Tuning Engines

Tuning Engines is a unified AI control and governance layer built for teams that are moving beyond isolated experiments and into production-grade intelligence. It serves as a single, governed platform that brings together 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, policy-as-code, data capture, runtime traces, usage analytics, API keys, billing, team roles, and integrations. The product is designed for developers who need OpenAI-compatible APIs and Anthropic-compatible routes, CLI workflows, MCP access, and coding-agent integrations. It also serves admins who require role-based access, per-key budgets, rate limits, routing profiles, fallback rules, guardrails, credential sources, auditability, usage traces, billing controls, tenant isolation, and team management. Tuning Engines helps organizations securely govern, optimize, and scale every AI interaction through one endpoint. It provides centralized policy control, full auditability, and token economics managed by design. The platform is backed by Google Cloud for Startups, NVIDIA Inception, Rogers Cybercatalyst, ElevenLabs Grants, AWS Activate, and BDC Capital, making it a trusted choice for startups and enterprises scaling their AI operations.

Unified Inference

Access any open, commercial, or custom tuned model through a single OpenAI-compatible endpoint. Developers can keep their existing SDK and simply swap one base URL to call over 100 models including Llama, DeepSeek, Qwen, Mistral, Gemma, and more. Centralized policy, guardrails, and token controls apply to every request automatically.

Model Tuning and Lifecycle Management

Adapt open models to your specific data, workflows, and production goals using supervised fine-tuning and LoRA adapters. The platform handles the full model lifecycle from building and tuning to hosting and scaling without requiring you to manage GPU infrastructure. Evaluation gates ensure quality moves with your business.

Policy-as-Code and Guardrails

Implement centralized access controls, routing profiles, fallback rules, and guardrails using AGT YAML policies. Every request is governed by configurable policies that enforce cost ceilings, quotas, rate limits, and tenant isolation. Full request traceability and auditability are built into every interaction.

Token Economics and Cost Controls

Manage token spend with predictable cost ceilings, per-key budgets, and rate limits. Infrastructure costs are passed through at-cost with zero markup, meaning you only pay for platform support and upkeep. This design ensures that as you scale, your token economics remain under control and transparent.

Code Assistance and IDE Copilots

Build and deploy code generation, refactoring, and debugging agents that integrate with tools like Claude Code, OpenCode, Aider, Cline, Roo, Continue.dev, Cursor, VS Code, and Windsurf. Tuning Engines provides a governed platform where these AI workflows connect through a single API with centralized policy and auditability.

Conversational AI and Customer Support

Deploy customer support bots, internal helpdesks, and multilingual chat applications that leverage any open or commercial model. The unified inference endpoint allows you to route between models based on cost, quality, or availability, while guardrails and fallback policies ensure reliable and safe interactions.

Agentic Systems and Multi-Step Reasoning

Build multi-step reasoning, planning, and tool-using execution pipelines that require coordination between models, agents, and external tools. Tuning Engines supports MCP servers, reusable skills, and agent management so your agentic systems can scale with governance and observability.

Enterprise RAG and Semantic Search

Implement secure, scalable retrieval over knowledge bases and private documents using embeddings and retrieval-augmented generation. The platform supports embedding models from the BGE and E5 families alongside LLMs, enabling enterprise assistants and personalized recommendations with full audit trails.

How does Tuning Engines compare to using OpenAI or Anthropic directly?

Tuning Engines provides a unified OpenAI-compatible endpoint that gives you access to over 100 models including open, commercial, and your own tuned variants. Unlike direct API usage, you get centralized policy control, guardrails, token economics, fallback routing, and full auditability across all models. Infrastructure costs are passed through at-cost with zero markup, so you only pay for platform support and upkeep.

Can I bring my own fine-tuned models to Tuning Engines?

Yes, you can import your own models or fine-tune models directly on the platform using supervised fine-tuning and LoRA adapters. Once tuned, your custom models are accessible through the same OpenAI-compatible endpoint as all other models, with the same policy controls, guardrails, and auditability applied automatically.

What coding agents and IDEs are supported?

Tuning Engines integrates with Claude Code, OpenCode, Aider, Cline, Roo, Continue.dev, Cursor, VS Code, Windsurf, and other AI workflows. Developers can connect these tools through the single governed platform, ensuring that every AI interaction in the development pipeline is secure, observable, and cost-aware.

How does pricing work for infrastructure and platform usage?

Infrastructure costs for model inference and compute are passed through at-cost with zero markup. You only pay Tuning Engines for platform support and upkeep. The platform provides per-key budgets, rate limits, cost ceilings, and usage analytics so you can manage spend predictably as you scale from prototypes to production workloads.