Overview

Alephant is an AI Gateway for cost control, multi-provider routing, and agent observability.

It sits between your application and AI model providers, giving teams one unified layer to route requests, track token usage, control budgets, inspect agent activity, and understand where AI spend comes from.

With Alephant, every AI request becomes observable, attributable, and controllable.

Why Alephant

AI applications and agents often call multiple model providers, tools, and workflows across users and sessions. As usage grows, teams need to answer operational questions such as:

• Which user, agent, session, or tool call created this cost?
• How many tokens did this workflow consume?
• Which model or provider drove the spend?
• Is an agent looping, retrying, or failing in a way that burns budget?
• Can we control spend before the provider bill arrives?
• Can engineering, product, and finance share the same AI usage trail?

Alephant solves these problems at the gateway layer.

What Alephant Does

Alephant provides a unified AI request layer for production AI systems.

Applications send model requests through Alephant instead of connecting directly to each provider. Alephant then applies routing, policy checks, budget controls, logging, cost tracking, and observability before and after the provider call.

Core capabilities include:

• Unified AI Gateway routing
• Support for 52+ providers and 320+ models
• OpenAI-style request and response interface
• Token and model spend tracking
• User, session, agent, and tool-call attribution
• Budget controls and usage limits
• Rate limits and model allowlists
• Retry, fallback, and cache-aware execution
• Agent loop and anomaly detection
• Dashboard visibility for spend, traces, latency, errors, and usage trends

How It Works

A typical request flow looks like this:

1. Your application or agent sends an AI request to Alephant Gateway.
2. Alephant authenticates the request and attaches workspace, user, session, agent, and route metadata.
3. Alephant checks budgets, rate limits, model policies, and safety rules.
4. Alephant routes the request to the selected model provider.
5. The provider response is normalized and returned to your application.
6. Alephant records token usage, model cost, latency, cache status, retries, errors, and trace metadata.
7. The dashboard shows spend, budget status, session traces, agent behavior, and anomalies.

Integration Model

Alephant is designed to be easy to adopt.

In most cases, teams can integrate by changing the model API base URL and using an Alephant API key.

$
OPENAI_BASE_URL="https://ai.alephant.io/v1"
$
OPENAI_API_KEY="your_alephant_virtual_key"

Existing OpenAI-style SDKs can continue to work while Alephant handles routing, observability, budget control, and provider adaptation behind the gateway.

Designed For Production AI Teams

Alephant is useful for:

• AI application teams that need cost visibility and provider flexibility
• Agent teams that need session tracing, loop detection, and budget guardrails
• Platform teams that need one control layer for AI traffic
• Finance and operations teams that need AI spend attribution and reporting
• Security teams that need policy checks, audit metadata, and controlled model access

Summary

Alephant helps teams move from black-box AI usage to governed AI operations.

Every request is routed through one gateway, attributed to the right user or agent, measured for tokens and cost, checked against budget and policy, and made visible in the dashboard.