Cadenya vs Illume Labs: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of Cadenya and Illume Labs — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

Cadenya

Paid

A managed agent runtime that layers your tools, agents and objectives so teams can test agent behavior safely and iterate fast.

Unified Tool Layer: Connect MCP servers, OpenAPI specs and existing endpoints once, and expose them to every agent through a single managed interface.
Model-Agnostic Variations: Set a default model and run canary variations on other providers side by side to compare behaviors before promoting a change.
Progressive Tool Discovery: Tool schemas stay out of the context window until an agent asks for them, with configurable max tools per search, search hints and a rerank threshold, so every request gets smaller.
Live Token Metering: Track cost as it accrues across loops, active variations, memory entries and widgets, so usage is visible rather than discovered on the invoice.
Webhooks and SSE Streaming: Push agent events — assistant messages, tool results, approval requests, sub-agent spawns, compaction, timeouts — into your own apps in real time.
Memory Layers: Attach stored documents such as playbooks and policy sets to an agent so its guidance persists across objectives.
Outcome Feedback Scoring: Collect scored comments on each objective, attributed to the variation and model that produced it, to see which behaviors actually work.
Embeddable Widgets: Drop an agent experience into any frontend as a widget rather than building the conversational surface yourself.

Operational Exception Handling: Run an agent that detects stalled shipments or orders and reroutes them through your dispatch API within policy.
Safe Model Migration: Evaluate a new frontier model as a canary variation against live objectives before switching the default.
Wrapping Existing APIs: Turn internal OpenAPI endpoints into agent-callable tools without rewriting the services behind them.
Embedding Agent Chat in a Product: Ship a conversational agent surface into an existing frontend using widgets instead of building it in-house.
Cost Control at Scale: Use progressive discovery and live metering to keep context size and per-loop cost down as agent traffic grows.
Agent Quality Review: Compare scored feedback across variations to understand which prompt or model changes improved real outcomes.

Freemium

A 24/7 personalized AI health companion you text — connects wearables, bloodwork, and genomics to give actionable longevity insights.

Text-first Interface: Talk to Illume over SMS-style chat, so tracking and coaching happen in the same place as everyday messaging.
Wearable Sync: Automatically pulls sleep, activity, and recovery data from connected wearables to keep context up to date.
Meal Photo Logging: Text a photo of any meal to log it and get nutrition breakdowns in context of your goals.
Bloodwork & Lab Uploads: Upload lab panels so Illume can reason across biomarkers alongside daily signals.
Cross-source Pattern Detection: Connects insights across wearables, labs, and food logs that individual apps can't see on their own.
Longevity Focus: Frames advice around long-horizon health outcomes rather than isolated daily scores.
24/7 Availability: Always-on personal companion for questions, check-ins, and adjustments to your routine.

Personal Health Monitoring: Individuals who want a single AI that reasons across their wearables, labs, and diet in one thread.
Longevity & Wellness Planning: People optimizing for long-term health metrics rather than single-app scores.
Nutrition Tracking: Users who prefer texting meal photos over manual food-log apps.
Post-lab Interpretation: Turning a bloodwork PDF into concrete lifestyle changes without a clinician visit.
Recovery & Training: Athletes correlating sleep, HRV, and training load with performance and recovery.