Orbyt vs Pave.

At a glance.

Infrastructure coverage.

What Pave is.

Pave is a compensation benchmarking and planning platform founded in 2019 by Matt Schulman (former early Facebook engineer), headquartered in San Francisco. The product pulls real-time data from connected customer payroll systems and surfaces benchmarks inside a polished HR-facing dashboard. Pave raised $146M in Series C funding in 2022 at a reported $1.6B valuation and now serves roughly 6,000 companies: mostly VC-backed startups at Series A through Series C. For participating companies, the real-time payroll feed makes the dataset fresher than anything else in the category; the network effect is genuine. Outside the customer network, coverage drops sharply: public companies, enterprise SaaS, frontier AI labs that are not customers, non-tech industries, and regional employers are effectively not benchmarked. Orbyt Intelligence ships coverage that is source-driven, not customer-driven: 3,445 roles across 81 U.S. cities, whether or not any given company has ever signed with Orbyt.

Pricing, head to head.

Pave pricing is not published. Access requires a sales qualification call, with quotes typically ranging from low five figures to mid six figures annually depending on seat count, feature scope (benchmarking only vs. benchmarking plus comp planning), and company size. There is no self-serve developer tier. Demo access is behind the sales process. Orbyt Intelligence publishes its pricing grid in full: Free (60 req/min, 1,000 requests a month, AI Role Taxonomy engine, no card), Pro at $99 per month (300 req/min, MCP server, /lineage provenance), and Ultra at $199 per month (1,500 req/min, company leveling catalog, annual reports). Every tier is visible at orbyt-intelligence/pricing: no sales call required. For an HR team at a VC-backed startup already running Pave, the two products solve different problems. For anyone outside that buyer profile, Pave is closed and Orbyt is open, starting free.

Orbyt wins.

A club, or the public square.

Feature by feature.

On real-time freshness inside the customer network, Pave is best-in-class: the payroll-feed data refreshes continuously for participating companies and the benchmarks reflect the true current state of the Pave customer base. On coverage breadth outside that network, Orbyt Intelligence wins by a wide margin. Pave's dataset is concentrated on VC-backed startups in major hubs; Orbyt covers 3,445 roles across 81 U.S. cities including FAANG, enterprise SaaS, public companies, and frontier AI labs regardless of whether they are Orbyt customers. On developer experience, it is not close: Pave has no self-serve API tier, no MCP manifest, no OpenAPI spec. Orbyt ships all three starting on the free tier. On license, Orbyt is CC BY 4.0 (cite, redistribute, train models, embed); Pave data is contract-locked even for paying customers. On forward projections, Orbyt models through 2030 with a public methodology; Pave reports current-quarter only. On emerging-role coverage specifically, Orbyt tracks 598 specialized roles with leveling frameworks for Anthropic, OpenAI, DeepMind, Meta AI, and Cohere; Pave's AI-lab coverage depends on customer status.

Who each is for.

Use Pave if you are the head of People at a VC-backed startup in the Pave customer network, you need real-time peer benchmarks against similar-stage companies, and you also want a comp planning workflow integrated with the benchmarking data. For that buyer, inside that network, Pave is probably the best product on the market. Use Orbyt Intelligence if your coverage needs extend past the VC-backed startup ecosystem, if you are a developer building a feature, if you are an AI team running agent workflows, if you are a researcher citing data in a paper, or if your use case requires a license that permits redistribution. The products are not in direct competition for the same buyer: they serve different markets with different features. Most teams will end up running both if they have the budget and the breadth requirements.

Bottom line, in 2026.

In 2026, Pave owns real-time compensation benchmarking for VC-backed startups and Orbyt Intelligence owns open programmatic salary data for everyone else. Pave will keep being the right answer inside its customer network. Orbyt will keep being the only answer for public companies, AI labs outside the Pave customer list, developers, researchers, AI agents, and anyone who needs a license that permits building on top of the data. The two products solve adjacent problems; the market segmentation is clean. What matters is picking the right one for the use case. If the use case is 'benchmark my startup against Series B peers for next year's comp review,' Pave wins. If the use case is 'embed salary data in a product, query it from an AI agent, cite it in a paper, or cover employers who have never heard of Pave,' Orbyt wins.

How to migrate from Pave to Orbyt.

Most teams do not migrate off Pave: they add Orbyt Intelligence for the use cases Pave cannot reach. If you are using Pave for startup comp benchmarking and your coverage, licensing, or developer experience requirements have grown past what Pave ships, here is the path.

1. ✓Sign up for an Orbyt Intelligence account at intelligence/signup. The free tier unlocks 60 req/min with Bearer authentication and the AI Role Taxonomy engine, with no card.
2. ✓Generate an API key from the API dashboard. Set `ORBYT_INTELLIGENCE_KEY` as an environment variable. The OpenAPI 3.1 spec is at /openapi-intelligence.yaml.
3. ✓Map your current Pave role taxonomy to Orbyt role slugs. Orbyt's 3,445 roles include every startup role Pave covers, plus FAANG, enterprise SaaS, public companies, and AI labs that Pave benchmarks inconsistently depending on customer status.
4. ✓For AI agents, drop the Orbyt MCP manifest URL into Claude Desktop or ChatGPT Actions. Manifest is at /mcp-intelligence.json. The agent queries salary data as a first-class tool. Pave has no MCP story.
5. ✓Replace any Pave CSV exports or API workflows with `/api/v1/intelligence/salaries` queries. Orbyt returns structured base/equity/bonus/signing with percentile bands and source citations.
6. ✓Confirm your citation complies with CC BY 4.0. Orbyt's required attribution is 'Orbyt Intelligence, Q2 2026' plus a link to the dataset. Pave's data cannot be legally redistributed in a derivative product, even by paying customers.

Most teams finish the migration in under a day. The largest practical win is coverage outside the VC-backed startup slice: AI labs, public companies, enterprise SaaS, regional employers, and non-tech roles all become first-class. The licensing win (CC BY 4.0 vs. contract-locked) is the durable one for anyone planning to build on top of the data.

Response shape is stable. OpenAPI spec published at /openapi-intelligence.yaml. Every field is cited, versioned, and under CC BY 4.0.