Anyray
Anyray provides a self-hosted AI gateway that reduces LLM costs by eliminating wasted tokens through just-in-time optimization and token attribution. The platform sits between an organization's applications and any LLM provider, compressing payloads without degrading output quality while ensuring all data remains within the customer's own cloud environment.
- Artificial Intelligence
- AI Agents
- Developer Tools
- Software Only
Funding
Founders
Product
Problem
Organizations using LLMs pay for every token sent to a model, yet a typical request contains 40-70% waste—irrelevant context, redundant system prompts, and unnecessary data—that is billed at full rate. This waste compounds across every call, session, and teammate, inflating costs and degrading performance without adding value to the output.
Solution
Anyray provides a self-hosted AI gateway that sits on the path of every LLM request, from coding assistants like Claude Code, Cursor, and Windsurf to in-house agents, SDK jobs, and scripts. The platform applies just-in-time optimization to compress and streamline payloads before they reach the model, reducing token consumption by 40-70% while preserving output quality. It runs entirely inside the customer's own cloud environment, ensuring prompt and response content never leaves their network, with encryption at rest and no plaintext logging. The gateway works with any provider and any model, requiring no changes to existing workflows or IT overhead, and delivers per-workload savings metrics across coding, research, analysis, and operations use cases.
Target Audience
Primary customers are engineering and platform teams at organizations running LLM workloads at scale—including those using coding assistants, in-house agents, and automated scripts—who need aggressive cost control while maintaining strict data privacy and security requirements.
Features
- Self-hosted deployment that runs inside the customer's cloud, with prompt and response content never leaving their environment
- Provider-agnostic gateway supporting any LLM model, from coding assistants to in-house agents and SDK jobs
- Just-in-time payload compression that reduces token waste by 40-70% without degrading output quality
- Content-free usage rollup for metering that sends only request, token, cost, and savings totals to the Portal, never user identities or prompt content
- Open documentation, OpenAPI specification, and install guides published on GitHub for evaluation and deployment without a sales gate
- Per-workload savings reporting that quantifies cost reduction across coding agents, research, analysis, and operations