Together AI Turbo Serverless vs Dedicated Endpoint Calculator

# together_turbo_stream.py - High-throughput inference with Together AI Turbo
import os
import asyncio
from together import AsyncTogether

client = AsyncTogether(api_key=os.environ.get("TOGETHER_API_KEY"))

async def stream_inference(prompt: str):
    # Model: Llama-3.3-70B-Instruct-Turbo ($0.88 / 1M tokens blended)
    stream = await client.chat.completions.create(
        model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
        messages=[{"role": "user", "content": prompt}],
        max_tokens=1024,
        temperature=0.7,
        stream=True
    )
    
    full_text = ""
    async for chunk in stream:
        token = chunk.choices[0].delta.content or ""
        full_text += token
    return full_text

# Execute 10 parallel requests concurrently with zero infrastructure management
async def main():
    prompts = [f"Synthesize architectural trade-offs for microservice batch #{i}" for i in range(10)]
    results = await asyncio.gather(*[stream_inference(p) for p in prompts])
    print(f"✓ Completed {len(results)} inference tasks successfully.")

asyncio.run(main())

Together AI Architecture: Turbo Shared Endpoints vs Dedicated GPU Instances TCO

Deploying open-weights foundation models on Together AI involves deciding between Serverless Turbo Endpoints (pay-per-token, $0.88/1M for Llama-3.3-70B) and Dedicated GPU Endpoints ($1.75 to $3.50 per GPU-hour). While Turbo endpoints eliminate idle hardware costs and scale instantaneously to hundreds of concurrent requests, dedicated instances guarantee zero cold-start queuing, consistent sub-30ms time-to-first-token (TTFT), and fixed monthly compute costs for high-throughput enterprise pipelines.

Production Pipeline: Together Streaming Fallback Router in Python

When using shared Turbo endpoints, traffic surges can trigger transient queue delays. Deploying an automated fallback to dedicated or alternative providers ensures 99.9% uptime:

from together import Together
import time

client = Together()

def stream_with_timeout(prompt: str, timeout_seconds: float = 2.0):
    start_time = time.time()
    try:
        response = client.chat.completions.create(
            model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
            messages=[{"role": "user", "content": prompt}],
            stream=True
        )
        for chunk in response:
            # Enforce TTFT SLA: if first token takes >2s, fallback to dedicated endpoint
            if time.time() - start_time > timeout_seconds:
                print("[Router] First token SLA exceeded; falling back to dedicated cluster")
                return fallback_to_dedicated(prompt)
            yield chunk.choices[0].delta.content or ""
    except Exception as e:
        print(f"[Router] Turbo error: {e}, cascading to fallback")
        return fallback_to_dedicated(prompt)

Production Best Practices & Concurrency Guardrails

• Breakeven Token Volume: At $0.88/1M tokens, dedicated 4x L40S hardware ($2,160/mo) becomes cheaper when monthly volume surpasses 2.45 billion tokens (~80 million tokens/day).
• Custom LoRA Adapter Support: Together AI allows hot-swapping fine-tuned LoRA adapters directly on Turbo serverless endpoints with zero cold start latency and zero extra GPU hosting fees.
• Private VPC Peering: Dedicated endpoints support AWS PrivateLink and GCP VPC peering, ensuring compliance requirements that forbid inference data transit over public internet backbones.