Comparison Intermediate · 3 min read

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

python

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

python

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.

Verified 2026-04 · meta-llama/Llama-3.3-70B-Instruct-Turbo, llama-3.3-70b-versatile

Verify ↗

Community Notes

No notes yetBe the first to share a version-specific fix or tip.