Tuning Engines is a unified security and financial control layer for managed intelligence that governs every model, agent, and MCP call through policy-as-code, routing, tracing, and cost controls in real time.
What is Tuning Engines?
Tuning Engines is a production-ready AI governance platform that intercepts every API call to models, agents, tools, and fine-tuned systems. It takes requests via OpenAI-compatible or Anthropic-compatible endpoints and enforces policies, budgets, and approvals inline before any provider execution. The platform is built by Tuning Engines Inc. and runs as a cloud service with optional BYO cloud credentials (AWS, Azure, GCP, OpenAI, Anthropic direct) or its hosted marketplace of 40+ models.
Key Features
- Policy-as-code engine — Inline enforcement of allow/deny rules, prompt injection detection, PII redaction, and approval workflows using AGT YAML policies. Policies execute before model responses complete.
- Per-key budget and rate limits — Assign sk-te-* API keys per user or team with configurable spend caps, rate limits, and model access controls. Suspension happens before cost escalates.
- Smart routing and failover — Route requests across multiple providers (AWS Bedrock, Azure OpenAI, GCP Vertex AI, Cloudflare AI GW, OpenAI direct, Anthropic direct) with automatic fallback, A/B model comparison, and latency/quality benchmarking.
- Managed LoRA fine-tuning — Capture agentic sessions (tool calls, decisions, traces) as training data, apply PII redaction, and run managed LoRA fine-tuning pipelines. Evaluate new weights against frontier baselines with win-rate, latency, and cost metrics.
- Agentic audit trail — Every agent call, policy decision, approval, and outcome logged immutably. Export evidence packs formatted for EU AI Act, NIST AI RMF, and SOC 2 AI Controls.
- Zero-code agent integrations — Connect Claude Code, OpenCode, Aider, Cline, Roo, Continue.dev, Cursor, VS Code, Windsurf, and other AI workflows through a single governed endpoint without modifying agent code.
- Multi-tenant team management — Role-based access, tenant isolation, billing controls, and usage analytics that roll up from individual developer sessions to department-level spend to org-wide cost-per-outcome views.
Who is it for?
- CFOs and finance teams — Measure AI ROI by mapping token usage to actual business outcomes (e.g., contract reviews leading to closed deals), enforce budgets per team, and get savings recommendations.
- CIOs and IT leaders — Standardize AI operations across all models and providers with a single control plane, consistent policies, and full audit trails for compliance.
- Security teams — Enforce runtime guardrails, block prompt injections, require human approval for sensitive agent actions, and generate audit-ready compliance evidence packs.
- AI platform teams — Deliver a unified API across 100+ models with built-in routing, caching, fine-tuning, and governance — without bolting on separate tools later.
What can you do with Tuning Engines?
- Govern multi-provider AI spend: Route app and agent requests across AWS Bedrock, Azure OpenAI, GCP Vertex AI, OpenAI direct, and Anthropic direct while enforcing per-team budgets and model access policies from one place.
- Fine-tune from production traffic: Automatically surface agentic session traces as training candidates, run managed LoRA fine-tuning, and evaluate new model weights against frontier baselines — all within the platform.
- Prove compliance readiness: Export evidence packs for EU AI Act, NIST AI RMF, and SOC 2 AI Controls — with policy decisions, approval trails, and audit logs structured for immediate review.
