Span is an engineering intelligence platform for software organizations that need to measure delivery performance, developer experience, and the impact of AI coding tools. It connects engineering work across source control, tickets, pull requests, projects, and coding-agent sessions, then relates that activity to engineering outcomes, team priorities, and spend.

Span’s trace capabilities capture prompts, model choices, tool calls, file changes, token consumption, session duration, and resulting pull requests from supported AI coding workflows. Related sessions can be connected across planning, implementation, testing, debugging, and review work, including work that does not result in a merged pull request. This gives engineering and AI platform leaders a record of how agent-assisted work moved from a prompt to production.

The platform provides reporting for AI effectiveness, engineering allocation, development workflow performance, AI spend, and return on investment. Teams can attribute token costs and engineering time to projects, work types, and business initiatives; track budgets against actual use; and create reports for software capitalization and R&D tax-credit documentation. Span also offers benchmarks, developer-experience surveys, and AI-generated analysis to help teams identify workflow or agent-harness improvements.

Span’s Src AI agent provides conversational access to engineering context for questions, investigations, scheduled updates, and executive reports. It can be used in Slack and through skills for supported AI tools. The platform also supports API, CLI, MCP, and Slack workflows. Security capabilities include SOC 2 Type II controls, SSO, SCIM, role-based access controls, audit logs, data redaction, and configurable access to trace content.

Span uses custom pricing based on code contributors. Dashboard-only users who do not contribute code or appear in connected engineering tools are not counted as paid contributors.

Span

What is Span?

Categories & Use Cases