Preloop vs Trigger.dev: Which is Better in 2026?
A comprehensive comparison of Preloop and Trigger.dev covering features, pricing, use cases, and which tool is the right choice for your needs.
⚡ Quick Verdict
Choose Preloop if:
- →You need mcp firewall with allow, deny and require-approval rules on tool access or ai model gateway with per-agent budgets and cost attribution
Choose Trigger.dev if:
- →You want more affordable paid plans (from $10/mo)
- →You need long-running typescript tasks with durable retries and queues or concurrency controls and elastic scaling
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Preloop vs Trigger.dev: At a Glance
Pricing Comparison: Preloop vs Trigger.dev
Understanding the pricing differences between Preloop and Trigger.dev is crucial for making the right choice. Here's how their plans compare side by side.
Preloop Pricing
Trigger.dev Pricing
💡 Pricing takeaway: Both Preloop and Trigger.dev offer free tiers, making it easy to try before you buy. Compare the specific plans to find the best value for your use case.
Feature-by-Feature Comparison
Here's how every feature from Preloop and Trigger.dev stacks up.
What Makes Each Tool Unique
🔵 Unique to Preloop
Features available in Preloop but not in Trigger.dev:
- ✓MCP firewall with allow, deny and require-approval rules on tool access
- ✓AI model gateway with per-agent budgets and cost attribution
- ✓Policy-as-code in YAML with CEL expressions
- ✓Human approvals on mobile, watch, Slack, Mattermost or webhook
- ✓One-command discovery and transparent rewrite of existing agent configs
- ✓Runtime session observability and an audit trail for AI Act evidence
🟣 Unique to Trigger.dev
Features available in Trigger.dev but not in Preloop:
- ✓Long-running TypeScript tasks with durable retries and queues
- ✓Concurrency controls and elastic scaling
- ✓Durable cron scheduling without timeouts
- ✓Realtime streaming from tasks to a frontend
- ✓Official MCP server with agent chat and prompt management
- ✓Open source and self-hostable
Use Case Recommendations
Best for: Preloop
Preloop is an Apache-2.0 licensed control plane for AI agents, aimed at the governance problem that appears the moment more than one agent is running in an organisation: nobody can see what tools they can reach, what they are spending, or who approved anything. It bundles six capabilities into one self-hostable platform — an MCP firewall that defines allow, deny and require-approval rules on tool access; an AI model gateway that attributes cost and enforces per-agent budgets; policy-as-code written in YAML with CEL expressions; human-in-the-loop approvals delivered to mobile, watch, Slack, Mattermost or a webhook; runtime session observability; and an audit trail. Onboarding is deliberately frictionless: `preloop agents discover` finds compatible agent configurations already on the machine and transparently rewrites them so tool calls route through the MCP firewall and model traffic through the gateway, with no SDK changes and no agent code changes. The named runtimes it rewrites include Claude Code, Codex CLI, Cursor, Gemini CLI, Hermes, OpenClaw, OpenCode and Windsurf. The vendor positions Preloop explicitly as an open-source alternative to AWS Bedrock AgentCore and as a way to build EU AI Act readiness evidence, with dedicated guidance published for that use case. Because the full control plane is Apache 2.0 and self-hostable, the paid tiers sell hosting, team governance and conditional-approval sophistication rather than the core capability.
Ideal use cases:
- •Teams or individuals who need mcp firewall with allow, deny and require-approval rules on tool access
- •Teams or individuals who need ai model gateway with per-agent budgets and cost attribution
- •Teams or individuals who need policy-as-code in yaml with cel expressions
- •Teams or individuals who need human approvals on mobile, watch, slack, mattermost or webhook
- •Anyone focused on mcp workflows
- •Anyone focused on agent-governance workflows
Best for: Trigger.dev
Trigger.dev is an open-source platform for writing long-running AI agents and background workflows in TypeScript and running them as fully managed infrastructure. The problem it exists to solve is the one every serverless deployment hits the moment an agent gets interesting: a task that needs to run for ten minutes, retry a flaky model call, fan out to a hundred sub-tasks and survive a redeploy does not fit a request-response timeout. Tasks are ordinary TypeScript functions with durable execution around them — retries, queues, concurrency limits and elastic scaling are configuration, not code you write. Scheduled tasks give durable cron without timeouts. Realtime connects a frontend directly to a running task so a UI can stream progress rather than poll. Observability and tracing are first-class, with custom dashboards, log retention and alert destinations tiered by plan. The team ships an official MCP server, which as of the current changelog carries agent chat, prompt management and reports, so the platform is drivable from a coding agent as well as from code. It is genuinely open source and self-hostable, with a documented self-hosting guide, and the cloud plans are a convenience layer over that rather than the only way to run it. Cal.com's booking engine is the reference deployment. The pricing model is credits plus concurrency, which maps cleanly onto how agent workloads actually consume resources.
Ideal use cases:
- •Teams or individuals who need long-running typescript tasks with durable retries and queues
- •Teams or individuals who need concurrency controls and elastic scaling
- •Teams or individuals who need durable cron scheduling without timeouts
- •Teams or individuals who need realtime streaming from tasks to a frontend
- •Anyone focused on typescript workflows
- •Anyone focused on background-jobs workflows
🤖 Other AI Agent Infrastructure Tools to Consider
Preloop and Trigger.dev aren't the only options. Here are other popular tools in the same space:
SuperAGI
Open-source autonomous AI agent framework with visual dashboard — 14K GitHub stars
MetaGPT
Multi-agent AI framework simulating software teams — 45K GitHub stars, builds full apps from prompts
Cerebras
Fastest LLM inference powered by the Wafer Scale Engine.
Scale AI
AI data platform for training data and model evaluation.
Roboflow
End-to-end computer vision platform for developers.
Labelbox
Enterprise data labeling platform for ML training datasets.
Is one of these your tool?
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Frequently Asked Questions
Is Preloop better than Trigger.dev?
It depends on your needs. Preloop offers 6 key features including MCP firewall with allow, deny and require-approval rules on tool access and AI model gateway with per-agent budgets and cost attribution, while Trigger.dev provides 6 features including Long-running TypeScript tasks with durable retries and queues and Concurrency controls and elastic scaling. Preloop uses a open-source model with a free tier, while Trigger.dev is freemium with free access available. Choose based on which features and pricing model align with your requirements.
Is Preloop cheaper than Trigger.dev?
Trigger.dev is cheaper, starting at $10/month compared to Preloop's $29/month. Both tools offer free tiers, so you can try each before committing. Always check the official websites for the most current pricing.
Can I use Preloop and Trigger.dev together?
Yes, many users combine Preloop and Trigger.dev in their workflow. Preloop excels at mcp firewall with allow, deny and require-approval rules on tool access, while Trigger.dev shines with long-running typescript tasks with durable retries and queues. Using both allows you to leverage the strengths of each tool, though this means managing two subscriptions — though free tiers can help manage costs.
What's the main difference between Preloop and Trigger.dev?
While both are ai agent infrastructure tools, Preloop emphasizes mcp firewall with allow, deny and require-approval rules on tool access, whereas Trigger.dev is known for long-running typescript tasks with durable retries and queues. The best choice depends on your specific workflow and feature priorities.
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