Tracecat vs Tines: Which is best for you?

Tracecat includes a dedicated AI agent builder, skills registry, subagents, and sandboxed execution. Tracecat agents are designed to operate as long-running AI colleagues that can investigate, reason, and take action across tools.

Tines supports AI agents inside workflows, but its agent model is centered around workflow steps. That works well when an agent is one part of a larger deterministic process. Tracecat is better suited when agents are the primary interface for investigation, enrichment, decision-making, and response.

Tracecat treats coding assistants as a first-class way to build and automate security work. Its MCP server is purpose-built for Claude Code, Microsoft Copilot, and other coding assistants, with access across agents, workflows, cases, tables, Python integrations, and version control.

Tines started as a visual workflow builder. MCP support is improving, but it is still being added onto a product originally built for click-and-drag automation.

Tracecat includes 500+ integrations and 50+ security-specific MCP servers that agents can use directly or inside workflows. These MCP servers can be remote or stdio-based, which gives teams more flexibility when connecting local tools, internal systems, and security engineering workflows.

Tines supports connecting AI agent actions to remote MCP servers, which is useful for workflow augmentation. Tracecat goes further by treating MCP servers as a native tool layer for both standalone agents and orchestrated workflows.

Tracecat lets teams bring their own Python functions and template integrations from git with a single click. Once registered, those functions can be reused globally across workflows and agents. This makes integrations easier to maintain, easier to review, and easier to improve over time.

Tines supports Python through workflow actions and custom runtimes. That is useful when a workflow needs script execution, but Tracecat is designed around reusable engineering assets: shared functions, git-backed registries, and code that can be used across the platform.

Tracecat syncs workflows, agents, and table schemas to your existing version control system, including GitHub, GitLab, and Bitbucket. This lets security teams use the same review, rollback, branching, and change-management practices they already use for infrastructure and application code.

Tines has its own versioning experience for workflows inside the product. That is helpful for visual workflow management, but it does not replace external version control for teams that want security automation to live alongside the rest of their engineering assets.

Tracecat gives teams unlimited workflows, so they can build smaller, modular automations that are easier to maintain. Instead of forcing teams to pack more logic into fewer workflows, Tracecat encourages workflow-as-function design: smaller units, clearer ownership, and better reuse.

Tines pricing and packaging is more modular. Depending on the plan, teams may need to account for flows, events, cases, AI usage, API access, version control, or other packaged capabilities. Tracecat is designed around all-in-one pricing for teams that want to build broadly without worrying that every new automation creates another pricing decision.

Tracecat is open source, so users can inspect the code, review integrations, contribute improvements, and avoid black-box automation. It also works better with coding assistants because they can reason over the real platform code.

Tines is closed source and proprietary, so customers cannot inspect or modify the underlying platform code.

Tracecat gives every customer direct access to a forward-deployed AI engineer. Engineering and customer support is founder led so all customer feedback directly impacts the product roadmap.

Tines uses a traditional sales-led support model with an account executive. Services and support are charged as add-on credits.