PostHog vs Snowplow
An independent, review-free comparison compiled by the SaaSTracker editorial team. Both products are profiled in full, and neither can pay for placement here.
Still shortlisting? Browse the best in GTM Analytics and the best in Customer Data Platforms for every option we track, with award winners called out.
The short answer
Editorial assessmentSnowplow compared with PostHog
PostHog bundles collection with product analytics, session replay, feature flags, and experimentation in one self-hostable platform, which is an entirely different value proposition. Snowplow provides no analysis at all. Product teams wanting answers quickly should look at PostHog; data platform teams building their own analytical and machine learning layer should look at Snowplow.
Choose PostHog if
Engineering-led SaaS and product teams that want web analytics, product analytics, replay, flags, and experimentation consolidated in one usage-priced platform, and that are comfortable doing their own revenue-attribution modeling if they need it.
Choose Snowplow if
Data-mature organizations that need complete, granular, schema-validated behavioral data in their own infrastructure to power analytics, machine learning, and personalization, and that have engineering capacity to operate a pipeline.
Side by side
13 attributes| Attribute | PostHog | Snowplow |
|---|---|---|
| Category | Analytics | CDP |
| Starting price | $0 (generous monthly free tiers; pay only past the allowance) | Free and open source to self-host; commercial deployments quoted, typically enterprise-scale annual contracts |
| Pricing model | Usage-based pricing per product: every product has a monthly free allowance, then per-unit billing (per event, recording, flag request, survey response, or row) with steep volume discounts; optional platform packages add support and compliance features. | Open-source components are free to self-host. The commercial offering is a quoted annual subscription based on event volume and deployment model, with the pipeline typically running in the customer's own cloud account, where infrastructure costs are additional and paid to the cloud provider. |
| Free plan | 1M analytics events, 5K session recordings, 1M feature-flag requests, 100K exceptions, 1,500 survey responses, and 1M data warehouse rows per month, on 1 project with 1-year retention. | Open-source edition, self-hosted with no license fee |
| Free trial | Not applicable; the free tier is permanent, not a trial | Trial and proof-of-concept arrangements through sales |
| Best for | Engineering-led SaaS and product teams that want web analytics, product analytics, replay, flags, and experimentation consolidated in one usage-priced platform, and that are comfortable doing their own revenue-attribution modeling if they need it. | Data-mature organizations that need complete, granular, schema-validated behavioral data in their own infrastructure to power analytics, machine learning, and personalization, and that have engineering capacity to operate a pipeline. |
| Setup time | Minutes to first data with the JS snippet and autocapture; days to weeks to define clean custom events, dashboards, and cohorts; warehouse sources and pipelines are a separate project. | Weeks to months. Managed deployment into a cloud account, schema design, tracker implementation across surfaces, and downstream modelling all take real time, and schema design in particular rewards care. |
| Learning curve | Low for the web analytics dashboard; moderate for funnels, cohorts, and replay; high for SQL insights, warehouse modeling, and experimentation statistics. | Steep for teams new to schema-first data collection, and the discipline extends beyond engineering: agreeing what an event means across departments is the slow part. |
| Platforms | Web app, JavaScript snippet and web SDK, Server SDKs (Python, Node, Go, PHP, Ruby, and others), Mobile SDKs (iOS, Android, React Native, Flutter), Self-hosted Hobby build (Docker, open source) | Web trackers, Mobile SDKs, Server-side trackers across languages, Streaming infrastructure on AWS, GCP, and Azure |
| Compliance | SOC 2 Type II, GDPR, HIPAA (BAA on Boost, Scale, or Enterprise package) | GDPR, CCPA, SOC 2, HIPAA-capable deployments |
| Founded | 2020 | 2012 |
| Headquarters | Remote-first; US-incorporated (San Francisco), team distributed globally | London, United Kingdom |
| Ownership | Venture-backed | Private, venture-backed |
Strengths and limitations
PostHog
Strengths
- Breadth with real integration: analytics, replay, flags, experiments, surveys, error tracking, and a warehouse genuinely share one event stream and identity graph rather than being bolted-on acquisitions.
- The most generous free tier in the category, 1M events and 5K recordings monthly, permanently, which makes evaluation and early-stage use genuinely free.
- Open-source codebase plus a choice of US or EU (Frankfurt) cloud gives privacy and procurement teams inspectability and residency options most rivals lack.
- Usage pricing with automatic volume discounts and per-product spending caps scales from hobby project to hundreds of millions of events without a sales call.
Limitations
- No packaged B2B revenue attribution: connecting spend and touchpoints to CRM pipeline and closed-won revenue is a data-warehouse project in PostHog, not a built-in report as in Dreamdata or HockeyStack.
- Depth demands technical investment; non-technical marketers can read the web dashboard but will struggle to self-serve funnels, SQL insights, or warehouse joins.
- Usage-based billing is unpredictable without configured limits, and replay-heavy or autocapture-noisy sites can generate surprising invoices.
- The full event platform carries more GDPR surface than minimalist tools: consent, masking, and retention need deliberate configuration, where Plausible or Fathom are compliant nearly by default.
Snowplow
Strengths
- Complete ownership of raw behavioral data in your own infrastructure with no sampling.
- Schema validation at collection, which is the strongest available defence against data quality decay.
- Failed-events handling makes instrumentation problems visible and recoverable rather than silent.
- Entity contexts produce a dataset that remains analyzable long after the questions it was built for.
Limitations
- No reporting or visualization layer at all; everything downstream is your responsibility.
- Substantial engineering commitment even on the managed service.
- Cloud infrastructure and warehouse costs are additional and grow with volume.
- Pricing and positioning exclude small businesses entirely.
Pricing compared
PostHog
Usage-based pricing per product: every product has a monthly free allowance, then per-unit billing (per event, recording, flag request, survey response, or row) with steep volume discounts; optional platform packages add support and compliance features.
- Free (no card)$0
- Pay-as-you-goUsage-based
- Platform packages (Boost, Scale, Enterprise)$250 / $750 / $2,000
At small and mid scale PostHog is close to unbeatable on price: the permanent free tiers cover a real startup's entire measurement stack, and the per-unit rates undercut buying analytics, replay, flags, and experimentation separately. The honest caveat is that usage pricing shifts the budgeting burden onto you: a replay-heavy or event-noisy implementation can quietly cost more than a flat-rate point tool, and the platform packages needed for enterprise compliance add a fixed $250 to $2,000 a month. Treat it as extremely cheap by default and only as cheap as your instrumentation discipline at scale.
Snowplow
Open-source components are free to self-host. The commercial offering is a quoted annual subscription based on event volume and deployment model, with the pipeline typically running in the customer's own cloud account, where infrastructure costs are additional and paid to the cloud provider.
- Open source$0
- Snowplow commercialQuoted
- EnterpriseQuoted
Snowplow is expensive in every sense: licensing, infrastructure, and engineering attention. It earns that when behavioral data is a strategic asset feeding models and products rather than dashboards, because no conventional analytics vendor will give you complete, validated, owned event data at that granularity. Teams whose questions are answered by aggregate reports are paying an enormous premium for optionality they will not exercise, and should buy a conventional analytics tool instead.
Editorial verdict on each
PostHog
PostHog is the default recommendation in this category for any team with an engineer on it: no rival matches the combination of a free-forever tier that covers real workloads, usage pricing that scales without a sales call, and genuinely integrated replay, flags, and experimentation on top of web and product analytics. The two honest reservations are that marketing teams without technical support will use a fraction of it, and that B2B pipeline attribution, the question GTM leaders most want answered, remains a do-it-yourself exercise on PostHog's warehouse rather than a shipped feature. Buy it as the behavioral system of record; budget separately if you need turnkey revenue attribution.
Read the full PostHog profileSnowplow
Snowplow is the most rigorous answer available to the question of behavioral data, and rigor is exactly what it charges for. Schema validation at collection, failed-event capture, entity contexts, and deployment inside your own cloud account together produce a dataset that remains trustworthy and analyzable years later, which no conventional analytics vendor offers at any price. It also provides nothing that resembles an answer on its own: no dashboards, no reports, no quick wins, and a time to first insight measured in weeks. That makes the buying decision unusually clear. If behavioral data feeds models, products, and decisions at a scale where ownership matters, it is the reference implementation. If you want to know how many people visited the pricing page, it is emphatically not for you.
Read the full Snowplow profilePostHog profile last reviewed 2026-09-27; Snowplow last reviewed 2026-08-22. Pricing is compiled from public sources and can change without notice. See our methodology.
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Frequently asked questions
6 questionsWhat is the difference between PostHog and Snowplow?
PostHog bundles collection with product analytics, session replay, feature flags, and experimentation in one self-hostable platform, which is an entirely different value proposition. Snowplow provides no analysis at all. Product teams wanting answers quickly should look at PostHog; data platform teams building their own analytical and machine learning layer should look at Snowplow.
Is PostHog or Snowplow cheaper?
PostHog starts at $0 (generous monthly free tiers; pay only past the allowance). Snowplow starts at Free and open source to self-host; commercial deployments quoted, typically enterprise-scale annual contracts. The billing models differ, so the entry price is rarely the whole cost. PostHog pricing model: Usage-based pricing per product: every product has a monthly free allowance, then per-unit billing (per event, recording, flag request, survey response, or row) with steep volume discounts; optional platform packages add support and compliance features. Snowplow pricing model: Open-source components are free to self-host.
Does PostHog or Snowplow have a free plan?
PostHog has a free plan. 1M analytics events, 5K session recordings, 1M feature-flag requests, 100K exceptions, 1,500 survey responses, and 1M data warehouse rows per month, on 1 project with 1-year retention. Trial terms: Not applicable; the free tier is permanent, not a trial. Snowplow has a free plan. Open-source edition, self-hosted with no license fee. Trial terms: Trial and proof-of-concept arrangements through sales.
Who should choose PostHog?
Engineering-led SaaS and product teams that want web analytics, product analytics, replay, flags, and experimentation consolidated in one usage-priced platform, and that are comfortable doing their own revenue-attribution modeling if they need it.
Who should choose Snowplow?
Data-mature organizations that need complete, granular, schema-validated behavioral data in their own infrastructure to power analytics, machine learning, and personalization, and that have engineering capacity to operate a pipeline.
What are the best alternatives to PostHog and Snowplow?
SaaSTracker profiles 16 products in GTM Analytics. The Summer 2026 awards in the category went to Google Analytics 4 (Category Leader), Umami (Best Value), HockeyStack (Momentum). SaaSTracker profiles 16 products in Customer Data Platforms. The Summer 2026 awards in the category went to Dataddo (Ease of Use). Every profile is compiled from primary sources, so a shortlist can be built from pricing, limitations, and fit rather than from star ratings.
