Experiential Labs vs Manifest: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Experiential Labs and Manifest — 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.
Manifest
Omfang
API that turns any webpage into a structured JSON action manifest — every button, form, and input an AI agent can use, with required fields spelled out.
Key features
- URL-to-Action Manifest: A single POST call turns any page into a JSON manifest of buttons, forms, and inputs with descriptions and required flags.
- Accessibility-Tree Grounding: Reads the page the way a screen reader does, so labels and roles come from semantic signals instead of guessed CSS.
- DOM Cross-Referencing: Augments the accessibility tree with input types, required fields, placeholders, and disabled states pulled from the live DOM.
- Redesign-Resilient Selectors: No hand-maintained CSS paths — Manifest re-derives the action set per request so page redesigns don't break your agent.
- Structured Navigation Extraction: Returns the page's navigation links alongside actions so agents can plan multi-step flows across a site.
- Developer-First API: Simple REST endpoint, live playground, and docs designed for teams building browser-using AI agents.
Best for
- Agent Web Task Execution: Give an autonomous agent a machine-readable list of what it can actually do on any given URL before it acts.
- Automation Without Brittle Scrapers: Replace hand-written selectors with a resilient action layer that survives page redesigns.
- Form-Filling and Sign-Up Automation: Let agents discover required fields, input types, and validation constraints before submitting a form.
- Multi-Site Workflow Orchestration: Compose flows that span vendor portals, SaaS dashboards, and public sites using a consistent action schema.
- QA and UI Auditing: Snapshot a page's interactive surface as structured data to compare across releases or audit accessibility gaps.