n4n vs Together AI: API compatibility compared

When you’re deciding between inference gateways, the practical question is how much of your OpenAI client code survives a switch. This post puts n4n vs Together AI API compatibility under a microscope, comparing the two across the dimensions that actually break builds: request shapes, parameter passthrough, and failure modes. If you’ve wrapped openai in your own client, the devil is in the headers.

API surface and OpenAI compatibility

Both platforms expose REST endpoints that mirror OpenAI’s /v1/chat/completions. You can point the official Python or TS SDK at either by changing base_url. That’s the surface-level compatibility most blog posts stop at.

Endpoint structure

Together AI uses https://api.together.xyz/v1 as its prefix. n4n presents a single OpenAI-compatible endpoint that addresses 240+ models from multiple upstream providers. The path conventions are identical: POST /chat/completions, POST /completions, POST /embeddings.

from openai import OpenAI

# Together AI
together = OpenAI(base_url="https://api.together.xyz/v1", api_key="TOGETHER_KEY")
r1 = together.chat.completions.create(
    model="mistralai/Mixtral-8x7B-Instruct-v0.1",
    messages=[{"role": "user", "content": "ping"}],
    stream=False,
)

# n4n (OpenAI-compatible gateway)
n4n = OpenAI(base_url="https://api.n4n.ai/v1", api_key="N4N_KEY")
r2 = n4n.chat.completions.create(
    model="anthropic/claude-3-haiku",
    messages=[{"role": "user", "content": "ping"}],
)

The difference appears when you need provider-specific behavior. Together collapses everything into its own model catalog. n4n forwards provider cache-control hints and honors client routing directives through request headers, so you can pin or prefer a backend without changing the URL.

Capabilities

Together AI hosts open-weight models (Llama, Mixtral, Qwen) and a few proprietary ones, with first-class support for function calling, JSON mode, and vision on multimodal checkpoints. Fine-tuning and batch inference are native API resources.

n4n.ai fronts 240+ models from multiple providers behind one OpenAI-compatible endpoint, and automatically falls back when a provider is rate-limited or degraded. That means a single chat.completions call can target an OpenAI model, a Together-hosted model, or a self-hosted endpoint without branching logic. Tool calls and vision depend on the underlying model’s support, not the gateway’s translation layer.

# Together: explicit JSON mode
curl https://api.together.xyz/v1/chat/completions \
  -H "Authorization: Bearer $TOGETHER_KEY" \
  -d '{"model":"meta-llama/Llama-3-8b-chat-hf","response_format":{"type":"json_object"}}'

Price/cost model

Together publishes per-token rates per model, with committed-use discounts and a clear separation between on-demand and reserved GPU pools. You pay the meter on usage.completion_tokens and usage.prompt_tokens exactly as OpenAI reports them.

n4n.ai meters per-token usage across underlying providers, passing through the origin cost plus gateway overhead. You don’t negotiate separate contracts with each model vendor; the gateway abstracts that. Neither side introduces non-standard billing fields in the response object—both extend OpenAI’s usage block rather than replacing it.

Latency/throughput

Together owns the hardware stack for its hosted models, so tail latency is a function of its scheduler. For high-concurrency batch jobs, you can provision dedicated capacity.

n4n adds a routing hop. In normal operation the overhead is a single proxy layer, but automatic fallback means a degraded provider can trigger a cross-provider retry, which trades latency for availability. If your SLO is “never 429,” that’s a worthwhile swap. If your SLO is “p99 under 200ms,” measure both against your exact model choice.

Ergonomics

Together’s API docs map 1:1 to OpenAI parameter names: temperature, top_p, max_tokens, stop, frequency_penalty. They add logprobs and echo on /completions. SDK support is “change the base url and go.”

n4n keeps the same parameter namespace but treats unknown fields as passthrough. That’s critical when you call a provider that accepts seed or top_k—Together will reject what it doesn’t know; n4n forwards it. Header-based routing is the one ergonomic divergence:

x-routing: prefer: together; fallback: openai
x-cache-control: ttl=3600

Those headers are ignored by Together’s endpoint.

Ecosystem

Together ships a fine-tuning API, a model marketplace, and reference notebooks for LoRA training. It’s a destination, not a transit point.

n4n is a gateway. Its ecosystem value is the union of its upstream providers: you get one API key, one retry policy, one usage dashboard. You lose provider-specific extras like Together’s training jobs unless you call them directly.

Limits

Together enforces per-key RPM and TPM ceilings documented per tier; context windows are bounded by the model, not the platform. n4n inherits the strictest of gateway and upstream limits, and may return 429 with a retry-after that reflects the failing backend.

Which to choose

You should pick Together AI if

• You only need open-weight or Together-hosted models.
• You want to fine-tune or run dedicated capacity on the same bill.
• Strict low-latency to a known model matters more than provider redundancy.

You should pick n4n if

• Your code must swap between Anthropic, OpenAI, and open-source without branching.
• You need automatic fallback when a provider is degraded.
• You want one metering layer across 240+ models and header-driven routing.

For most teams building production agents, the compatibility story isn’t “which API looks like OpenAI” but “which one lets me change the model string at 3am without a deploy.” That’s the real split in n4n vs Together AI API compatibility.