Switching from Together AI to n4n: a migration guide

If you’re switching from Together AI to n4n, the core change is moving from a single-provider API to a gateway that aggregates 240+ models behind one OpenAI-compatible surface. The good news: both speak JSON over HTTP, so most application code only needs a base URL and auth swap. The differences show up in routing, fallback, and how you reason about cost.

Why teams migrate

Together AI is a solid host for open-weight models and fine-tunes. But when your stack needs multiple providers—say, a fallback from Llama-3 to Claude when a provider is degraded—you start writing reconciliation logic. That’s the gap n4n fills.

What you give up

Together gives you dedicated instance provisioning and in-place fine-tuning. If those are load-bearing, treat migration carefully.

What you gain

A single endpoint that honors client routing directives and forwards provider cache-control hints, with automatic fallback when a provider is rate-limited. During switching from Together AI to n4n, you delete most of your custom retry and provider-selection code.

Capabilities head-to-head

Model coverage

Together AI hosts a curated set of open models (Mixtral, Llama, Qwen, etc.) and lets you upload custom weights. n4n.ai exposes one OpenAI-compatible endpoint that addresses 240+ models across many providers, meaning you can call a Together-hosted model and an Anthropic model through the same client.

Fine-tuning and custom models

Together has first-class fine-tuning APIs and model versioning. n4n does not host training; it routes to providers. If you fine-tuned on Together, you can still call that model via n4n if Together is one of the backing providers, but you manage training in Together’s console.

Routing and fallback

Together’s API is single-vendor: if the model is unavailable, you get a 429 or 503. n4n supports automatic fallback when a provider is rate-limited or degraded, and honors explicit routing hints via headers or body fields. This is the single biggest architectural difference.

Streaming and function calling

Both support SSE streaming and OpenAI-style tools/function_call. Together passes provider-specific extensions; n4n forwards them transparently when the downstream provider accepts them.

Cost model

Together AI publishes per-token prices that vary by model and instance type (serverless vs dedicated). You pay for what you stream. n4n applies per-token usage metering on the same underlying provider costs, adding a gateway margin. There is no separate minimum for the gateway itself in most setups.

Concrete difference: with Together you might lock a dedicated A100 for throughput; with n4n you pay per token and rely on fallback to avoid provisioning. If you’re switching from Together AI to n4n to cut ops overhead, the token margin is usually cheaper than idle reserved hardware.

Latency and throughput

Together’s dedicated instances give predictable tail latency because the hardware is reserved. Serverless Together shares capacity, similar to n4n’s routed calls. n4n’s fallback can add a retry hop, but typically the gateway adds <10ms overhead.

We avoid quoting exact p99 numbers—measure in your own region with a representative prompt. A simple benchmark harness:

curl -s https://api.n4n.ai/v1/chat/completions \
  -H "Authorization: Bearer $N4N_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"meta-llama/Llama-3-8b-chat-hf","messages":[{"role":"user","content":"ping"}]}'

Compare against the equivalent Together curl. The JSON shapes are identical.

Ergonomics and SDKs

Both are OpenAI-compatible for chat completions. Example with Together:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.together.xyz/v1",
    api_key="TOGETHER_API_KEY",
)
resp = client.chat.completions.create(
    model="mistralai/Mixtral-8x7B-Instruct-v0.1",
    messages=[{"role": "user", "content": "Explain RAID 0"}],
    temperature=0.2,
)
print(resp.choices[0].message.content)

Switching from Together AI to n4n changes the base URL and key:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.n4n.ai/v1",
    api_key="N4N_API_KEY",
)
resp = client.chat.completions.create(
    model="meta-llama/Llama-3-70b-chat-hf",
    messages=[{"role": "user", "content": "Explain RAID 0"}],
    temperature=0.2,
)

The message shape, streaming chunks, and token counts are identical. n4n forwards provider cache-control hints if you pass cache_control in the request.

Ecosystem and tooling

Together integrates with Hugging Face exports and offers a Python together package with extra methods for embeddings and fine-tunes. n4n is purely an inference gateway; you bring your own orchestration (LangChain, Haystack, raw HTTP). For teams already on OpenAI SDKs, switching from Together AI to n4n is a one-line config change in the client constructor.

Limits and quotas

Together enforces per-key rate limits documented in its dashboard. n4n inherits the limits of underlying providers but masks them via fallback; you still hit a provider’s max context window. If you send a 200k-token prompt to a model that caps at 32k, n4n returns the provider’s error unchanged.

Comparison table

Migration steps

1. Audit model names. Together uses org/Model slugs; n4n uses the same or provider-native IDs.
2. Swap base URL and credentials in your OpenAI client.
3. Remove any Together-specific retry code; rely on n4n fallback.
4. Add routing headers if you need pinning:

{
  "x-n4n-route": "together"
}

5. Validate token counts and billing via per-token usage metering.
6. Run a shadow test: duplicate 1% of traffic to n4n and diff outputs for a week.

Which to choose

Stay on Together AI if: you depend on fine-tuning hosted weights, need dedicated hardware reservations, or want a single bill from one inference vendor.

Switch to n4n if: you’re building a product that must survive provider outages, want to A/B across 240+ models without code changes, or need unified per-token metering across vendors. The decision to finish switching from Together AI to n4n usually comes when your on-call starts paging on provider 429s.

Hybrid: Keep Together for training and dedicated loads; route production traffic through n4n for resilience. This is common for teams with existing fine-tunes who still want multi-provider coverage.