Portfolio Lab vs Trigger.dev: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Portfolio Lab and Trigger.dev — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
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Portfolio Lab
alphaAI Capital Management
AI-powered platform to build, validate, and auto-execute systematic investment strategies through your own brokerage.
Key features
- AI Strategy Builder: Describe an investing goal and Portfolio Lab generates several complete, tunable strategies with holdings, entry/exit rules, and rebalance logic.
- Multi-Objective Optimization: Every build outputs strategies optimized for Return, Sharpe ratio, and Minimum Drawdown so you can compare tradeoffs side by side.
- Live Paper Validation: Strategies are stress-tested with in-sample and out-of-sample data, then run on live paper trading with real market prices and simulated fills.
- Agentic Trading via MCP: Claude, ChatGPT, or any MCP-compatible agent connects to your own brokerage account, mirrors your portfolio, and places timestamped trades.
- Diverse Strategy Rulebooks: Risk-Aware Hedged, Tactical Long-Only, Tactical Long/Short, Momentum, Mean Reversion, and Leveraged Regime Switcher — each with a clear discipline.
- Specialized AI Models: Seven purpose-built models with 200+ predictors work over fundamental, technical, estimate, macroeconomic, and alternative data — not LLM guesswork.
- Full Explainability: Every trade, allocation shift, and move to cash is visible, timestamped, and auditable so you can see exactly why the strategy acted.
Best for
- Systematic Long-Term Portfolios: Build a core hedged strategy that shifts to cash or adds a downside hedge automatically when AI detects elevated market risk.
- Trend Following: Deploy momentum strategies that rank assets by signal strength and rotate into the strongest performers while cutting losers.
- Dip Buying: Run mean-reversion strategies that systematically buy oversold assets and exit when prices snap back, with a cash buffer during high-risk regimes.
- Agent-Driven Execution: Let your AI agent auto-execute today's trade plan in your own Robinhood or brokerage account without giving up custody.
- Strategy Research: Explore how the same idea performs under different objectives (Return vs. Sharpe vs. Drawdown) before deploying real capital.
- Hedged Trend Following: Combine momentum with an AI-activated margin hedge to capture trends while protecting against sharp reversals.
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Trigger.dev
Trigger.dev, Inc.
Open-source TypeScript platform for durable AI agents and long-running workflows with no timeouts, plus queues, retries, and observability.
Key features
- No-timeout task runtime: Tasks run for as long as they need — hours if necessary — unlike Lambda or Vercel functions, making it usable for long-running agents and heavy batch jobs.
- Durable AI agents: Chat agents survive tab closes, refreshes, redeploys, and crashes because their execution state is checkpointed by the platform.
- Streaming to the frontend: Stream tokens or intermediate step output straight to your UI with no extra API routes to build or maintain.
- Tool calling and human-in-the-loop: First-class primitives for LLM tool calls and for pausing runs on human approval before continuing.
- Queues, retries, idempotency: Built-in job queues, retry policies, and idempotency keys so you don't hand-roll reliability around every AI call.
- Self-host or managed cloud: Apache 2.0 core with a documented self-hosting path, plus a managed cloud for teams that want elastic scale without ops.
Best for
- Long-running chat agents: Support or research chat agents that keep working across sessions and stream results back to the browser once the user returns.
- Multi-step LLM pipelines: RAG pipelines that fan out to hundreds of documents, retry failed calls, and finish minutes or hours later without a client staying connected.
- Human-in-the-loop workflows: Agents that draft output, pause for a human approval step in Slack or a web UI, and resume automatically once approved.
- Batch AI processing: Nightly jobs that classify, embed, or transform thousands of records with automatic queueing and observability.
- Backend for autonomous agents: Serves as the durable execution layer for autonomous agents built with the OpenAI Agents SDK, Vercel AI SDK, or custom orchestration.