Edgee
Edgee is an AI gateway that sits between your application and large language model providers, acting as an edge intelligence layer that compresses prompts before they reach the model to reduce token usage, lower costs, and improve latency without changing your existing code. Applications call Edgee through a single OpenAI-compatible API, and Edgee applies edge-level policies such as intelligent token compression, routing, privacy controls, retries, caching, and cost governance before forwarding requests to the selected provider, including OpenAI, Anthropic, Gemini, xAI, and Mistral. Its token compression engine removes redundant input tokens while preserving semantic intent and context, achieving up to 50% input token reduction, which is especially valuable for long contexts, RAG pipelines, and multi-turn agents. Edgee enables tagging requests with custom metadata to track usage and spending by feature, team, project, or environment, and provides cost alerts when spending spikes.
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GPT-Image-2.5 Flare
GPT-Image-2.5 Flare is a hosted image-generation model available through the OpenAI API. It accepts text prompts and image inputs and produces image outputs. Developers can call it through the Images API to generate images or edit supplied images, or use it as an image-generation tool within Responses API workflows that support conversations and multiple steps. Requests can set image quality to low, medium, high, xhigh, max, or auto, and configure output size, format, and compression. OpenAI positions Flare for fast, everyday image generation, while its GPT-Image-2.5 Sunburst model is intended for workflows prioritizing precise editing. The model is designed for developers and organizations adding image creation or editing to their own applications. Access requires API credentials, and usage is billed by tokens, with rates depending on text or image input, image output, and standard or batch processing.
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condense.chat
condense.chat is an LLM input compression API and drop-in proxy that shrinks prompts, retrieved documents, tool outputs, and repeated agent context before they hit upstream models. Less context, same Claude Code; its harness intercepts an agentās growing session history and passes it through compression models before it reaches the main model, helping long-running coding agents start each next turn with fewer tokens. Condense sits between an app and the upstream LLM provider, tracks the conversation as a content-addressed chain, and transparently compresses repeated context on the way upstream. Developers can point their SDK at the Condense provider route, add a Condense key, keep their existing provider key, and change nothing else. It supports Anthropic and OpenAI-compatible routes, plus pass-through behavior for other provider paths such as model lists and embeddings.
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Oridica
Ordica is an AI infrastructure layer designed to reduce the cost of using large language models by compressing prompts before they are sent to providers like GPT-4o, Claude, Gemini, or Grok. It operates as a lightweight proxy that sits directly in the request path, requiring no new dependencies. Users simply point their existing SDK to Ordicaās endpoint and continue using their current API keys unchanged. It processes prompts entirely in memory, compressing them in transit and forwarding them to the selected provider without storing, logging, or retaining any message content, ensuring that data privacy is preserved at every step. Ordica dynamically decides whether to compress a request based on confidence thresholds; if compression is expected to preserve output quality, it reduces token usage; if not, the request passes through unchanged, guaranteeing no degradation in responses. This approach allows developers to achieve measurable cost savings across different workloads.
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