A marketplace of AI services built to solve the operational challenges that come with running large language models at scale.
SOMA is a marketplace of AI services built to solve the operational challenges that come with running large language models at scale.
Each service available through SOMA addresses a specific bottleneck faced by teams shipping production AI applications. The first service is context compression - a system that reduces the number of tokens required to produce equivalent model outputs. The result is direct and measurable: lower inference costs, faster response times, and significantly more usable space within any model's context window.
Context compression is the first step. SOMA is designed as a long-term platform where additional services will be introduced over time, each targeting a distinct layer of the AI infrastructure stack.
Lower inference costs
Fewer tokens per request means smaller bills across every model and every call.
More usable context
Compressed inputs free space for reasoning, memory, and richer outputs.
Continuously improving
Open competition between providers means the best implementation always wins.
Every service on SOMA operates as an open competition between independent providers. Each provider builds its own implementation - whether that involves a fine-tuned model, a learned encoder, or a proprietary pipeline - and competes to deliver the strongest possible result.
Outputs are continuously assessed by an evaluation layer that measures quality against efficiency. Providers consistently delivering the best results capture a larger share of the network. Underperforming implementations are filtered out automatically.
This architecture produces a system that improves on its own. Customers integrating with SOMA always receive the current best implementation of each service, without the engineering cost of swapping providers or upgrading versions.
SOMA delivers its services through a growing suite of purpose-built tools, each designed to solve a specific operational challenge in modern AI infrastructure. Every tool integrates cleanly into existing production pipelines and delivers measurable improvements from the first call.
SOMARIZER reduces the number of tokens required to produce equivalent model outputs, lowering inference costs, accelerating response times, and freeing significant space within any model's context window.
Coming soon
Stay tuned for updates!
Coming soon
Stay tuned for updates!
SOMA is developed and operated by Dendrite - a technology company founded in 2022, that entered the Bittensor ecosystem early and has since grown into its primary infrastructure architect.
Led by a team of 50+ elite engineers and mathematicians, Dendrite operates at every layer of the ecosystem - from designing high-performance mining infrastructure to launching proprietary subnets and building end-user products like SimplyTao.
SOMA is a marketplace for MCP (Model Context Protocol) services, built for AI agents that need to integrate, coordinate, and execute reliably at scale. The first service live on SOMA is Somarizer, with more MCP services in the pipeline.
MCP stands for Model Context Protocol, an open standard for connecting AI models with external tools, data sources, and execution environments. It gives AI agents a consistent way to exchange context and take action across different systems. SOMA delivers MCP services as a continuously optimized layer, so teams plug in performance instead of building it from scratch.
Context Compression is the process of reducing the number of tokens an AI model needs to produce a given output, while preserving the quality of that output. Fewer tokens translate directly into lower costs, faster responses, and more usable space inside any context window. For production AI workloads, the savings compound with every request.
Right now, SOMA has one live tool: SOMARIZER - a context compression service for AI agents and LLM applications. It plugs into existing pipelines and shrinks input tokens without compromising downstream task performance. More tools are in the works and coming soon.