Deploy and scale
Secure, governed LLM deployment
Host agents and LLMs securely in your private infrastructure with fine-grained role-based access control (RBAC) . Efficient model mounting enables fast, real-time inference at scale.

Domino brings secure model hosting, deep observability, structured evaluations, and production-grade monitoring in one integrated platform so teams can buildfaster, deploy confidently, and continuously improve Agentic AI quality.
Deploy LLMs from your own infrastructure with high-performance inference, observability, and built-in governance.
Trace, compare, and evaluate every agent interaction with the context teams need — metadata, metrics, and complete configuration lineage.
Real-time production traces, user feedback, scheduled evaluations, and performance dashboards ensure ongoing system quality.
Secure, governed LLM deployment
Host agents and LLMs securely in your private infrastructure with fine-grained role-based access control (RBAC) . Efficient model mounting enables fast, real-time inference at scale.
Full observability and trace-level debugging
With a single line of instrumentation, teams capture tokens, latencies, inputs, outputs, and downstream calls across multi-agent systems built on any framework. The Experiment Manager captures prompts, parameters, models, datasets, and code automatically.
Structured, reproducible evaluation
Evaluate agentic AI systems using heuristic metrics or LLM-as-judge functions and compare runs side by side with trace-level detail. Restore any run into an interactive workspace to continue iteration with guaranteed reproducibility.
LLM endpoints are deployed securely in your VPC or on-premises, with full performance observability, and an efficient hosting design that lowers cost while keeping model calls fast and reliable.
Domino connects experimentation, iteration, and production in a single system of record. Teams work from a shared source of truth instead of juggling fragmented tools across the agent development lifecycle.
Continuous production monitoring and improvement
Production traces are logged automatically using the same SDK as development, while scheduled evaluations generate continuous quality metrics. Dashboards help teams spot regressions early before they impact customers or the business.
Native support for leading agentic frameworks captures traces, metrics, and configurations automatically. Teams gain visibility without rewriting application code or disrupting existing workflows.
Every prompt, model, dataset, and code change is captured by default, with workspace restore ensuring teams can reliably reproduce results and explain decisions at any point in time.
Production traces and continuous evaluations provide ongoing visibility into agentic system performance, even as models change and usage grows over time.