Tuning Engines

Tuning Engines is a unified AI control and governance platform designed for teams that are building production intelligence across models, agents, tools, and fine-tuned systems. Think of it as a single operating layer for all your AI work, from the first experiment to a fully deployed, mission-critical application. It brings together the entire 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, policy files, data capture, runtime traces, usage analytics, API keys, billing, team roles, and integrations. For developers, Tuning Engines provides familiar OpenAI-compatible APIs, Anthropic-compatible routes, CLI workflows, and coding-agent integrations. You can connect tools like Claude Code, OpenCode, Aider, Cline, Roo, Continue.dev, Cursor, VS Code, and Windsurf through a single governed platform. For administrators, Tuning Engines delivers the controls needed for production, 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. A standout feature is that infrastructure costs are passed through at-cost with zero markup, so you only pay for support and platform upkeep.

Unified Inference API

Access any model through one single OpenAI-compatible endpoint. Whether you are using open models, commercial frontier models, or your own custom-tuned variants, you can keep your existing SDK and simply swap one base URL. This means no code rewrites, no new client libraries to learn, and no managing multiple API keys. Every request benefits from centralized policy control, full auditability, and token controls applied automatically.

Model Tuning and Lifecycle Management

Adapt open models to your specific data, language, and workflows with built-in fine-tuning capabilities. You can run supervised fine-tuning and LoRA adapters directly on the platform without managing any GPU infrastructure. The platform also includes evaluation gates so you can measure quality, compare variants, and ship with evidence. Your tuned models are hosted on the same endpoint, making them instantly accessible to your applications.

Policy, Guardrails, and Governance

Admins get comprehensive controls for production AI deployments. You can set role-based access, per-key budgets, rate limits, routing profiles, and fallback rules. The platform supports policy-as-code with YAML files, credential sources, and full auditability with runtime traces. These guardrails ensure that every AI interaction is secure, observable, and compliant with your organization's standards, whether you are running a small team or a large enterprise.

Token Economics and Cost Management

Tuning Engines gives you complete visibility and control over your AI spending. You can set cost ceilings, quotas, routing profiles, and fallback policies so that spend and rate limits stay predictable. The platform provides detailed usage analytics and billing controls. Infrastructure costs are passed through at-cost with zero markup, meaning you only pay for the compute power used and the platform support. This transparent pricing model helps you scale AI without surprise bills.

Code Assistance and IDE Copilots

Build powerful code generation, refactoring, and debugging agents that integrate directly into development environments. With Tuning Engines, you can connect tools like Cursor, VS Code, Windsurf, and Continue.dev through a single governed API. Developers get the speed of local coding agents with the security and policy controls of a centralized platform. Fine-tune models on your codebase for better suggestions and context-aware completions.

Conversational AI and Customer Support

Deploy customer support bots, internal helpdesks, and multilingual chat applications with confidence. Use the unified API to route between different models based on cost, latency, or quality requirements. Apply guardrails to ensure responses are safe and appropriate. With full traceability, you can audit every conversation and continuously improve your models by capturing real-world data for fine-tuning.

Agentic Systems and Multi-Step Reasoning

Build agents that can plan, reason, and use tools to complete complex tasks. Tuning Engines supports MCP servers, reusable skills, and agent workflows. You can define fallback policies so that if one model fails, another takes over automatically. The platform provides runtime traces so you can debug and optimize your agent's behavior. This is ideal for automation, data processing, and decision-support systems.

Enterprise RAG and Search

Create secure, scalable retrieval-augmented generation systems over your private knowledge bases and documents. Use the platform to manage embeddings, connect to your data sources, and apply fine-tuned models for better retrieval accuracy. With centralized policy controls, you can ensure that only authorized users access sensitive information. The unified API makes it easy to integrate RAG into existing applications without complex infrastructure.

How do I get started with Tuning Engines?

Getting started is simple. You can sign up for a beta account and receive an API key. The platform is compatible with the OpenAI SDK, so you just need to change the base URL to "https://api.tuningengines.com/v1/" and start making requests. There is a sandbox environment available for testing. You can also connect popular coding agents like Claude Code, Aider, or Cline with minimal configuration. The team offers onboarding support to help you set up your first workflows.

What models are available on the platform?

Tuning Engines provides instant access to a wide library of leading open-weight models, including Llama 3.3 70B, Llama 3.1 8B, DeepSeek V3, DeepSeek R1, Qwen 2.5 72B, Mistral Small 3, Mixtral 8x7B, Gemma 2 27B, and many more. You also get access to commercial frontier models and any models you fine-tune yourself. All models are accessible through the same OpenAI-compatible endpoint, making it easy to switch between them without changing your code.

How does pricing work?

Tuning Engines uses a transparent pricing model where infrastructure costs are passed through at-cost with zero markup. You only pay for the actual compute power used by your requests and a platform support fee. This means you avoid the typical cloud AI markup. The platform provides detailed usage analytics and billing controls so you can set budgets, quotas, and rate limits. There are no hidden fees or surprise charges.

Can I use my own fine-tuned models?

Yes, absolutely. Tuning Engines supports the full model lifecycle, including fine-tuning. You can upload your own datasets, run supervised fine-tuning and LoRA adapters, and evaluate your models. Once tuned, your custom models are hosted on the same unified endpoint and are accessible with the same API. You can also import and export models as needed, giving you full control over your AI assets.