Together AI vs Groq speed comparison
Quick answer
Together AI and Groq both offer high-performance AI inference with low latency. Groq generally delivers faster response times due to its specialized hardware acceleration, while Together AI provides competitive speeds with strong model support and scalability.
VERDICT
For raw inference speed, Groq is the winner due to its hardware-optimized architecture; use Together AI when you need a broader model ecosystem with solid speed and flexibility.
| Tool | Key strength | Pricing | API access | Best for |
|---|---|---|---|---|
| Together AI | Wide model selection, strong community | Check pricing at https://together.xyz/pricing | OpenAI-compatible API | Versatile AI workloads with good speed |
| Groq | Ultra-low latency, hardware acceleration | Check pricing at https://groq.com/pricing | OpenAI-compatible API | High-speed inference and real-time applications |
| Together AI | Easy integration, multi-model support | Freemium with paid tiers | REST API with OpenAI SDK support | Developers needing flexibility |
| Groq | Optimized for large LLMs | Enterprise-focused pricing | REST API with OpenAI SDK support | Latency-sensitive production systems |
Key differences
Groq leverages custom hardware accelerators to achieve lower latency and faster throughput compared to Together AI, which runs on cloud GPU infrastructure. Together AI offers a broader range of large language models including meta-llama/Llama-3.3-70B-Instruct-Turbo, while Groq focuses on optimized versions of llama-3.3-70b-versatile for speed. Integration-wise, both provide OpenAI-compatible APIs, but Groq targets enterprise users with emphasis on speed-critical applications.
Side-by-side example: Together AI
from openai import OpenAI
import os
client = OpenAI(api_key=os.environ["TOGETHER_API_KEY"], base_url="https://api.together.xyz/v1")
response = client.chat.completions.create(
model="meta-llama/Llama-3.3-70B-Instruct-Turbo",
messages=[{"role": "user", "content": "Explain the speed advantages of Groq."}]
)
print(response.choices[0].message.content) output
Groq achieves ultra-low latency by using custom hardware accelerators designed specifically for large language model inference, enabling faster response times compared to general GPU-based cloud services.
Side-by-side example: Groq
from openai import OpenAI
import os
client = OpenAI(api_key=os.environ["GROQ_API_KEY"], base_url="https://api.groq.com/openai/v1")
response = client.chat.completions.create(
model="llama-3.3-70b-versatile",
messages=[{"role": "user", "content": "Explain the speed advantages of Groq."}]
)
print(response.choices[0].message.content) output
Groq's hardware acceleration and optimized model execution pipelines deliver significantly faster inference speeds, making it ideal for latency-sensitive AI applications.
When to use each
Use Groq when your application demands the fastest possible inference latency, such as real-time AI services or high-frequency trading. Choose Together AI for broader model availability, easier integration, and when you need a balance of speed and flexibility.
| Scenario | Recommended Tool | Reason |
|---|---|---|
| Real-time low-latency inference | Groq | Hardware acceleration delivers minimal latency |
| Multi-model experimentation | Together AI | Supports diverse models with flexible API |
| Enterprise production with speed focus | Groq | Optimized for large-scale, fast inference |
| General AI development and prototyping | Together AI | Ease of use and model variety |
Pricing and access
| Option | Free | Paid | API access |
|---|---|---|---|
| Together AI | Yes, with limits | Yes, usage-based | OpenAI-compatible API with base_url https://api.together.xyz/v1 |
| Groq | Limited or enterprise trial | Enterprise pricing | OpenAI-compatible API with base_url https://api.groq.com/openai/v1 |
Key Takeaways
- Groq offers superior inference speed due to custom hardware acceleration.
- Together AI provides a wider model selection with competitive speed and easier integration.
- Choose Groq for latency-critical production; choose Together AI for flexibility and model variety.