Edgee

Product Introduction

1. Definition: Edgee is an AI Gateway operating as a reverse proxy layer between applications and Large Language Model (LLM) providers. It functions as an edge intelligence platform, processing and optimizing AI traffic in real-time.
2. Core Value Proposition: Edgee exists to significantly reduce LLM token costs (up to 50%) and latency by intelligently compressing prompts before they reach providers like OpenAI or Anthropic, while preserving semantic intent. It provides centralized cost governance, observability, and routing for enterprise AI deployments.

Main Features

1. Token Compression:
   How it works: Edgee applies proprietary semantic compression algorithms at the edge to analyze and optimize prompts. It identifies and removes redundant tokens, repetitive phrases, and non-essential syntax without altering the core meaning or intent. This is particularly impactful for long-context prompts, RAG payloads, and multi-turn agent conversations. The compressed prompt is then sent to the chosen LLM provider (e.g., gpt-4o, claude-3), reducing input token consumption and associated costs.
2. Edge Tools:
   Enables deployment and invocation of serverless functions (tools) directly on Edgee's global edge network. Users can run shared tools managed by Edgee or deploy private custom tools. This reduces latency for tool-augmented generation (e.g., function calling) by executing logic closer to users and LLM providers, improving response times and control.
3. Bring Your Own Keys (BYOK):
   Offers flexibility in billing. Users can leverage Edgee's managed keys for convenience or integrate their own provider API keys (OpenAI, Anthropic, Gemini, etc.) directly. This allows granular cost control, access to custom models via provider accounts, and avoids vendor lock-in.
4. Observability & Cost Governance:
   Provides detailed real-time monitoring of API traffic. Users can tag requests with custom metadata (e.g., team:analytics, feature:reports). The system tracks token usage, costs, latency, and errors per tag, model, app, and environment. Configurable cost alerts trigger notifications for spending spikes (e.g., "Tag feature:reports exceeded $500 in 24h"), enabling proactive budget management.
5. Edge Models:
   Allows deployment and execution of small, optimized ML models (e.g., classifiers, redactors, routers) directly on Edgee's edge infrastructure. These models pre-process requests (e.g., PII redaction, intent classification, request enrichment) before reaching primary LLMs, reducing latency and cost for upstream providers.
6. Private Models:
   Users can deploy open-source LLMs (e.g., Llama 3, Mixtral) as serverless endpoints within Edgee's infrastructure. These private models are exposed through the same unified Edgee API gateway alongside public providers, enabling hybrid model strategies without managing infrastructure.

Problems Solved

1. Pain Point: Exorbitant LLM token costs, especially for complex applications involving long contexts, RAG, or multi-agent systems, where prompt payloads are large and inefficient. Manual prompt optimization is time-consuming and unsustainable at scale.
2. Target Audience:
   • Developers & Engineering Teams: Building production AI applications (chatbots, agents, RAG systems) needing cost-effective LLM integration.
   • AI Product Managers: Responsible for feature ROI and managing escalating AI operational expenses.
   • DevOps/SRE Teams: Requiring observability, reliability, and cost controls for AI infrastructure.
   • Enterprises: Scaling AI usage across teams while needing centralized governance, security (SOC 2/GDPR), and cost tracking.
3. Use Cases:
   • Cost Reduction for RAG: Compressing large retrieved context chunks before feeding to LLMs.
   • Optimizing Multi-Turn Agents: Reducing cumulative token usage in conversational agent loops.
   • Centralized AI Gateway: Providing a single, secure, observable entry point for multiple LLM providers and private models.
   • Spending Control: Tagging and alerting on costs per project, team, or feature to prevent budget overruns.
   • Low-Latency Tool Use: Running custom logic (e.g., data enrichment, PII scrubbing) at the edge before LLM calls.

Unique Advantages

1. Differentiation: Unlike basic API gateways or manual optimization, Edgee provides intelligent, semantic-aware token compression as a core service at the edge. It surpasses competitors by combining this with integrated cost governance (tagging/alerting), edge-native tool/model execution, and multi-provider normalization in one platform. Unlike provider-specific optimizations, Edgee is universally compatible (OpenAI, Anthropic, Gemini, xAI, Mistral, private OSS).
2. Key Innovation: The core innovation is its edge-based semantic compression engine. This technology dynamically analyzes and optimizes prompts in flight across a global network of 100+ Points of Presence (PoPs), significantly reducing token payloads while demonstrably preserving intent and output quality, as evidenced by their benchmark claims. The integration of serverless edge compute for tools and models further reduces round-trip latency and processing overhead.

Frequently Asked Questions (FAQ)

1. How does Edgee reduce LLM token costs without changing output quality?
   Edgee uses advanced semantic analysis algorithms at the edge to identify and remove redundant tokens and non-essential syntax from prompts while preserving core meaning and intent. This compression happens before the prompt reaches the LLM provider, directly reducing the input token count billed by providers like OpenAI or Anthropic, leading to lower costs without significant impact on response quality.
2. Is Edgee compatible with all major LLM providers?
   Yes, the Edgee AI Gateway provides universal LLM compatibility. It seamlessly integrates with OpenAI, Anthropic, Google Gemini, xAI (Grok), Mistral, and allows deployment of private open-source models (e.g., Llama 3, Mixtral). Requests are normalized through Edgee's single API endpoint.
3. What level of cost savings can I expect with Edgee's token compression?
   Edgee typically achieves up to 50% reduction in input tokens for eligible prompts, especially effective for long-context scenarios, RAG applications, and multi-turn agent conversations. Actual savings depend on prompt structure and length, but significant cost reduction on LLM API bills is the primary outcome.
4. How does Edgee ensure the security and privacy of my prompts and data?
   Edgee is designed with enterprise-grade security, adhering to SOC 2 and GDPR compliance standards. It operates as a secure gateway, and users can leverage features like Bring Your Own Keys (BYOK) and deploy private models/tools on the edge network for enhanced data control. Data processing occurs within its secure global infrastructure.
5. Can Edgee help me track and control spending across different teams or projects?
   Absolutely. Edgee's cost governance features allow you to tag API requests with custom metadata (e.g., team:marketing, project:chatbot). You can then monitor token usage, costs, and latency per tag in real-time and set up custom cost alerts to notify you immediately of unexpected spending spikes per team, project, or feature.