Lower AI costs without losing productivity

Organizations can reduce model lock-in by using a common access and governance layer across multiple model providers. Unity Gateway helps organizations control and optimize AI costs across users, teams, applications, agents and model providers. Teams can attribute token usage, set budgets and spend limits, apply rate limits and identify inefficient AI activity. Smart Routing can automatically select the best model and agent harness for each task based on cost and quality, helping organizations reduce AI spend without sacrificing performance.

Organizations can reduce model lock-in by using a common access and governance layer across multiple model providers. Unity Gateway provides a single governed access layer for leading proprietary and open models, including OpenAI, Anthropic Claude, Google Gemini, Meta Llama, Qwen, Kimi and GLM. Teams can use the best model for each workload without rebuilding applications or governance for every provider. Organizations get reliable model capacity and fast access to new models while centrally managing permissions, policies, costs and observability across providers.

Governing AI agents at scale requires centralized controls across the models, tools and enterprise systems agents can access. Unity Gateway extends governance beyond model requests to the coding agents, harnesses, MCP servers, tools and skills that make up enterprise AI systems. Organizations can centrally govern coding agents and harnesses such as Claude Code, OpenAI Codex, Cursor and Gemini CLI, control model and tool access, and attribute usage and costs. With Smart Routing in the Omnigent meta-harness, Unity Gateway can automatically select the best model and coding harness for each task based on cost and quality, while centralized policies, tracing and audit logs provide control and visibility across AI workflows.

AI agent observability requires visibility across model interactions, agent behavior, tool calls, performance and costs. Unity Gateway provides centralized observability across models, agents, MCP servers and tools. Organizations can securely capture AI traces and analyze prompts, responses, token usage, latency, identity and tool activity. Lakewatch can monitor AI systems and surface operational issues, while Genie lets teams investigate trace and usage data using natural language to identify failures, inefficient behavior and cost optimization opportunities.

Unity Gateway goes beyond model API management to govern the broader AI estate, including proprietary and open models, agents, MCP servers, tools and skills. Organizations can use one governance foundation from data access through runtime AI interactions rather than stitching together separate gateways, security tools and observability systems.

Unity Gateway uses usage-based pricing, so organizations only pay for the products they use, billed at per-second granularity. This keeps spend predictable and tied to actual usage rather than requiring large upfront commitments. Cost controls such as budgets, spend limits and rate limits give teams additional ways to keep usage-based spend in check across users, teams and agents. For detailed rates, see the Databricks pricing page.

You can start by reviewing the Unity Gateway documentation or watching a product demo to see how model access, routing, cost controls and observability work together. Developers can connect existing coding agents, such as Claude Code, Codex, Gemini CLI, OpenCode, GitHub Copilot and Pi, through the Unity Gateway CLI without changing their workflows. Platform teams can then centrally configure model access, routing, budgets and policies as adoption scales. Because pricing is usage-based, teams can begin with real workloads and expand coverage over time.