Evaluating the two leading open-source model inference clouds. Dive into token pricing, FireAttention vs Together Turbo throughput, LoRA fine-tuning APIs, and dedicated GPU cluster economics.
Calculate monthly bills across Together AI and Fireworks AI for Llama 3.3 70B and DeepSeek models.
Evaluating architecture specifications, benchmark rates, throughput latency, and pricing tiers.
| Dimension | Together AI | Fireworks AI | Key Takeaway / Advantage |
|---|---|---|---|
| Llama 3.3 70B In / Out | $0.88 / M (In & Out) | $0.90 / M (In & Out) | Together AI (-2.2%) |
| Llama 3.1 8B In / Out | $0.18 / M | $0.20 / M | Together AI (-10%) |
| DeepSeek-V3 / R1 In / Out | $1.25 / M | $0.90 / M | Fireworks AI (-28%) |
| Inference Latency (TTFT) | 180–240 ms | 140–190 ms (FireAttention) | Fireworks AI |
| LoRA Deployment Surcharge | Zero surcharge for serverless LoRA | Instant LoRA hot-swapping | Tie |
| Function Calling Accuracy | Standard Hermes/Llama templates | FireFunction-v2 specialized model | Fireworks AI |
| Private VPC / Cloud Peering | AWS & GCP VPC Peering supported | Enterprise On-Prem / VPC available | Tie |
Both Together AI and Fireworks AI are phenomenal platforms that eliminate the pain of orchestrating Kubernetes clusters for vLLM or TensorRT-LLM. Token pricing for standard models like Llama 3.3 70B is virtually tied ($0.88/M vs $0.90/M). However, their technical architecture targets subtly different engineering needs.
Fireworks AI excels in raw runtime performance and compound agent pipelines. Its proprietary FireAttention engine reduces Time-To-First-Token (TTFT) by 25–35% compared to standard vLLM stacks, and its custom FireFunction-v2 model delivers GPT-4 level JSON tool-calling on open-source weights. Conversely, Together AI provides a superior end-to-end training-to-inference lifecycle with full-parameter fine-tuning, extensive image/video models, and an unmatched catalog of over 200 open-source architectures.
Evaluating real monthly unit economics across practical engineering architectures.
Streaming multi-turn dialogue where TTFT under 200ms is essential for user experience. Fireworks AI FireAttention delivers superior responsiveness.
Training a 70B model on proprietary legal or medical contracts and serving hot-swapped LoRA adapters without dedicated GPU provisioning fees.
Processing 100M tokens monthly of deep analysis. Fireworks AI offers DeepSeek-V3 at $0.90/M versus Together AI's $1.25/M, saving $35/mo per 100M.
import os
from openai import OpenAI
# Both providers support standard OpenAI SDK compatibility
together_client = OpenAI(
api_key=os.environ.get("TOGETHER_API_KEY"),
base_url="https://api.together.xyz/v1"
)
fireworks_client = OpenAI(
api_key=os.environ.get("FIREWORKS_API_KEY"),
base_url="https://api.fireworks.ai/inference/v1"
)
def query_llama_70b(prompt: str, provider: str = "together"):
"""Run Llama 3.3 70B with sub-second streaming on either cloud."""
client = together_client if provider == "together" else fireworks_client
model = (
"meta-llama/Llama-3.3-70B-Instruct-Turbo"
if provider == "together"
else "accounts/fireworks/models/llama-v3p3-70b-instruct"
)
response = client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": prompt}],
temperature=0.3,
max_tokens=1024
)
return response.choices[0].message.contentReal-world engineering trade-offs and hidden bill shock traps to watch out for.
Explore complementary benchmarks and financial modeling tools across the index.
Review dedicated instance rates and fine-tuning costs.
Explore FireAttention benchmarks and function calling guides.
Compare Groq LPUs against Together AI serverless GPUs.
Rankings of the lowest cost token providers on the market.
For standard Llama 3.3 70B, Together AI is slightly cheaper ($0.88/M vs $0.90/M). However, for DeepSeek-V3, Fireworks AI is significantly cheaper ($0.90/M vs $1.25/M). For 8B models, Together AI is $0.18/M vs Fireworks $0.20/M.
Fireworks AI built FireAttention, an optimized CUDA inference kernel designed to replace vLLM PagedAttention. It minimizes memory bandwidth bottlenecks, reducing Time-To-First-Token by roughly 25–35% during high concurrency.
Yes. Both Together AI and Fireworks AI allow you to upload Hugging Face LoRA weights and call them on serverless base models using the model='model-name:lora-id' parameter without paying for dedicated GPUs.
Yes, both support JSON Schema and response_format parameters. Fireworks AI has an edge with its FireFunction-v2 model, which is specifically trained for multi-turn function calling.
Together AI charges approximately $3.50–$4.50/hour per 8x H100 node. Fireworks AI offers enterprise reserved capacity with custom multi-cloud SLA agreements.
Neither platform uses enterprise API inputs or outputs to train foundation models. Both offer zero-data-retention (ZDR) agreements on enterprise plans.