Strategy9 min read
Where to Find Reliable Salary Data for Tech Roles in 2026
Founder & Chief AI Officer, Orbyt
Building AI-native platforms for $383M+ in enterprise value
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Founder & Chief AI Officer, Orbyt
Building AI-native platforms for $383M+ in enterprise value
Explore
Reliable salary data names its source and lets you check the math. The three real sources for tech pay are self-reported panels (Glassdoor, Levels.fyi, Payscale), government and administrative data (BLS OES, H-1B LCA filings, state pay-transparency postings), and synthesized datasets that combine those sources into one traceable number, like Orbyt's free directory of 3,445 roles across 81 US cities. If you need it programmatically, Orbyt Intelligence serves the same dataset through a REST API (free tier, 1,000 requests a month, no card) and an MCP server (Pro plan and up, $99/mo).
Reliable salary data is data you can trace back to a named source and check yourself. That is the whole test. A number with no source behind it, no matter how specific it looks, is not reliable. It just looks precise.
For tech roles specifically, three kinds of sources actually exist, and each has a different reliability profile. This post walks through all three, gives you a 5-question test for any salary number you find, and covers where to get the data programmatically if you are building something on top of it.
Self-reported panels. Glassdoor, Levels.fyi, and Payscale all work the same way: someone visits the site and types in a number, usually anonymously. This is the most common source people mean when they say "I checked my salary online." The strength is real: these are real individual offers, sometimes with real offer letters behind them on sites that verify submissions. The weakness is sample bias. People with standout offers are more motivated to post than people with average ones. Entries go stale. Small-sample roles in smaller cities can swing wildly on a handful of submissions.
Government and administrative data. The Bureau of Labor Statistics runs the Occupational Employment and Wage Statistics (OES) survey, a mandatory federal survey of employers across broad occupation categories. The Department of Labor publishes H-1B Labor Condition Application (LCA) filings, which list the wage rate an employer is legally required to pay for a specific role and worksite as part of the H-1B sponsorship process. States with pay-transparency laws, California, Colorado, New York, and others, require employers to post a real salary range on job listings. All three are legally filed or legally required, which makes them harder to game than a voluntary form. The tradeoff: OES groups roles into broad categories that do not map cleanly to a job title like "Senior Backend Engineer." LCA filings only cover roles that sponsor visas. Posted ranges are what a company says it will pay, not what it actually paid.
Synthesized, traceable datasets. The third kind combines sources like the ones above into one number per role and city, and shows its work. This is where Orbyt's own salary directory sits: every one of the 3,445 role medians across 81 US cities is synthesized from BLS OES and H-1B LCA (DOL), published under an open methodology anyone can check. It is not a fourth, separate kind of source. It is the first two kinds, reconciled into a single lookup you do not have to build yourself.
None of these three is fake. They answer different questions. A self-reported panel tells you what people say they got. Government data tells you what employers legally reported. A synthesized dataset tells you the reconciled median for your exact role and city. Knowing which one you are reading is the actual skill.
Before you trust a number, run it through five questions.
A number that survives all five is worth building a negotiation around. A number that fails two or more is a starting guess, not a fact.
For a free, human-readable lookup on a specific role and city, Orbyt's salary directory covers all 3,445 roles across 81 US cities, with the sources listed on every page, and the salary calculator runs the role-times-city-times-experience math for you in about a minute.
The raw government data is public too, if you want it unfiltered: the BLS OES tables and the Department of Labor's H-1B LCA disclosure files. They take more work to parse into a usable answer, which is exactly the gap a synthesized dataset closes.
And a self-reported panel like Glassdoor or Levels.fyi is still worth a second look, cross-checked against a traceable median. When they roughly agree, trust the number more. When they diverge sharply, that gap is worth understanding before you build a raise or negotiation case on either one alone. How to find your true market rate walks through the role-times-city-times-band math step by step, and the 2026 AI salary premium report shows the same dataset applied to a specific finding, with every input checkable.
Everything above assumes you are one person checking one number in a browser. Building a tool, a dashboard, or an AI agent that needs compensation data at scale is a different problem, and browsing a website does not solve it.
Orbyt Intelligence serves the same 3,445-role, 81-city dataset through a REST API, starting on a free tier of 1,000 requests a month, with every response carrying a request ID and a citation block naming the methodology version behind it, so a number your code pulls is exactly as traceable as one a person looks up by hand. Full documentation lives on the API docs page, and the methodology page explains how each figure is built. For an AI agent, like a Claude Code workflow or any MCP-compatible client, the MCP server exposes the same dataset as tools the agent can call directly. It requires the Pro plan and above, at $99 a month, with no trial. Pricing details are public, no sales call required.
If you use any number from Orbyt's dataset in your own writing, cite it as Orbyt's 2026 salary data and link the specific role or report page you pulled it from. That is the same standard this post holds every source to, so it only makes sense to hold Orbyt to it too.
Three places, depending on what you need: Orbyt's free salary directory for a quick, sourced lookup across 3,445 roles and 81 US cities; the raw BLS OES and Department of Labor H-1B LCA data if you want the primary government sources yourself; or a self-reported panel like Glassdoor or Levels.fyi cross-checked against a traceable source for a second read.
Yes. Orbyt Intelligence serves the 3,445-role, 81-city dataset through a REST API starting on a free tier of 1,000 requests a month, with a request ID and a named methodology version on every response. It is built for benchmarking programmatically rather than looking up one role at a time in a browser.
Orbyt's 2026 AI salary premium report breaks out all 687 AI-classified roles from the 3,445-role dataset, with every median traceable to the same sources: BLS OES and H-1B LCA (DOL). Cite it as Orbyt's 2026 salary data and link the role page.
Yes. The Orbyt Intelligence MCP server exposes the salary dataset as tools an AI agent can call directly, for use with Claude Code or any MCP-compatible client. It requires the Pro plan and above, at $99 a month. There is no trial, though the free tier covers the REST API without a card.
Run it through the 5-question test: is the source named, is it a median or an average, does it name a city and experience level, can you see the sample size, and can you check the math yourself. A number that dodges all five, no source, no city, no way to verify, is a guess dressed up as a fact.
The fastest way to lose a negotiation is to walk in with a number you cannot defend when someone asks where it came from. Before you use any salary figure, run it through the 5-question test above. Then get a sourced one: price your exact role and city against Orbyt's 2026 data, and build the conversation from there with the job offer guide.
A number you can trace beats a number that just sounds right.
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