Tuning Engines

Tuning Engines is a unified AI control and governance layer built by CerebrixOS for teams developing production intelligence across models, agents, tools, and fine-tuned systems. It functions as a universal intelligence runtime that allows organizations to secure, govern, and optimize every AI interaction through a single platform. The product consolidates the full AI lifecycle including inference, model routing, fallback policies, fine-tuning jobs, datasets, evaluations, model imports and exports, custom models, agents, MCP servers, reusable skills, guardrails, AGT YAML policies, data capture, runtime traces, usage analytics, API keys, billing, team roles, and integrations. Developers gain access to OpenAI-compatible APIs, Anthropic-compatible routes, CLI workflows, MCP access, coding-agent integrations, and resource catalogs for models, agents, tools, and skills. Teams can connect popular AI workflows like Claude Code, OpenCode, Aider, Cline, Roo, Continue.dev, Cursor, VS Code, and Windsurf through a single governed platform. Admins receive production-grade controls including role-based access, per-key budgets, rate limits, routing profiles, fallback rules, guardrails, policy-as-code, credential sources, auditability, usage traces, billing controls, tenant isolation, and team management. Tuning Engines is designed to help organizations move beyond isolated AI experiments into a secure, observable, cost-aware, and extensible AI operating layer where models can be trained, evaluated, routed, governed, and used by agents and tools at scale. Infrastructure costs are passed through at-cost with zero markup, meaning organizations only pay for support and platform upkeep.

Unified Inference

One OpenAI-compatible endpoint serves as the single access point for all model interactions. Developers can keep their existing SDK and simply swap one base URL to call any open, frontier, or tuned model. This endpoint supports 100+ models including open weight models like Llama 3.3 70B, DeepSeek V3, Qwen 2.5 72B, Mistral Small 3, and Gemma 2 27B, plus commercial frontier models and any custom fine-tuned variants. Centralized policy controls, full auditability, and token management are applied to every request automatically.

Model Tuning

Organizations can adapt open models to their specific data, workflows, and production goals without managing GPU infrastructure. The platform supports supervised fine-tuning and LoRA adapters, allowing teams to train models on proprietary datasets and custom tasks. Evaluation gates ensure quality moves with business requirements, and tuned models are immediately available through the same unified API endpoint as all other models.

Policy and Governance

A comprehensive policy-as-code framework enables centralized guardrails, access controls, and full request traceability across every model interaction. Admins can implement role-based access controls, per-key budgets, rate limits, routing profiles, and fallback rules. AGT YAML policies provide declarative policy management, while credential sources and tenant isolation ensure secure multi-team deployments.

Token Economics and Cost Management

Built-in cost controls include cost ceilings, quotas, routing policies, and fallback rules that keep spend and rate limits predictable. Infrastructure costs are passed through at-cost with zero markup, meaning organizations only pay for support and platform upkeep. Usage analytics and billing controls provide full visibility into token consumption across teams, projects, and API keys.

Code Assistance

Teams building IDE copilots, code generation tools, refactoring agents, and debugging assistants can leverage Tuning Engines as their backend runtime. The platform supports connections to popular coding agents like Cursor, VS Code, Windsurf, Continue.dev, Aider, Cline, and Roo through a single governed API. Developers get OpenAI-compatible endpoints with centralized policy enforcement, making it simple to integrate AI code assistance into existing development workflows while maintaining security and cost controls.

Conversational AI

Customer support bots, internal helpdesks, and multilingual chat applications benefit from the unified inference and model routing capabilities. Teams can deploy multiple models behind a single endpoint, implement fallback policies for reliability, and apply guardrails for content safety. The platform supports streaming responses and structured output, enabling real-time conversational experiences with full auditability and token tracking.

Agentic Systems

Multi-step reasoning, planning, and tool-using execution pipelines can be built and governed through Tuning Engines. The platform provides MCP server support, reusable skills, and agent resource catalogs that enable complex agent workflows. Teams can implement routing profiles and fallback rules to ensure agent reliability, while role-based access controls and per-key budgets maintain governance over autonomous operations.

Enterprise RAG

Secure, scalable retrieval augmented generation over knowledge bases and private documents is supported through the unified API. Organizations can combine embedding models with LLMs for semantic search and enterprise assistant use cases. The platform provides data capture, runtime traces, and usage analytics that enable teams to monitor and optimize RAG pipelines while maintaining tenant isolation and auditability for compliance requirements.

How does the unified API work with existing code?

Tuning Engines provides a drop-in OpenAI-compatible endpoint. You keep your existing OpenAI SDK and simply change the base URL to https://api.tuningengines.com/v1/ with your API key. No code rewrites or new client libraries are needed. The same endpoint works for open models, commercial frontier models, and your own fine-tuned variants, with centralized policy controls applied automatically to every request.

What models are available through the platform?

The model library includes popular open weight models like Llama 3.3 70B, Llama 3.1 8B, DeepSeek V3, DeepSeek R1, Qwen 2.5 72B, Qwen 2.5 Coder 32B, Mistral Small 3, Mixtral 8x7B, Gemma 2 27B, Llama 3.2 Vision, Whisper Large v3, and various embedding models. Commercial frontier models are also accessible, plus any models you fine-tune through the platform. All models are available through the same unified API endpoint.

How does pricing work for infrastructure costs?

Infrastructure costs are passed through at-cost with zero markup. Organizations only pay for support and platform upkeep. This means you pay exactly what the underlying GPU and compute resources cost, with no hidden margins. Token economics tools like cost ceilings, quotas, routing policies, and fallback rules help keep spend and rate limits predictable across teams and projects.

What governance controls are available for production deployments?

Admins get comprehensive controls including role-based access management, per-key budgets, rate limits, routing profiles, fallback rules, guardrails, policy-as-code through AGT YAML policies, credential sources, full auditability with request traces, usage analytics, billing controls, tenant isolation, and team management. These controls ensure secure, observable, and cost-aware AI operations at scale.