Experiential Labs vs Portfolio Lab: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Experiential Labs and Portfolio Lab — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Experiential Labs
Experiential Labs
Open-source AI gateway that routes every model through one endpoint at provider cost, then improves that traffic with caching, routing and fine-tuning.
Key features
- Unified Model Endpoint: One OpenAI-compatible POST endpoint fronts every hosted provider, your own bring-your-own keys and your own GPUs, so switching models is a parameter change rather than an integration.
- Zero-Markup Routed Tokens: Routed traffic bills at the provider's list price with 0% added on top, with the company earning on hosted inference and the Pro plan instead of on your tokens.
- Model Recommendation from Traffic: The intelligence layer watches real request patterns and tells you when switching models would win, including newly released models on the day they ship, with optional per-prompt optimization.
- Caching Opportunity Detection: Identifies where cache hit rate could improve and shows the projected savings, with repeated tokens returning at 90% off once enabled.
- Traffic-Trained Custom Models: Fine-tunes a model on your own traffic and proves it in closed-loop simulation before it ever serves, then exposes it through the same endpoint you already call.
- Spend Attribution Console: Breaks requests and dollars down by agent, person, model, provider and day across the whole organization, alongside catalog, usage and limits.
- Live Request Logs and Metrics: Streams per-request time-to-first-token, token counts, provider, status and cost, with dashboard rollups for requests, spend, p50 TTFT and cache hit rate.
- Governance Controls: Budgets, provider allowlists and attribution are available from the free tier upward for controlling who can spend what on which models.
Best for
- Consolidating Multi-Provider Access: Replace separate SDKs and keys for OpenAI, Anthropic, Google and others with a single endpoint and key across every application.
- Cutting Inference Spend: Use caching recommendations and model-switch suggestions to lower the cost of an existing production workload without changing application code.
- Replacing a Frontier Model with a Small One: Distill or fine-tune a small model on your own traffic for a narrow repetitive task and serve it at a fraction of frontier-model cost and latency.
- Chargeback and Budgeting: Attribute AI spend to individual agents, teams or people for internal cost allocation and to enforce per-key budget caps.
- Evaluating New Model Releases: Compare a newly shipped model against your current one on your own traffic before committing to a migration.
- Hybrid Local and Hosted Serving: Route some workloads to self-hosted GPUs at zero marginal cost while sending the rest to hosted providers through the same interface.
- Self-Hosting the Gateway: Run the open-source gateway inside your own infrastructure when hosted routing is not an option.
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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.