Ghostrun

Product Introduction

1. Ghostrun is a unified API platform that enables seamless integration with multiple AI providers through a single interface, eliminating the need for separate integrations. It standardizes API requests and responses across providers like OpenAI, Google, Groq, and Nebius while maintaining contextual continuity across model interactions.
2. The core value of Ghostrun lies in its ability to abstract complexity from multi-provider AI workflows, allowing developers to focus on application logic rather than credential management, payment systems, or vendor-specific API structures. It ensures cost transparency by passing provider pricing directly to users without markup.

Main Features

1. Ghostrun enables instant switching between AI providers and models by modifying the provider and model parameters in API requests, supporting major platforms like OpenAI’s GPT-4o, Groq’s Llama-3.3-70B, and Google’s Gemini-2.0 with identical request structures.
2. The platform maintains persistent context across threaded conversations, allowing users to reference prior interactions even when switching between models or providers within the same session. This is achieved through session tokens that track dialogue history and model-specific context windows.
3. Ghostrun integrates Retrieval-Augmented Generation (RAG) pipelines via a rag_pipeline_id parameter, enabling users to ground AI responses in proprietary data without custom code. Prebuilt RAG templates can be deployed through a dashboard, with support for vector database integrations and dynamic context injection.

Problems Solved

1. Ghostrun eliminates the operational overhead of managing multiple API keys, billing accounts, and vendor-specific integration patterns for teams using diverse AI models. It consolidates authentication, usage tracking, and cost allocation into a single system.
2. The product targets developers and enterprises building AI-native applications that require flexibility to switch between cost-optimized or performance-tuned models across providers. It is particularly relevant for SaaS platforms offering AI features and internal tooling teams managing multi-model architectures.
3. Typical use cases include applications requiring fallback mechanisms during provider outages, A/B testing of model outputs across vendors, and context-aware chatbots that combine general-purpose LLMs with domain-specific RAG pipelines.

Unique Advantages

1. Unlike vendor-specific SDKs or aggregation tools, Ghostrun provides deterministic response normalization across providers, ensuring consistent output formatting for parameters like temperature and max_tokens regardless of backend model.
2. The platform innovates with provider-agnostic thread persistence, enabling hybrid conversations that leverage multiple models (e.g., GPT-4 for reasoning followed by Llama-3 for cost-sensitive tasks) while retaining shared context.
3. Competitive advantages include real-time price-per-token tracking across all integrated providers, automated credential rotation for enterprise security compliance, and sub-100ms latency overhead compared to direct API calls.

Frequently Asked Questions (FAQ)

1. How does Ghostrun handle API rate limits across different providers? Ghostrun implements provider-specific rate limit monitoring and automatic request queuing, with configurable retry policies and fallback model routing to maintain service availability.
2. Can I use custom fine-tuned models through Ghostrun? Yes, the platform supports private model deployments via Bring Your Own Endpoint (BYOE) configurations, allowing custom models to be integrated into the same workflow as managed provider offerings.
3. How are costs calculated when using multiple providers in a single thread? Ghostrun attributes costs per API call to the respective provider, with itemized billing records that break down usage by model, token count, and provider-specific pricing tiers.
4. What security measures protect stored credentials? Ghostrun uses hardware security modules (HSMs) for credential encryption, OAuth2 tokenization for provider access, and zero-trust architecture principles for all API gateway operations.
5. How does RAG pipeline integration work with thread context? RAG documents are injected into the model context window after thread history, with configurable weighting to prioritize retrieved content over general conversation context.