n4n vs Together AI: pricing per million tokens

The n4n vs Together AI pricing debate collapses if you treat both as interchangeable APIs. Together AI owns datacenter GPUs and sells raw inference on open-weight models at published per-million-token rates. n4n sits in front of many providers, including Together, and adds routing, fallback, and metering on top. Your effective cost per million tokens depends on which layer you actually need.

Capabilities

Together AI is a single-vendor inference platform. You get hosted generative models (Llama, Mixtral, Qwen, and others), fine-tuning jobs, and dedicated endpoints with guaranteed capacity. It is built for teams that want one contract, one dashboard, and direct access to a specific model family. If you need to spin up a LoRA on a known base model and serve it with a fixed latency SLA, Together is a first-class choice.

n4n is a gateway. It does not run GPUs itself; it brokers requests to upstream providers. One OpenAI-compatible endpoint addresses 240+ models, and the gateway honors client routing directives and forwards provider cache-control hints. If a provider is rate-limited or degraded, it can automatically fall back to another. That capability changes the cost conversation because you are buying resilience, not just tokens. You trade a thin margin for not writing your own multi-provider abstraction.

What you can’t do on each

Together will not serve Claude or GPT-4o—those are competitor models. n4n will, because it aggregates those providers. Conversely, Together gives you bare-metal style controls (instance size, fine-tune epochs) that a gateway cannot expose without leaking provider internals.

Price/Cost Model

Together AI publishes a transparent per-token price sheet. You pay separately for input and output tokens, billed per million. For open-weight models, those rates are typically well below frontier-model API prices, but they vary by model size and quantization. There is no markup hidden behind a routing layer—what you see is what the GPU time costs.

n4n’s cost model is passthrough plus metering. You pay the underlying provider’s per-token rate for the model you route to, and n4n.ai records per-token usage metering on top. If you route to Together through n4n, your token price is Together’s rate; the gateway adds the cost of its fallback and routing logic. For proprietary models like GPT-4o or Claude, n4n aggregates the same providers you’d hit directly, so the per-million-token number is the provider’s retail rate unless negotiated elsewhere.

The n4n vs Together AI pricing comparison only becomes direct when you pin the model. Example: route to Llama 3 8B on Together via either path. Direct Together call:

import openai

client = openai.OpenAI(
    base_url="https://api.together.xyz/v1",
    api_key="TOGETHER_KEY",
)
resp = client.chat.completions.create(
    model="meta-llama/Llama-3-8b-chat-hf",
    messages=[{"role": "user", "content": "ping"}],
)
print(resp.usage.total_tokens)

Same model through n4n’s OpenAI-compatible endpoint:

client = openai.OpenAI(
    base_url="https://api.n4n.ai/v1",
    api_key="N4N_KEY",
)
resp = client.chat.completions.create(
    model="meta-llama/Llama-3-8b-chat-hf",
    messages=[{"role": "user", "content": "ping"}],
    extra_headers={"x-n4n-route": "together"},  # routing directive
)

The token count is identical; the billed amount differs only by the gateway’s metering policy.

Cache behavior

Together supports prompt caching on some models, reducing repeated prefix cost. n4n forwards provider cache-control hints, so you keep that discount when routing through the gateway. If you send cache_control markers in your request, they reach the upstream unmodified. This keeps the per-million-token math honest across both paths.

Hidden line items

Together charges for storage on fine-tunes and idle dedicated endpoints. n4n charges nothing for idle—but you pay for the upstream’s idle if you reserve capacity there. Neither hides egress fees at the token level; both bill per token metered.

Latency/Throughput

Together gives you the shortest path: your request hits their load balancer, then a worker GPU. For bulk batch jobs, you can saturate dedicated endpoints and get predictable throughput measured in tokens per second per request. The p50 is excellent; the p99 degrades when shared clusters fill.

n4n adds a network hop and a routing decision. In normal operation that adds single-digit milliseconds. The tradeoff is tail latency: when Together’s queue backs up, n4n can shift your traffic to an equivalent model on another provider if you enabled fallback. You trade a sliver of p50 latency for a much lower p99. For synchronous user-facing apps, that matters more than the raw per-million-token number.

Throughput on n4n is bounded by the slowest eligible upstream. If you route auto across three providers, your effective tokens/sec is the median of their capacities, not the max. Set explicit routing when you need guaranteed throughput.

Ergonomics

Together’s API is OpenAI-compatible but has provider-specific extensions for fine-tuning and image generation. Their SDKs are solid, and the docs assume you live in their ecosystem. You’ll write retry code for 429s yourself.

n4n speaks one OpenAI-compatible dialect across all 240+ models. You write one client, one message shape, and switch models by changing a string. Routing directives are passed as headers, not new SDK calls. That reduces code churn when you swap a degraded provider.

{
  "model": "anthropic/claude-3.5-sonnet",
  "messages": [{"role": "user", "content": "Summarize this"}],
  "x-n4n-route": "auto"
}

Error shapes are normalized. A degraded upstream becomes a structured gateway_fallback event instead of a cryptic provider timeout.

Ecosystem

Together’s ecosystem is self-contained: model hub, fine-tune console, usage graphs. You can pull models into LangChain or LlamaIndex, but the billing and support are single-vendor.

n4n’s ecosystem is the union of its upstream providers. You get one meter for OpenAI, Anthropic, Google, Together, and dozens of open-model hosts. For a team running a portfolio of models across vendors, that eliminates per-provider invoice reconciliation. Your finance team gets one CSV, not six.

Limits

Together enforces per-key rate limits and context windows tied to the specific model. Dedicated endpoints lift those caps but require commitment.

n4n inherits the limits of upstream providers and layers its own global rate ceiling per account. If you route to Together through n4n, you are still bound by Together’s context window and per-minute token quota; the gateway will not magically exceed them. It will, however, return a structured error when a provider is degraded rather than a silent timeout.

Context window reality

A 128K model on Together is 128K on n4n. The gateway does not truncate or extend. If you need larger contexts, that’s a provider selection problem, not a routing problem.

Head-to-Head Comparison

Which to Choose

Choose Together AI if you have a single model family in production, want the cheapest possible open-weight tokens, and can tolerate occasional queue delays. Batch jobs, offline summarization, and RAG indexing at scale are natural fits. You avoid gateway overhead and get direct GPU throughput. If your entire stack is Llama or Mixtral, the n4n vs Together AI pricing gap is purely the gateway margin—skip the gateway.

Choose n4n if you run multiple model providers in production and need resilience without writing your own retry logic. The n4n vs Together AI pricing gap shrinks once you account for the engineering cost of handling provider outages yourself. For user-facing chat where p99 latency kills UX, the automatic fallback pays for itself. When you must mix Claude, GPT, and open models in one request loop, the single endpoint removes a pile of glue code.

Hybrid approach: many teams route baseline traffic to Together directly for cost, then mirror critical paths through n4n with fallback enabled. You get the published per-million-token rate on the bulk, and insurance on the tail. This is common for shops that already have a Together contract but fear blackout windows.

For proprietary models only: n4n is the simpler way to meter cross-provider usage; Together cannot serve those at all. If your stack already calls Anthropic and OpenAI, adding n4n removes N billing integrations and gives you one usage dashboard.

The per-million-token number is never the whole story. Compute the token cost, then add the cost of the code you’ll write to survive a 429. That second line item is where this comparison actually lands.