Groq vs Together AI
Side-by-side — pricing, strengths, tradeoffs, and fit.
Groq
Ultra-fast LLM inference powered by custom LPU hardware.
Together AI
Fast, low-cost inference for open-source LLMs, plus fine-tuning.
At a glance
Free TierEnterprise
· Not enough data·From ~$0.05/1M tokens
Free TierEnterprise
● Established·From Usage-based (per token)
Overview
One-liner
One-liner
Ultra-fast LLM inference powered by custom LPU hardware.
One-liner
Fast, low-cost inference for open-source LLMs, plus fine-tuning.
Maturity
Maturity
Not enough dataMaturity
EstablishedOpen source
Open source
✕ NoOpen source
✕ NoPricing
Free tier
Free tier
✓ AvailableRate-limited dev access — 30 req/min, 14,400 req/day, 6,000 tokens/min
Free tier
✓ AvailableFree credits for new users
Starts at
Starts at
~$0.05/1M tokens
Starts at
Usage-based (per token)
Model
Model
Usage-based
Model
Usage-based
Enterprise
Enterprise
✓ AvailableEnterprise
✓ AvailableBest for
✓ Best for
Teams building real-time applications requiring fast inference speeds.
Projects where low latency is critical for user experience.
Developers needing access to large models without managing infrastructure.
✓ Best for
Serving open-source LLMs cheaplyFast API over many open models
Fine-tuning and dedicated hostingCustomize and scale
Not ideal for
✕ Not for
Budget-constrained projects that cannot afford usage-based pricing.
Applications with very high request volumes, potentially leading to high costs.
✕ Not for
—
Strengths
↑ Strengths
Fastest inference speed on the market
OpenAI-compatible API
Generous free tier for development
Competitive per-token pricing
↑ Strengths
Huge open-model catalog
Competitive token pricing
Fine-tuning + dedicated endpoints
Weaknesses
↓ Weaknesses
Limited model selection
No fine-tuning or private model hosting
Rate limits on free tier
Context window smaller than frontier models
↓ Weaknesses
Cold starts on serverless