TokenGO vs Wirkung
A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.
TokenGO compared with Wirkung
| Detail | 
TokenGO | 
Wirkung |
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| What it does | Self-operated inference API for open-weight LLMs and video models
| Everything has an algorithm
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| Stage | Not given
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| Founded | Not given
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| Based in | US
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| Pricing model | Paid
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| Pricing | 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 | - 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
| - Constant-state attention: models serve any context length from the same memory per request
- HCGM: models run from disk storage instead of memory
- M-Rail: context processed by a model can be reused instantly in future requests
- CPUDA/sGPU: run unmodified CUDA programs on CPUs with bit-identical results
- Organ grafting: transfer capabilities between models without training
- Personal instruction set (ISA) compiled on device from phone signals
- Population ISA: compile laws from many people without personal records
- Digital twins of customers, fleets, cities or markets
- Results identical across all machines and architectures
- Computation cost follows novelty not data size
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| What makes it different | - 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
| - Constant memory cost regardless of context length or data size
- Exact reuse: every computed result is kept and reused instantly
- Portable execution with bit-identical results across all hardware
- Models run from storage without retraining or fitting in memory
- Privacy-preserving: raw data never leaves the device
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | 17 ● no change since last check | Not checked yet ● no change since last check |
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| Trust Flow (Majestic) | 4 | 0 |
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| Citation Flow (Majestic) | 27 | 0 |
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| Referring domains (Majestic) | 77 | 0 |
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3 details filled in for both companies.
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