Tuning Engines

Tuning Engines is a unified AI control and governance layer designed for teams building production intelligence across models, agents, tools, and fine-tuned systems. Developed by CerebrixOS, this platform serves as a universal intelligence runtime that allows organizations to secure, govern, and optimize every AI interaction. It brings together the full AI lifecycle in one governed platform, 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 benefit from 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 gain production-ready 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. The platform is built 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. Notably, infrastructure costs are passed through at-cost with zero markup, meaning organizations only pay for support and platform upkeep.

Unified Inference

Access any model through a single OpenAI-compatible endpoint. Tuning Engines provides one unified API for open models, commercial frontier models, and your own tuned variants. You can keep your existing SDK and simply swap one base URL to call any model with centralized policy, full auditability, and token controls applied to every request. This eliminates the need for code rewrites or learning new clients, supporting over 100 models behind one interface with streaming and structured output capabilities.

Model Tuning

Adapt open models to your specific data, workflows, and production goals without managing GPU infrastructure. Tuning Engines enables supervised fine-tuning, LoRA adapters, and evaluation gates so model quality moves with your business requirements. The platform handles the infrastructure complexity, allowing you to focus on improving model performance for your unique use cases, whether that involves domain-specific language, task optimization, or custom behavior alignment.

Policy and Governance

Centralize guardrails, access controls, and full request traceability across every model interaction. Admins can implement role-based access, per-key budgets, rate limits, routing profiles, fallback rules, policy-as-code with AGT YAML, credential sources, and tenant isolation. Every request is auditable with complete usage traces, ensuring organizations maintain compliance and security while scaling their AI operations across teams and departments.

Token Economics

Manage cost ceilings, quotas, routing, and fallbacks so spend and rate limits stay predictable. Tuning Engines provides built-in token economics that give organizations complete visibility and control over their AI spending. With infrastructure costs passed through at-cost and zero markup, teams can scale their AI usage without worrying about unexpected expenses. Billing controls, usage analytics, and per-key budgets ensure financial predictability across all AI workloads.

Code Assistance

Build IDE copilots, code generation tools, refactoring agents, and debugging workflows using a governed AI platform. Teams can connect tools like Cursor, VS Code, Windsurf, and Continue.dev to access models through a single endpoint with centralized policy controls. This enables development teams to leverage AI code assistance while maintaining security, auditability, and cost controls across all developer interactions.

Conversational AI

Deploy customer support bots, internal helpdesks, and multilingual chat applications with production-grade reliability. Tuning Engines provides the routing, fallback policies, and guardrails needed to ensure conversational AI systems maintain consistent quality and safety. Organizations can fine-tune models on their specific domain data and deploy them through the same unified API, ensuring every customer interaction is governed by centralized policies.

Agentic Systems

Build multi-step reasoning, planning, and tool-using execution pipelines that operate reliably at scale. Tuning Engines supports agents, MCP servers, reusable skills, and AGT YAML policies to orchestrate complex agentic workflows. Teams can connect coding-agent integrations and manage the full lifecycle of agent interactions, with complete traceability and audit trails for every action taken by autonomous systems.

Enterprise RAG

Implement secure, scalable retrieval over knowledge bases and private documents with centralized governance. Tuning Engines enables organizations to deploy retrieval-augmented generation systems that access enterprise data while maintaining strict access controls and auditability. The platform supports embeddings, semantic search, and personalized recommendations, all governed by the same policy layer that controls every other AI interaction.

How does Tuning Engines handle model access and compatibility?

Tuning Engines provides a drop-in OpenAI-compatible endpoint that works with your existing SDK. Simply point your client at https://api.tuningengines.com/v1/ and use your API key to access over 100 models, including open models like Llama 3.3, DeepSeek V3, Qwen 2.5, and commercial frontier models, plus any custom models you fine-tune. No code rewrites or new clients are needed to switch between models.

What infrastructure costs and pricing model does Tuning Engines use?

Tuning Engines passes through infrastructure costs at-cost with zero markup. Organizations only pay for support and platform upkeep, making it a cost-effective solution for teams scaling their AI operations. The platform provides built-in token economics with cost ceilings, quotas, and rate limits to ensure spend remains predictable and controlled.

What governance and security controls are available for administrators?

Admins get comprehensive production controls including role-based access, per-key budgets, rate limits, routing profiles, fallback rules, guardrails, policy-as-code with AGT YAML, credential sources, auditability, usage traces, billing controls, tenant isolation, and team management. Every request is fully traceable with complete audit trails across all models and agents.

Can Tuning Engines integrate with existing AI development tools and workflows?

Yes, Tuning Engines connects seamlessly with popular AI development tools including Claude Code, OpenCode, Aider, Cline, Roo, Continue.dev, Cursor, VS Code, and Windsurf. It provides CLI workflows, MCP access, coding-agent integrations, and resource catalogs for models, agents, tools, and skills, enabling teams to maintain their existing workflows while gaining centralized governance.