Experiential Labs vs PageIndex: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Experiential Labs and PageIndex — 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.
PageIndex
Vectify AI
Vectorless, reasoning-based RAG engine that indexes long documents as a tree and lets an LLM reason through it, with traceable citations.
Key features
- 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.
Best for
- 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.