Experiential Labs vs Olostep: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Experiential Labs and Olostep — 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.
Olostep
Olostep Technologies
A single web data API for AI agents that scrapes, crawls, maps, searches, answers, and monitors sites, returning clean Markdown, HTML, or JSON.
Key features
- Unified Web Data API: Seven endpoints — scrapes, crawls, maps, batches, searches, answers, and monitors — behind one object-oriented client instead of separate services.
- Clean Format Output: Returns any URL as Markdown, HTML, screenshots, or structured JSON so content is immediately usable as LLM context.
- Site Crawling with URL Globs: Retrieve every page on a site with max-page limits and include/exclude glob patterns, then iterate pages and fetch content per page.
- Batch Processing: Process up to 100,000 URLs in roughly five to seven minutes for large enrichment and indexing jobs.
- Natural-Language Search and Answers: Query the web in plain language for ranked links, or use the answers endpoint to get an AI-composed response with sources.
- Web Monitors: Set up monitors that fire when something changes across the web, such as a company publishing a new blog post.
- JS Rendering with Residential IPs: All requests, including those on the free trial, are JavaScript-rendered and routed through residential IP addresses.
- Agent-Native Access: Native Python and Node.js SDKs plus an MCP server and CLI so coding agents and AI tools can call Olostep directly.
Best for
- RAG Pipeline Ingestion: Crawl documentation sites and convert every page to clean Markdown for embedding into a retrieval index.
- Agent Web Browsing: Give an autonomous agent reliable read access to any URL through an MCP server rather than a brittle custom scraper.
- Lead and Company Enrichment: Batch-process tens of thousands of company URLs to extract structured firmographic data.
- Competitive Monitoring: Watch competitor blogs, changelogs, and pricing pages and get notified when they change.
- Real-Time Research: Run natural-language searches and pull AI-generated answers with source links inside an application.
- Price and Catalog Tracking: Scrape ecommerce listings on a schedule to keep an internal pricing dataset current.