How to Build an App Like Glassdoor
Building an app like Glassdoor costs $50,000–$80,000 for a vertical review platform (10–12 weeks) or $80,000–$140,000 for a full platform with reviews, salary data, job listings, and employer dashboards (14–18 weeks). The hardest problems are anonymous review credibility (preventing fake reviews with email domain verification, rate limiting, IP fingerprinting, and ML classifiers) and the content cold-start problem (launching with zero reviews). The tech stack uses Node.js, React, Elasticsearch, PostgreSQL, and Redis for fraud detection counters.
Key Takeaways
- A vertical review platform costs $50,000–$80,000 and takes 10–12 weeks. A full Glassdoor-style platform with salary data, job listings, and employer dashboards costs $80,000–$140,000 over 14–18 weeks.
- Anonymous reviews require verification, not just a checkbox. Email domain verification or LinkedIn OAuth checks confirm an employment connection without revealing who posted.
- Fake review prevention needs multiple layers: one review per company per account per 12 months, IP fingerprinting, device fingerprinting, and an ML classifier trained on fake review patterns.
- The cold-start problem kills most review sites. Seed with anonymized public salary data, partner with an existing community, and start in one city or one industry rather than launching globally from day one.
- Moderation at scale needs a tiered system: ML auto-reject for high-confidence violations, human review queue for borderline cases, and an appeals workflow for employer disputes.
Employers know something job seekers don't. What it's actually like to work there. The management style, the real salary bands, the interview process, the culture behind the career page copy. Glassdoor's entire business is closing that information gap. And it's worth $1.2 billion, the price Recruit Holdings paid for it in 2018.
The mechanism is simple. Employees write anonymous reviews. Employers can respond but cannot delete them. The asymmetry creates trust: job seekers know the reviews haven't been curated by HR, so they believe them. Employers pay for enhanced profiles and job ads because their prospective hires are definitely reading the platform before accepting an offer.
That model works in any vertical where there's an information gap between employers and workers. Healthcare staffing, franchise operations, software agencies, legal firms. If job seekers don't have reliable data on what a specific employer is actually like, there's a platform to build.
This guide covers what it takes to build one.
What "Glassdoor" actually means to build
Glassdoor is three products in one. A review platform (anonymous employee reviews, interview reviews, Q&A). A salary intelligence platform (user-contributed compensation data, aggregated into ranges). A job board (employer-posted listings, integrated with the review data so a job ad appears alongside the company's reviews and ratings).
You don't need to build all three from day one. Most vertical review platforms start with reviews and salary data. Job listings come later once there's enough traffic to make employer posting worthwhile.
The two things that make Glassdoor Glassdoor are: anonymous but verified reviews, and employer profiles they can't sanitize. Build those correctly and you have the foundation. Get them wrong and you have a site full of fake reviews that nobody trusts.
Core features to build (MVP vs. full)
MVP scope (10–12 weeks, $50,000–$80,000):
Company profiles are the anchor. Each company gets a page with name, logo, description, industry, headquarters, employee count, and an overall rating. These can start as community-sourced records that employers later claim and verify.
Anonymous reviews are the core product. Each review captures an overall rating (1–5 stars), sub-ratings for work-life balance, culture and values, management quality, and compensation, plus written pros, cons, and advice to management. The reviewer provides their job title and employment status (current or former employee) but their name is hidden. The key design decision: reviews can never be deleted by employers. Employers can flag a review for policy violations, but only a moderator can remove it. This rule is what creates trust.
Salary reports let users contribute compensation data: job title, base salary, bonus, equity, total compensation, location, experience level, and submission date. Individually these are data points. Aggregated across dozens of contributors, they become salary ranges. Display them as ranges, not individual submissions, to protect contributor privacy.
Employer responses let companies respond publicly to reviews. One response per review, visible to all readers, never edits or removes the original. This gives employers a voice without giving them control.
Search needs to cover company name lookup and review filtering by rating, date, and job title.
Full platform (14–18 weeks, $80,000–$140,000):
Add interview reviews: candidates describe the interview process, list questions they were asked, rate the experience, and note the outcome (offer received, rejected, withdrew). This is valuable data that Glassdoor has and most competitors don't.
Add Q&A: job seekers post questions about the company ("Do they support remote work?" "How is the work-life balance really?") and current or former employees answer anonymously. Think of it as the community FAQ that the company's career page never answers honestly.
Add the employer dashboard: paid feature. Employers can respond to reviews, post jobs, edit their company profile, and see analytics on how many people viewed their profile and how it compares to competitors.
Add job listings: either employer-posted directly or aggregated from partner job boards via API. The integration between a job listing and the company's review data is the differentiator. Job seekers see the job and the company's 3.2-star rating with 1,400 reviews on the same page.
The tech stack
Node.js for the API. Handles review submission, employer dashboard, search queries, and moderation workflows.
React for the frontend. Review submission forms, company profile pages, salary data visualizations, search results. All client-side interactive.
Elasticsearch for company search and salary data aggregation. Company name search needs fuzzy matching (searching "google" should return "Google LLC"). Salary aggregation queries calculate percentiles across all submissions for a given job title and location. Elasticsearch handles both faster than PostgreSQL can at scale.
PostgreSQL as the primary database. Reviews, salary submissions, user accounts, company records, employer responses, moderation decisions. All structured relational data.
Redis for fraud detection. Rate limiting counters (reviews per user per company per time window), IP-based request counters, and device fingerprint tracking all need sub-millisecond read/write speed. Redis handles this without touching the main database.
SendGrid for verification emails. When a user submits a review, they verify with a work email address or receive a confirmation at the address they provided. This is the first layer of fraud prevention.
Stripe for employer subscriptions. Enhanced profiles and job posting are paid features. Monthly subscription billing, not per-click.
Python for the ML fraud detection model. Training a classifier on fake review patterns (short text length, extreme rating with no written detail, submissions clustered in time, matching device fingerprints) requires a separate ML service. Python has better tooling for this than Node.
How long it takes and what it costs
Company profiles, anonymous reviews with email verification, salary reports, employer responses, company search
Interview reviews, Q&A, job listings, employer dashboard, fraud detection ML, tiered moderation system
The cost gap between MVP and full platform is mostly driven by two components: the fraud detection system (ML model training, integration, and ongoing tuning) and the moderation infrastructure (human review queue, appeals workflow, employer dispute handling). Both are essential. A review site without good fraud detection fills with fake reviews within weeks of launch.
Ongoing costs: hosting runs $400–$1,000/month, Elasticsearch adds $200–$500/month, and moderation labor (if you're using human moderators for borderline cases) is the variable cost to plan for.
The hardest technical challenge
Anonymous review credibility. Glassdoor fights fake reviews constantly. Employers post clusters of positive reviews to push up their ratings before recruiting season. Competitors post negative reviews to damage rivals. Disgruntled former employees post coordinated negative reviews in batches.
You need multiple layers of defense working together.
Email domain verification is the first gate. Require reviewers to verify with a work email address, or connect their LinkedIn profile to confirm employment history. You don't store which specific person submitted which review, but you confirm there's a real employment connection before publishing.
Rate limiting is the second gate. One review per company per user account per 12 months. If someone worked at three companies, they can review all three. But they can't submit twenty positive reviews for their current employer. Implement this as a Redis counter keyed on user ID and company ID.
IP fingerprinting is the third gate. Multiple reviews submitted from the same IP address within a short window are a fraud signal. Log IP addresses at submission time (not published, for internal fraud detection only). Flag review clusters from the same source for human review.
Device fingerprinting adds another layer. Browser fingerprinting using canvas rendering, installed fonts, screen dimensions, and timezone data creates a device signature without cookies. Multiple review submissions from the same device fingerprint across different accounts is a strong fake review signal.
The ML classifier is the last line of defense. Train it on labeled examples of fake and real reviews. Fake review signals include: very short text (under 30 words), extreme ratings (1 or 5 stars) without corresponding written detail, submission timestamps clustered together, language patterns that match the company's own marketing copy (fake positive reviews), and new account age (account created within 24 hours of review submission). High-confidence violations auto-reject. Borderline cases go to the human queue.
The second hard problem is moderation at scale. You cannot manually review every submission, especially after the platform grows. The tiered system described above is the solution: ML handles the clear violations, humans handle the ambiguous ones, and a clear appeals process gives both reviewers and employers a path when they disagree with a decision.
Build vs. Glassdoor: when custom wins
Build when you're serving a vertical that Glassdoor doesn't serve well. Restaurant and hospitality workers have different concerns than software engineers. Healthcare workers in nursing have specific license and credential contexts. Franchise employees have a franchisor-franchisee relationship that doesn't fit the standard employer-employee frame.
Build when you have an existing community. A professional association with 20,000 members in a specific field can seed reviews from members who already trust the association. That's a content advantage Glassdoor doesn't have in the niche.
Build when transparency is a feature of your broader product. An agency or freelance marketplace that wants to differentiate on verified client reviews. A B2B software directory for a specific vertical. A franchisor audit platform where franchisee transparency is the selling point.
Don't build a general Glassdoor competitor. Glassdoor has 100 million reviews and 15 years of accumulation. The general market has a content moat that's effectively insurmountable for a new entrant without a massive content acquisition budget. The niche approach wins because depth beats breadth in specific verticals.
How RaftLabs can help
A review platform combines things that are individually hard: anonymous but verified identity, fraud detection at submission time, ML classification at scale, and a monetization model that balances employer revenue with job seeker trust.
We've built data-intensive platforms, trust-critical applications, and community tools where the technical choices directly affect whether users believe in the product. The specific engineering challenges here, fraud detection, moderation infrastructure, and the cold-start content problem, are ones we've worked through before.
Our typical engagement starts with a 2-week discovery sprint to define scope, make architecture decisions, and plan the build week by week. For a vertical review MVP, budget $50,000–$80,000 and 10–12 weeks. For the full platform, budget $80,000–$140,000 and 14–18 weeks.
If you're building something in between, tell us what you have in mind and we'll scope it precisely.
Related reading: Build vs. buy software, SaaS app development cost, and how to build a job portal like Indeed if you're combining reviews with job listings.
Frequently asked questions
- A vertical review platform for a specific industry costs $50,000–$80,000 and takes 10–12 weeks. This covers company profiles, anonymous reviews, salary reports, and basic employer responses. A full platform with salary data aggregation, interview reviews, job listings, Q&A, employer dashboard, and fraud detection costs $80,000–$140,000 and takes 14–18 weeks. The biggest cost drivers are the fraud detection system and moderation infrastructure — both are essential and both take significant engineering time.
- Glassdoor uses email domain verification and employment history checks. When someone submits a review, they verify with a work email address or connect LinkedIn to confirm they worked at the company. The review publishes without their name, but the platform can confirm the reviewer has an employment connection. Rate limiting (one review per company per 12 months per account) and IP fingerprinting (flagging multiple reviews from the same IP address) add additional layers. An ML classifier trained on fake review patterns, short text, extreme ratings with no detail, batch submissions, catches what the rules miss.
- You can't fully prevent fake reviews, but you can detect them systematically. Build a tiered moderation system: ML classifier scores every incoming review for policy violations and fake signals. High-confidence violations auto-reject. Borderline cases go to a human review queue. Approved reviews publish. Build an appeals workflow so employers can flag reviews that violate policy and get a human decision within 48 hours. The key rule is that employers can respond to reviews publicly but cannot delete them. Allowing deletion destroys the trust that makes the platform valuable.
- Start narrow, not broad. Pick one city or one industry and build depth there before expanding. Seed salary data from public sources: H-1B Labor Condition Application disclosures are public records and include job title, employer, location, and wage. This gives you salary data before anyone contributes. Partner with an existing community (a professional Slack group, Discord, or industry forum) to get early reviews from members. Offer reciprocal access: contribute salary data to see salary data. This converts passive visitors into contributors.
- Glassdoor's value is 100 million reviews that employers cannot delete. That volume took 15 years and an acquisition to build. You cannot replicate that on a general platform. What you can replicate is the model within a vertical: restaurant workers reviewing employers, healthcare workers reviewing hospital systems, franchise employees reviewing franchisors. The niche version of Glassdoor has a credibility advantage over the general version because the reviewer community is more specific and employers are more likely to be present.
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