Switching from Together AI to a multi-provider gateway

Most teams start with a single specialized inference vendor because it’s simple. If you’re considering switching from Together AI API to gateway-based routing, the decision isn’t about which hosts Llama 3 better—it’s about whether one vendor’s model catalog and SLA match your evolving needs. A gateway abstracts dozens of providers behind one OpenAI-compatible endpoint, while Together remains a focused platform for open-weight models with extras like fine-tuning and dedicated capacity.

Together AI: the single-vendor baseline

Together AI runs its own GPU fleet and serves open-weight models (Llama, Mistral, Qwen, etc.) through a proprietary SDK and an OpenAI-compatible route. You get predictable performance on pinned models, fine-tuning jobs, LoRA adapters, and batch endpoints. For teams that live entirely in the open-model world, the surface area is small.

from together import Together

client = Together(api_key="together-xxx")
resp = client.chat.completions.create(
    model="meta-llama/Llama-3-70b-chat-hf",
    messages=[{"role": "user", "content": "Summarize this log"}],
    max_tokens=128,
)
print(resp.choices[0].message.content)

The native client exposes vendor-specific knobs (e.g., repetition_penalty, logprobs), and the platform bundles a playground, eval UI, and job tracking. Rate limits are account-scoped and enforced by Together’s infrastructure.

What a multi-provider gateway changes

A gateway sits in front of many inference providers—Together, Anthropic, OpenAI, Groq, Fireworks, and others—and presents one interface. For example, n4n.ai exposes a single OpenAI-compatible endpoint covering 240+ models and automatically fails over when a provider is rate-limited or degraded. Your code stops caring which backend answered.

from openai import OpenAI

# Point the standard OpenAI client at the gateway instead
client = OpenAI(
    base_url="https://api.n4n.ai/v1",
    api_key="gateway-xxx",
)
resp = client.chat.completions.create(
    model="anthropic/claude-3.5-sonnet",
    messages=[{"role": "user", "content": "Summarize this log"}],
    max_tokens=128,
    # Gateway honors routing directives via headers
    extra_headers={"x-n4n-routing": "prefer:anthropic,fallback:openai"},
)

The gateway forwards provider cache-control hints and returns per-token usage metering. You trade a thin proxy hop for provider diversity and a single integration point.

Head-to-head comparison

Capabilities

Together gives you fine-tuning and LoRA on open models—functionality a generic gateway typically does not host itself. But a gateway can still call Together as one of its backends, so switching from Together AI API to gateway does not mean losing Llama access; it means gaining Claude, GPT, and Groq behind the same call. The tradeoff is that gateway-level features (routing, fallback) are orthogonal to model training.

Price/cost model

Together publishes its own per-token rates. You pay their margin and any add-on for fine-tuning. A gateway usually meters exactly what the upstream provider charges, sometimes adding a flat basis-point fee or subscription. Because the gateway normalizes usage objects, you get one invoice line instead of five.

{
  "model": "meta-llama/Llama-3-70b-chat-hf",
  "usage": {
    "prompt_tokens": 12,
    "completion_tokens": 34,
    "total_tokens": 46,
    "provider": "together",
    "cost_usd": 0.00052
  }
}

If you already fine-tune on Together, keep that job there and route only inference through the gateway.

Latency/throughput

Together’s colocated stack gives consistent time-to-first-token for a pinned model. A gateway introduces a proxy round-trip (typically 5–20 ms) but can eliminate minutes-long outages by retrying on a second provider. For high-QPS workloads, run a benchmark against your exact prompt shape before migrating; open-weight models on Together are often faster than the same weights on a crowded multi-tenant gateway.

Ergonomics

The migration is mostly a base_url swap if you already used Together’s OpenAI-compatible route. The native Together SDK has extra fields; those must be mapped or dropped. Gateways standardize on OpenAI’s parameter set, so you lose vendor esoterica but gain portability.

# Before: Together native
from together import Together
client = Together()

# After: gateway via OpenAI client
from openai import OpenAI
client = OpenAI(base_url="https://api.gateway.example/v1")

Ecosystem

Together’s playground and eval tools are useful but lock insights to their UI. A gateway lets you use any OpenAI-compatible observability stack (LangSmith, Helicone, your own middleware). For teams standardizing on the OpenAI interface, the gateway is strictly less glue.

Limits

Together enforces its own RPM/TPM caps. When you hit them, you get a 429 and must back off. A gateway can detect the 429 and route to an alternate provider that still has headroom, then surface a normalized error only if all paths are exhausted. This is the single most cited reason engineers pursue switching from Together AI API to gateway in production.

Migration steps that don’t bite

1. Inventory model IDs. Together uses slugs like meta-llama/Llama-3-70b-chat-hf. Gateways often namespace by provider: together/llama-3-70b. Build a mapping table.
2. Swap the client. Change base_url and auth. Keep the same messages shape.
3. Strip vendor params. Remove logprobs or repetition_penalty if the gateway doesn’t forward them.
4. Add routing headers only where you need pinning (e.g., regulatory need to stay on a specific cloud).
5. Shadow traffic. Run both clients in parallel for a week; compare outputs and cost.

import random
def call_llm(msgs):
    if random.random() < 0.1:  # 10% shadow to gateway
        return gateway_client.chat.completions.create(model="together/llama-3-70b", messages=msgs)
    return together_client.chat.completions.create(model="meta-llama/Llama-3-70b-chat-hf", messages=msgs)

Which to choose

Stay on Together AI if:

• You depend on fine-tuning or LoRA on open models and don’t want to manage that elsewhere.
• Your traffic is steady, single-model, and fits comfortably under their rate limits.
• You want a curated open-model experience with first-party eval tooling.

Move to a multi-provider gateway if:

• You need high availability and can’t afford a single vendor’s 429 storm—automatic fallback is the killer feature.
• Your product mixes proprietary and open models (Claude for reasoning, Llama for cheap classification).
• You want one OpenAI-compatible integration for your entire engineering org and per-token metering across providers.
• You are already evaluating switching from Together AI API to gateway as part of a broader move off single-vendor risk.

Hybrid (recommended for most scaling teams): Keep Together for training and batch jobs; route interactive traffic through a gateway that includes Together as a backend. You get fine-tune control without sacrificing failover. This pattern adds one base_url change and pays for itself the first time a provider has a bad afternoon.