How It Works
GPT-Load is a self-hosted AI gateway designed to simplify the management of multi-channel, multi-credential setups for AI model access. It consolidates API keys, subscription accounts, and traffic scheduling into a single entry point, reducing complexity for developers and organizations. The gateway operates by pooling credentials, load balancing requests across providers, and exposing a management console for configuration and monitoring. This approach ensures that applications interact with a unified base URL and AccessKey, abstracting the underlying complexity of managing multiple providers and credentials.
The system is structured around two core layers: Groups and AccessKeys. Groups face upstream AI providers, managing channels, models, and credential health, while AccessKeys face client applications, defining permissions and usage limits. This separation allows for granular control over routing, retries, and cost management without requiring applications to handle these tasks individually.
GPT-Load supports a wide range of client protocols, including OpenAI Chat Completions, Anthropic Messages, Gemini, and custom OpenAI-compatible endpoints. Each channel—representing an upstream provider or service—declares the protocols it supports, and the gateway routes requests accordingly. For example, OpenAI Embeddings and Gemini Embeddings use distinct protocols, with the gateway converting between them where necessary. This ensures compatibility while maintaining the native format of each provider’s responses.
The management UI, embedded within the Go binary, provides real-time visibility into health, logs, usage statistics, and cost estimates. This transparency helps operators optimize performance, monitor spending, and troubleshoot issues efficiently.
Deployment is straightforward, with support for Docker Compose, native binaries, and source builds. The service defaults to listening on the loopback interface (127.0.0.1) for security, requiring explicit configuration for remote access. However, it is not backward-compatible with version 1.x, requiring a fresh deployment with a new database and encryption key.
Use Cases
Its ability to pool credentials and load balance requests makes it ideal for developers building AI-driven applications that need to integrate with diverse model services, such as OpenAI, Anthropic, Gemini, and specialized providers like DeepSeek or Moonshot AI.
For enterprises managing subscription-based accounts (e.g., Codex, Claude, or Grok), GPT-Load streamlines authentication and authorization through OAuth flows and shared credential pools. The gateway’s support for weighted rotation and session affinity ensures that traffic is distributed optimally, even when some credentials are temporarily unavailable or throttled.
Another key use case is cost optimization. By consolidating API usage under a single management console, organizations can track spending per model, credential, or group. The gateway provides cost estimates and usage trends, enabling data-driven decisions about resource allocation. For example, developers can prioritize high-performance models for critical tasks while using lower-cost alternatives for less demanding workloads, all without changing application code.
GPT-Load also addresses security and compliance requirements by encrypting credentials locally and restricting access to sensitive keys. The embedded management UI eliminates the need for external databases or separate frontend deployments, simplifying infrastructure while maintaining control.
In scenarios requiring high availability, the gateway’s built-in retry and cooldown mechanisms minimize downtime caused by provider outages or rate limits.
Unified API Entry Point
GPT-Load replaces the need for multiple API endpoints and credentials with a single base URL and AccessKey. Applications interact with the gateway using familiar protocols (e.g., OpenAI, Anthropic, or Gemini), while the gateway handles the underlying complexity of routing, authentication, and failover. This abstraction reduces integration effort and simplifies client-side logic, as developers only need to configure a base URL and key to access a wide range of models.
Multi-Channel and Multi-Credential Support
The gateway supports 32 built-in channels covering official APIs (OpenAI, Anthropic, Gemini), cloud platforms (Azure OpenAI, AWS Bedrock), and specialized model services (DeepSeek, Moonshot AI, Zhipu AI). Each channel can manage multiple credentials, including API keys and subscription accounts, with shared scheduling and health monitoring.
Advanced Traffic Management
GPT-Load includes sophisticated traffic management features such as weighted rotation, session affinity, and automatic retries. For example, if a credential hits a rate limit, the gateway can temporarily blacklist it and reroute traffic to alternative providers, maintaining service continuity.
Protocol Conversion and Compatibility
The gateway supports native protocols for OpenAI Chat Completions, Anthropic Messages, Gemini, and embeddings, as well as custom OpenAI-compatible endpoints. This ensures compatibility across providers without requiring clients to adapt their code.
Embedded Management Console
The management UI, included in the Go binary, provides a centralized interface for configuring channels, monitoring health, and reviewing usage statistics. Operators can inspect credential health, request logs, and cost estimates in real time, enabling proactive management of resources. The console supports SQLite, MySQL, and PostgreSQL backends, with credentials encrypted locally to prevent exposure.
Security and Compliance
Credentials are encrypted using a locally stored key, and sensitive data is redacted from logs to prevent leakage. The service defaults to loopback-only access, requiring explicit configuration for remote use, and includes features like request redaction and guardrails for content review.
Cost Transparency and Optimization
The gateway provides detailed cost estimates and usage trends, allowing organizations to monitor spending per model or group. For example, it can track token consumption, cache hit rates, and provider-specific pricing to identify cost-saving opportunities.