over. The debate around n4n vs Together AI open model support usually starts with catalog size but quickly runs into routing, fallback, and cache semantics. This piece breaks down both platforms across the dimensions that actually affect production workloads, from token metering to cache-control headers.

Catalog breadth and model access

Together AI is a single inference provider that hosts a curated set of open-weight models. The roster includes the Llama 3 family, Mistral and Mixtral variants, Qwen 2, Gemma, and a rotating set of community fine-tunes like Dolphin or OpenChat. Every model string you pass maps to a checkpoint running in their own datacenters. The catalog is deep for popular permissive licenses, but it is inherently bounded by what their infrastructure team decides to deploy and keep warm.

A gateway like n4n.ai collapses those provider endpoints into one OpenAI-compatible URL covering 240+ models, including most open weights you’d get from Together directly, plus equivalents served by Fireworks, Groq, and self-hosted vLLM clusters. If your definition of “open model support” is “how many distinct open checkpoints can I address with one credential,” the aggregation layer wins by construction. You can call a Llama-3-70B from Together, then switch to a Qwen-2-72B from another provider without changing your base URL.

That said, aggregation is not hosting. When you request llama-3-70b through the gateway, the token still lands on some upstream provider’s GPU. The gateway adds a routing and normalization layer rather than new silicon. You trade direct accountability for optionality.

Capabilities and routing

Together exposes chat completions, legacy completions, embeddings, and a few vision models through a single auth scheme. They support structured outputs via JSON mode, streaming, and seed-controlled generation on most text models. Their routing is internal: you pick a model string, they schedule it on available hardware. If that hardware is saturated, you get a 429.

In contrast, a gateway that honors client routing directives lets you pin a provider or let the system choose. n4n.ai forwards provider cache-control hints and performs automatic fallback when a provider is rate-limited or degraded. For a stateless microservice, that means you can send a preference list and get a response even if your primary path is red.

from openai import OpenAI
import os

client = OpenAI(base_url="https://api.n4n.ai/v1", api_key=os.environ["N4N_KEY"])
resp = client.chat.completions.create(
    model="llama-3-8b",
    messages=[{"role": "user", "content": "extract entities from: ..."}],
    extra_body={
        "routing": {"prefer": ["together", "fireworks"]},
        "cache_control": {"type": "ephemeral"}
    }
)

Together has no equivalent cross-provider fallback; if their endpoint 429s, you write the backoff. The gateway’s ability to honor cache_control means you can signal to upstream vLLM instances to reuse prefix caches, which matters when you send many similar system prompts.

Price and cost model

Together publishes per-token rates that are typically lower for open models than for equivalent closed APIs. They also offer dedicated instances and finetuning jobs with separate billing. You get a single invoice and predictable unit economics. For a 70B model at scale, the math is straightforward: tokens × published rate.

A gateway meters per-token usage and passes through underlying provider cost, often with a small margin. You trade a unified bill and credential for slightly less transparent unit economics. If you run heavy Llama-70b traffic, comparing the effective per-token cost requires summing provider rate + gateway fee. There is no public “gateway discount” that magically undercuts direct access; the value is in reduced integration cost.

Neither model is universally cheaper. For spiky workloads, gateway fallback can save engineering time; for steady high-volume, direct provider contracts win. If you need fine-grained cost attribution per team, the gateway’s per-token metering can be simpler than managing sub-accounts at each provider.

Latency and throughput

Together controls the stack: they tune vLLM deployments, use flash-attention, and expose throughput-optimized endpoints. Median latency for an 8B model is usually single-digit hundreds of ms for first token under moderate load. Throughput on batch endpoints can exceed 10k tokens/sec per node depending on config.

Adding a gateway introduces an extra hop. But the gateway’s fallback can cut tail latency dramatically—if your primary provider is degraded, you don’t sit on a timeout waiting for a cold replica. For batch jobs, Together’s internal batch endpoint may be faster than routing through an external layer because there is no proxy parsing your request body twice.

In practice, for interactive chat, the difference between direct and gateway is often within 20–40 ms p50, but p99 can be halved by fallback. Measure with your own traffic shape.

Ergonomics

Both speak OpenAI-compatible REST, so the same openai SDK works with a different base_url. Together’s model names include organization prefixes (meta-llama/Llama-3-8b-chat-hf), which forces you to track naming conventions. The gateway abstracts those into short slugs (llama-3-8b), at the cost of occasionally ambiguous version resolution.

# Together direct
from openai import OpenAI
t = OpenAI(base_url="https://api.together.xyz/v1", api_key="TOGETHER")
t.chat.completions.create(
    model="meta-llama/Llama-3-8b-chat-hf",
    messages=[{"role": "user", "content": "hi"}]
)

# Gateway
g = OpenAI(base_url="https://api.n4n.ai/v1", api_key="N4N")
g.chat.completions.create(
    model="llama-3-8b",
    messages=[{"role": "user", "content": "hi"}]
)

Streaming, function calling, and seed control behave similarly, but edge cases (e.g., logit bias ranges) vary by upstream. Together’s error messages are provider-specific; the gateway normalizes many of them to OpenAI-shaped faults, which simplifies client code.

Ecosystem and limits

Together offers finetuning, model distillation, and a hosted playground. Rate limits are per-account and published; context windows follow each model’s native size (e.g., 8k for older, 128k for Llama-3.1). You can purchase reserved capacity.

The gateway’s ecosystem is the union of its providers. Finetuning must be done with the underlying provider; the gateway just routes inference. Limits are the intersection of gateway quota and provider quota—if Together caps you at 100 req/min and the gateway at 200, you get 100. The gateway does provide a single place to set organizational policies like max tokens per request.

Head-to-head table

Which to choose

Choose Together AI if: You want a single accountable provider for open models, need finetuning, and can tolerate writing your own retry logic. Good for steady high-volume batch and teams standardizing on one stack. Their direct pricing is easy to forecast.

Choose the gateway if: You need breadth across 240+ models without juggling many API keys, or your SLA requires automatic fallback when a provider is degraded. Ideal for multi-model experimentation and latency-sensitive prod with spiky traffic. The unified cache-control forwarding reduces repeated prefix costs.

For a solo builder: Together’s simple billing and docs get you to first token fastest. For a platform team: the gateway’s routing directives and unified metering reduce integration debt. For a research shop: the gateway lets you A/B open weights from different hosts without rewriting clients.

Pick based on whether your pain is model diversity or operational simplicity. The n4n vs Together AI open model support question is really about aggregation versus dedicated control.