Groq vs TokenGO
A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.
Groq compared with TokenGO
| Detail | G
Groq | 
TokenGO |
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| What it does | Premier neocloud for fast inference
| Self-operated inference API for open-weight LLMs and video models
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| Stage | Established
| Not given
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| Founded | Not given
| Not given
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| Based in | US
| US
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| Pricing model | Not given
| Paid
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| Pricing | Not given
| Standard (pay-as-you-go, under $1K/month): List price with unified API, cost monitoring, usage limits, unlimited seats. Growth ($1K-$15K/month): 20% volume discount, quarterly billing, priority support, SLA. Scale ($15K-$40K/month): 30% volume discount, dedicated support, DPA. Custom ($40K+/month):
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| Key features | - GroqMetal: dedicated bare-metal infrastructure tuned for speed and reliability
- GroqCore: production-ready inference stack with no infrastructure expertise required
- GroqAssured: enterprise-grade governance, auditability, and control
- 256 LPUs per rack with 40 PB/s SRAM bandwidth
- 1,000 tokens per second per user throughput
- 128 GB on-chip SRAM per rack
- 315 PFLOPS of FP8 inference compute
- 13 globally distributed data centers across four continents
| - Zero-retention privacy policy
- Smart routing optimization
- Hardware adaptation per request
- Inference engine kernel optimization
- KV cache layer for token reuse
- Unified API across model catalog
- Cost and usage monitoring
- Volume-based tiered discounting
- Data Processing Agreement available
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| What makes it different | - Fast inference at scale without sacrificing affordability
- LPU pioneered technology integrated with NVIDIA GPUs
- Fully integrated platform combining infrastructure, inference, and control
| - Single API key for access to leading models like DeepSeek, Kimi, and Qwen
- Enterprise uptime and service guarantees without separate integrations
- Zero-retention privacy with US governance and DPA support
- Cost-efficiency through kernel optimization adopted by frontier labs
- No integration rewrite required for teams already using OpenAI-compatible APIs
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | 86 ▲ +1 (up) since last check | 17 ● no change since last check |
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| Trust Flow (Majestic) | 37 | 4 |
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| Citation Flow (Majestic) | 56 | 27 |
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| Referring domains (Majestic) | 12552 | 77 |
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4 details filled in for both companies.
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