Comparing dedicated Tensor Streaming LPU silicon against the premier open-source GPU cloud. Evaluate token pricing, throughput speed, fine-tuning capabilities, and dedicated endpoint economics.
Calculate monthly bills across Groq LPUs versus Together AI serverless GPUs.
Evaluating architecture specifications, benchmark rates, throughput latency, and pricing tiers.
| Dimension | Groq LPUs | Together AI | Key Takeaway / Advantage |
|---|---|---|---|
| Llama 3.3 70B Throughput | ~300 tokens/sec | ~110 tokens/sec | Groq (2.7x faster) |
| Time-To-First-Token (TTFT) | 120 - 150 ms | 200 - 250 ms | Groq (Faster) |
| 70B Blended Token Rate | $0.59 in / $0.79 out | $0.88 in / $0.88 out | Groq (Up to 33% cheaper input) |
| Model Fine-Tuning Support | None (Closed hardware compilation) | Complete API for LoRA & weights | Together AI |
| Model Selection Breadth | Curated top ~10 open models | 100+ open-source models & checkpoints | Together AI |
| Dedicated Enterprise Instances | Reserved racks available via sales | Instant self-serve GPU instances | Together AI |
Groq is the performance king for standard open weights: If your application serves real-time interactive user experiences (voice assistants, typing copilots, chat widgets) and utilizes standard foundation models (Llama 3.3 70B, Llama 3.1 8B, or Mixtral), Groq's 300+ tokens/sec speed and lower token pricing make it unbeatable.
Together AI is the full-stack developer cloud: If you need to fine-tune a model on private enterprise JSON data, deploy a custom adapter, or host specialized open models not compiled on Groq's hardware, Together AI provides unmatched flexibility, dedicated instance options, and rapid new model deployments.
Evaluating real monthly unit economics across practical engineering architectures.
Voice bot answering customer inquiries over telephony requiring instant text streaming.
Training and serving a LoRA fine-tuned checkpoint on proprietary clinical notes.
from openai import OpenAI
# 1. Groq Client (Fastest)
groq = OpenAI(base_url="https://api.groq.com/openai/v1", api_key="YOUR_GROQ_KEY")
res_groq = groq.chat.completions.create(
model="llama-3.3-70b-versatile",
messages=[{"role": "user", "content": "Summarize quantum entanglement in 2 sentences."}]
)
# 2. Together AI Client (Flexible)
together = OpenAI(base_url="https://api.together.xyz/v1", api_key="YOUR_TOGETHER_KEY")
res_together = together.chat.completions.create(
model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
messages=[{"role": "user", "content": "Summarize quantum entanglement in 2 sentences."}]
)Real-world engineering trade-offs and hidden bill shock traps to watch out for.
Explore complementary benchmarks and financial modeling tools across the index.
LPU speed battle against wafer-scale CS-3 engines.
Head-to-head comparison of leading open-weights GPU clouds.
Complete LPU hardware specifications and pricing tiers.
Full serverless model pricing and dedicated cluster rates.
Yes. On Llama 3.3 70B, Groq generates approximately 300 tokens per second compared to Together AI's ~110 tokens per second.
Groq charges $0.59/M input and $0.79/M output. Together AI charges $0.88/M input and $0.88/M output for Turbo. Groq is approximately 10% to 33% cheaper.
No, Groq does not currently support custom model fine-tuning. Together AI provides a complete end-to-end API for fine-tuning open models on private datasets.
Yes, both Groq and Together AI are 100% compatible with the OpenAI SDK by adjusting the base_url.
Groq uses proprietary Language Processing Units (LPUs) with high-speed on-chip SRAM. Together AI operates large clusters of NVIDIA H100, A100, and L40S GPUs.
Groq's 140ms Time-To-First-Token and 300 tok/sec throughput makes it the preferred engine for low-latency conversational telephony.