Ghostrun

Integrating different AI models into your applications often means wrestling with multiple APIs, managing separate credentials, and handling varied billing systems. Ghostrun streamlines this entire process, offering a unified AI Inference Operating System that lets you access leading models from providers like OpenAI, Groq, Google Gemini, Nebius, and more through a single, consistent API interface. Focus on building innovative features, not on managing complex integrations.

Key Features Tailored for Your Workflow

• 🔄 Switch Providers Seamlessly: Change the underlying AI provider (e.g., from OpenAI to Groq) by modifying just a single provider parameter in your API call. This allows for easy A/B testing, cost optimization, or fallback strategies without code refactoring.

• 🔗 Maintain Context with Automatic Threading: Build stateful, multi-turn conversational applications effortlessly. Ghostrun automatically manages conversation history within threads, preserving context even when you switch between different models or providers during a conversation. Every request returns a thread_id for easy continuation.

• 🔑 Eliminate API Key Management: Authenticate once with your Ghostrun API key. Ghostrun securely manages and rotates the necessary credentials for all underlying providers (OpenAI, Groq, etc.), freeing you from the burden of managing multiple keys and vendor accounts.

• 💰 Simplify Billing & Track Costs: Receive one consolidated bill for all your AI model usage. Ghostrun tracks usage costs per provider and model transparently and passes them directly to you with no markup, simplifying budget management.

• 🧠 Integrate Powerful RAG Pipelines: Enhance AI responses by grounding them in your own data. Create Retrieval-Augmented Generation (RAG) pipelines via the dashboard and activate them with a simple rag_pipeline_id parameter in your API calls. This reduces hallucinations and provides contextually relevant answers based on your proprietary information.

• ⚙️ Receive Standardized Responses: Get consistent JSON response structures regardless of the underlying provider, simplifying data parsing and integration logic in your application. Key details like content, usage, latency, and thread_id are always present.

• ⏱️ Minimal Performance Overhead: Ghostrun adds minimal latency (typically 30-60ms) to your requests. The overall response time remains primarily dependent on the selected provider and model performance.

Practical Use Cases for Developers

1. Optimizing for Speed and Cost: You're building a feature that needs rapid responses for some user interactions but higher quality for others. With Ghostrun, you can dynamically route requests to Groq's Llama models for speed-critical tasks and OpenAI's GPT-4o for complex generation within the same application, using the same API integration and simply changing the provider and model parameters.

2. Building Advanced Conversational Agents: You need to create a customer support chatbot that remembers the entire conversation history accurately. Ghostrun's automatic threading handles context management seamlessly. You can even switch models mid-conversation (e.g., start with a faster model, escalate to a more powerful one for complex queries) using the thread_id, ensuring a smooth user experience without losing context.

3. Developing Custom Knowledge Assistants: Your team needs an internal tool to answer questions based on your company's extensive documentation library. You can upload your documents to create a RAG pipeline in Ghostrun. Then, by adding the rag_pipeline_id to your /generate requests, your internal assistant can provide accurate answers grounded in your specific knowledge base, directly accessible via the API.

Conclusion

Ghostrun acts as your central nervous system for AI model interaction. By unifying access, simplifying management, and providing powerful features like threading and RAG through a single API, it removes significant friction from the development process. This allows you to experiment freely, optimize performance and cost, and ultimately build more sophisticated AI-powered applications faster. Spend your time innovating on your core product, letting Ghostrun handle the complexities of the diverse AI landscape.

Frequently Asked Questions (FAQ)

1. Which AI providers does Ghostrun currently support? Ghostrun provides unified access to models from OpenAI, Groq, Google Gemini, Nebius, Grok (X.ai), Mistral AI, Together.ai, Cohere, and Lambda Labs. You can retrieve a full list of available models per provider using the /api/v1/models endpoint.

2. How does Ghostrun handle pricing and billing? Ghostrun operates on a pass-through pricing model. We track the exact token usage costs from the underlying AI provider (e.g., OpenAI, Groq) for each request and bill you that amount with no additional markup or hidden fees. You receive a single, itemized invoice covering usage across all providers accessed via Ghostrun.

3. What is the typical latency added by Ghostrun? Our internal testing shows that Ghostrun typically adds only 30-60 milliseconds of overhead per API request. This includes routing, authentication, standardization, and logging. If using RAG, expect an additional 200-400ms for the retrieval step. The primary factor determining total latency remains the performance of the chosen AI provider and model.