Relevance AI - 2026 Pricing, Features, Reviews & Alternatives

Relevance AI is a cloud-based, low/no-code platform for building, deploying, and managing AI agents and coordinated multi-agent teams. Designed for both non-technical business users and engineers, the platform covers the full agent lifecycle: a plain-language agent builder (Invent) generates prompts, tools, and evaluations from a text description, while a visual workforce canvas lets teams compose multi-agent pipelines with drag-and-drop logic, approval steps, and defined handoffs. A pre-built agent marketplace provides cloneable agents and workforces for common use cases across sales, marketing, customer support, HR, operations, and research. Routing, queuing, evaluation, and tracing ship as one integrated system, removing the need to assemble separate tooling.

The platform equips agents with a broad tool layer: actions for sending emails, updating CRMs, searching the web, or calling any API; sandboxed Python and JavaScript execution within tool steps; and MCP server support for engineer-driven workflows via tools such as Claude Code, Codex, or Cursor. Knowledge connection syncs data from files, Google Drive, SharePoint, Notion, or websites, and hybrid RAG with reranking gives agents grounded, accurate responses. Long-term memory operates at both project and user level with compaction and observational memory. A built-in Evals framework runs automated quality testing, samples live runs, tracks pass rates, and flags drift. Model flexibility supports any major LLM provider with bring-your-own-model, fallback on failure, temperature controls, reasoning modes, and automated model swapping based on quality thresholds and cost.

Relevance AI targets enterprise and mid-market organizations, with customers including Canva, Autodesk, KPMG, and Lightspeed. Enterprise plans include data residency guarantees, and the platform is SOC 2 Type II certified with data encrypted in transit and at rest. Governance controls include audit logs, RBAC, SSO (SAML and OIDC), per-tool approval rules, and human-in-the-loop escalation. Agent scheduling, triggers, task queues, concurrency monitoring, and credit usage analytics round out operational management. An embedded deployment team assists with workflow mapping, initial agent builds, and internal training.