GroqCloud Platform vs Together AI
Detailed side-by-side comparison to help you choose the right tool
GroqCloud Platform
AI Model APIs
Fast, low-cost AI inference platform for running large language models and other AI workloads.
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Starting Price
CustomTogether AI
🔴DeveloperAI Model Hosting & Inference
AI-native cloud for inference, fine-tuning, and dedicated GPU clusters, offering 200+ open-source and frontier-class models behind an OpenAI-compatible API plus reserved H100/H200/B200 capacity.
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Starting Price
$0.02/1M tokensFeature Comparison
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💡 Our Take
Choose GroqCloud if your bottleneck is raw inference speed and per-token cost on open models — Groq's LPU architecture typically outperforms GPU-based providers on latency. Choose Together AI if you need to fine-tune models, host custom weights, or access a broader catalog of open-source models beyond what Groq has optimized for its hardware.
GroqCloud Platform - Pros & Cons
Pros
- ✓Industry-leading inference speed — customers like Fintool report 7.41x chat speed improvements versus prior GPU-based stacks
- ✓Significant cost reduction at scale, with Fintool reporting 89% cost decrease after switching to GroqCloud
- ✓OpenAI-compatible API means drop-in migration with minimal code changes (just swap base_url and API key)
- ✓Purpose-built LPU silicon (launched 2016) delivers more consistent latency than GPU-shared inference
- ✓Large developer community with 3M+ developers and teams already on the platform
- ✓Day-zero support for new open model releases, including OpenAI's open models in August 2025
Cons
- ✗Limited to inference only — no training, fine-tuning, or model-hosting-for-custom-weights workflows
- ✗Model catalog is narrower than GPU-based competitors that can run any HuggingFace model
- ✗Pricing for high-volume enterprise tiers requires direct sales contact rather than self-serve
- ✗Rate limits on the free tier can constrain prototyping of high-throughput applications
- ✗Dependency on Groq's proprietary hardware stack means vendor lock-in if you rely on unique latency characteristics
Together AI - Pros & Cons
Pros
- ✓Breadth of open-weight model catalog (200+) with one OpenAI-compatible API
- ✓One account spans serverless, dedicated endpoints, fine-tuning, and reserved GPU capacity
- ✓Transparent per-token pricing — easy to model unit economics against closed providers
- ✓InfiniBand-backed GPU Clusters are credible for real training, not just inference
Cons
- ✗Frontier-class reasoning still lags closed models on the hardest benchmarks
- ✗Fastest single-model latency is sometimes beaten by Groq or Cerebras
- ✗Many model variants means model selection itself becomes a project
- ✗Dedicated endpoint cost calculations require attention to GPU type and utilization
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