LeanCTX vs Kinetic
A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.
LeanCTX compared with Kinetic
| Detail | 
LeanCTX | 
Kinetic |
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| What it does | The Context Gateway for AI systems
| Open-source engine for faster local language model inference with bit-for-bit identical output
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| Stage | Not given
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| Founded | Not given
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| Based in | CH
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| Pricing model | Freemium
| Open source
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| Pricing | Community: Free (open-source, local use). Pro: $9/month or $90/year (personal cloud sync, 1 GB hosted index). Team: $29/month per seat or $290/year per seat (team server, shared workspace, 20 GB index, 1-25 seats). Enterprise: Contact for pricing.
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| Key features | - Source selection and retrieval with task-relevant context at configurable depth
- Access controls via PathJail boundary enforcement and role-based permissions
- Read-time content filters for injection patterns and personal data detection
- Memory and continuity to retain findings and connections across sessions
- Evidence and outcomes recording with source references and usage audit trails
- Integration with AI tools via SDK, MCP, hooks and supported client adapters
- Local, VPC, on-premises and offline deployment options
- Optional production intelligence for planning, ranking and routing enhancements
- SDK with five stable primitives and six Agent Tools language contracts
- Context cache, reference routing and usage accounting
| - Speculative decoding for faster token generation
- Prompt caching for context reuse
- Real-time prompt processing while typing
- Concurrent request batching
- Bit-for-bit identical output verification
- Multi-GPU and multi-backend support (CUDA, Vulkan)
- Vision model support with image encoding
- Mixture-of-experts model support
- RAD-1 automatic draft token generation
- Cross-platform support for Windows and Linux
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| What makes it different | - Deterministic, model-agnostic context processing independent of AI provider choice
- Governs context without controlling model calls, keeping AI decisions under user ownership
- Records measurable impact with 64.1% reported context reduction in field use
- Open-source core (Apache 2.0) with commercial tiers for teams and enterprises
- Transparent governance with separate evaluation of task outcome from context delivery
| - Exact deterministic inference: all default optimizations preserve bit-for-bit identical output compared to plain decoding
- 4.5× faster performance on editing tasks on small models compared to llama.cpp
- Verifiable reproducibility through receipt system that records all request parameters
- Nine optimization techniques enabled by default with ability to disable each individually
- No dependency on draft models or external selections for speculative decoding
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | 42 ● no change since last check | Not checked yet ● no change since last check |
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| Trust Flow (Majestic) | 3 | 0 |
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| Citation Flow (Majestic) | 35 | 11 |
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| Referring domains (Majestic) | 212 | 2 |
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4 details filled in for both companies.
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