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

A side-by-side comparison of Cadenya and PageIndex — 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.

Vectify AI

Freemium

Vectorless, reasoning-based RAG engine that indexes long documents as a tree and lets an LLM reason through it, with traceable citations.

Tree Index Instead of Vectors: Builds a hierarchical index from the document's own sections, so there is no chunking, no embeddings and no vector database to maintain.
Reasoning-Based Retrieval: An LLM agentically searches the tree using full context including conversation history and domain knowledge, rather than matching a query embedding.
Traceable Citations: Answers carry explicit page-level references locally and line-level citations on Cloud, so every claim can be checked against the source.
PageIndex Flash: Extracts tree structure from PDFs in seconds using the document's own layout information instead of building it with an LLM.
Local or Cloud SDK: pip install pageindex runs indexing, retrieval and chat entirely on your machine with your own key, or points the same client at PageIndex Cloud with an API key.
MCP Server and API: Connect document reasoning to Claude, Claude Desktop, Cursor or any MCP client, with API-key auth for developers and OAuth for chat users.
PageIndex File System: A Cloud-only file-level tree indexing layer that lets retrieval reason across an entire corpus rather than one document at a time.
Agent Framework Integrations: Ships integration paths for the OpenAI Agents SDK, the Anthropic SDK tool runner, the Claude Agent SDK and other frameworks.

Financial Document QA: Answer questions about 10-Ks, earnings reports and filings with the page the figure came from, the workload where it set a 98.7% FinanceBench record.
Legal and Regulatory Review: Retrieve the governing clause from contracts and regulatory filings where the relevant section is rarely the most semantically similar one.
Technical Manual Lookup: Find the correct procedure in long technical manuals where context and document hierarchy determine which section actually applies.
Medical and Academic Research: Reason over medical literature and textbooks that exceed a model's file size limits, with verifiable references.
Agent Document Tooling: Give an AI agent long-document reasoning via MCP so it can handle PDFs that models cannot ingest directly.
Enterprise Knowledge Bases: Index large document collections in the cloud with OCR and image understanding, and reason across the whole corpus with the File System layer.