Usage-based tiers by product

Once you pass a product's free quota, PostHog charges per unit with step-down pricing: the more you use, the lower the per-unit cost at higher volumes. Each product has its own meter, rates, and thresholds. The tables below reflect published rates as of August 2026.

1. Analytics (per event)

Analytics bills per event, with the first 1M events free each month.

Two optional analytics add-ons layer on top: group analytics (event data tied to a company or account) at $0.000071 per event, and data pipelines that forward events to your warehouse at $0.000062 per event.

Worked example, 12M events in a month: first 1M free, next 1M at $0.0000500 ($50.00), remaining 10M at $0.0000343 ($343.00). Total: $393.00.

One nuance the headline rate hides: PostHog charges an extra person profiles rate on events tied to identified users, on top of the base event rate. 

It's free for the first 1M, then starts at $0.0001980 per identified event in the 1M to 2M tier and steps down from there. 

The $393 example above assumes anonymous events, so traffic from identified users costs more than the base ladder alone suggests.

2. Session replay (per recording)

Session replay bills per recording, with the first 5,000 recordings free each month.

Mobile replays run on a separate ladder, with the first 2,500 free:

Worked example, 20k web replays: first 5k free, next 10k at $0.0050 ($50.00), remaining 5k at $0.0035 ($17.50). Total: $67.50.

3. Feature flags and experiments (per request)

Feature flags bill per request, with the first 1M requests free. Experiments run on top of feature flags, so they draw from the same request quota rather than a separate meter.

Worked example, 3M requests: first 1M free, next 1M at $0.000100 ($100.00), remaining 1M at $0.000045 ($45.00). Total: $145.00.

4. Surveys (per response)

Surveys bill per response, with the first 1,500 responses free each month.

Worked example, 2,500 responses: first 1,500 free, next 500 at $0.100 ($50.00), remaining 500 at $0.035 ($17.50). Total: $67.50.

Managed Warehouse (per row synced)

The data warehouse, now called Managed Warehouse and running on DuckDB, bills per row synced, with the first 1M rows free.

Worked example, 8M rows synced: first 1M free, remaining 7M at $0.000015. Total: $105.00.

Error tracking (per exception)

Error tracking bills per exception, with the first 100,000 exceptions free.

Worked example, 200k exceptions: first 100k free, remaining 100k at $0.000370. Total: $37.00.

The newer meters, AI, logs, and workflows

PostHog added several products in the last year, and each carries its own free quota and rate. These matter if you run AI features or route data and messages through PostHog:

• PostHog AI (the natural-language data copilot): 500 credits free, then $0.01 per credit.

• AI Observability / LLM analytics (usage, latency, and per-user cost for AI features): 100,000 events free, then per event on a step-down ladder that starts at $0.000350 and drops to $0.000060 at very high volume.

• Logs (OpenTelemetry-compatible log ingestion): 10 GB free, then billed per GB (around $0.25/GB, dropping at higher volume).

• Workflows (no-code automation and messaging): 10,000 messages per channel free, then per email or destination dispatch, with emails from about $0.003 and dispatches from about $0.00075, both stepping down at volume.

• Data Pipelines (real-time destinations and batch exports): 10,000 trigger events plus 1M rows free, then per trigger event or row on their own ladders.

For most teams, the largest cost drivers are analytics events and session replays. Surveys, feature flags, warehouse rows, and the newer meters usually stay manageable unless a workflow pushes them hard.

Feature gating and bundling logic

PostHog's pricing avoids charging teams twice for interconnected features. Instead of billing every action separately, it bundles related products and applies relationship logic so dependent usage doesn't double up on your invoice.

No double-charging for dependent usage

• When one feature triggers another under the hood, PostHog bills you once. 

• A feature flag evaluation might also generate an analytics event, but in PostHog's model you pay only for the flag request, not the associated event. 

• Without that rule, teams would pay twice for the same user interaction, once as a request and again as an event, which is common on platforms that meter everything separately with no relationship logic.

Experiments bundled under feature flags

• Experiments run on feature flags, so PostHog has no separate experiments meter. 

• Experiment usage counts entirely against your feature flag request quota. 

• Run an experiment that evaluates 500k users, and those requests consume 500k from your feature flag allowance. 

• You won't see a separate experiments line item on your bill.

Impact on cost control

• Bundling and no-double-charge rules make costs easier to forecast. 

• You monitor a single meter for certain workflows, such as flags plus experiments, and you can model the effect of launching a new experiment by estimating the extra flag requests it generates, with no hidden secondary charges. 

• Run three concurrent experiments against the same user cohort, and those evaluations still show up only as feature flag requests, never as duplicate analytics events.

Platform add-ons and data retention

Beyond metered product usage, PostHog sells platform packages and enforces data retention windows that vary by plan. Both can affect your bill even when your core usage holds steady.

Platform packages (monthly cost)

Platform packages are optional upgrades that add projects, security, and support controls across your whole account, not usage or ingestion speed. Here's what each platform package covers:

Enterprise used to carry a $2,000 flat price, but PostHog now lists it as custom pricing, so you talk to their team for a quote. These packages sit on top of your usage bill and don't change your per-product rate calculations.

Data retention by product (Cloud)

• Retention windows depend on your plan and product. On paid plans, PostHog keeps analytics events and user data for 7 years, session replay recordings for 3 months, and error tracking data on the same 7-year window as events. 

• On the Free plan, analytics events and user data drop to 1 year, with session replay still at 3 months. You can extend session replay retention to 12 months through the Boost package.

• On self-hosted ClickHouse instances, session replay retention defaults to 1 month unless you extend it. 

• Older data may move to slower cold storage, which can turn historical queries from milliseconds into seconds for high-volume teams.

Why retention matters for cost and performance

• Retention doesn't add line-item charges, but it shapes your infrastructure footprint and query speed. 

• Longer retention means more historical data to query, which can slow analytics without extra compute. 

• If you need more than the default window, say for compliance, you may need an enterprise contract. A compliance-heavy fintech that requires 10-year event storage would push past PostHog's 7-year paid window into a custom agreement with a higher monthly commitment.

Controlling and forecasting your bill

• PostHog's usage-based model gives you control over what you pay, but only if you manage it actively. 

• Without monitoring, it's easy to overshoot quotas on high-volume products like analytics and session replay. Five habits keep the bill predictable.

Set per-product billing caps

• Every product lets you define a hard monthly spend limit, and PostHog stops processing that product's usage once you hit it. 

• Set a $200 cap on session replay, reach it by day 20, and recordings stop while analytics and everything else continue. 

• This is the single most effective guardrail against surprise bills.

Filter and sample before ingestion

• You can filter events, target session replays, and restrict surveys before they're ingested, which keeps you from paying for noise. 

• Capture replays only from error-prone flows or specific URLs, and drop low-value events like minor UI interactions. 

• Filtering at ingestion is a core best practice for usage metering, because every event you don't ingest is an event you don't pay for.

Forecast usage with tier ladders

• Because the tiers are fixed, this month's numbers predict next month's bill. 

• If you're at 9M analytics events and expect 20% growth, you're looking at about 10.8M events: first 1M free, next 1M at $0.0000500 ($50.00), remaining 8.8M at $0.0000343 ($301.84). 

• Forecasted total: about $351.84. Run the same math per product to see where growth will bite.

Review usage patterns weekly

• Weekly checks catch anomalies before the end-of-month bill does. 

• A sudden replay spike or an unexpected jump in flag requests shows up days earlier when you look often, which gives you time to add a cap or a filter before the charge lands.

Revisit retention needs

• If you don't need 7-year analytics retention, store data externally after a shorter window to keep queries fast and storage lean. 

• This matters most on self-hosted setups, where you own the storage and compute directly.

• Controlling spend in PostHog isn't about using fewer features. It's about measuring what you truly need, then applying the right caps, filters, and sampling before the meter runs.

How can you replicate PostHog's pricing with Flexprice

PostHog's pricing model is genuinely one of the more complex ones in the SaaS world. Multiple independent meters, step-down volume tiers, free quotas per product, bundling rules that prevent double-charging, per-product spend caps, and platform-wide add-ons all running in parallel. Rebuilding something like this from scratch on a generic billing system would take weeks of engineering time and constant maintenance.

Flexprice handles this class of pricing natively. Through its MCP server, you describe your pricing model in plain language, and Flexprice translates that into a working billing configuration without touching infrastructure code. Want five independent usage meters with different unit types and step-down tiers? That's a prompt. Want a free quota that resets monthly before any charges kick in? That's a prompt. Want to bundle two products so one meter feeds into the other without creating a separate line item? Also a prompt.

The PostHog model specifically maps well to what Flexprice supports out of the box. Each product (Analytics, Session Replay, Feature Flags, and so on) becomes its own usage-based meter with its own tier ladder. The bundling logic between Experiments and Feature Flags, where experiment evaluations simply consume from the flag request quota rather than creating a new meter, is expressible as a relationship between meters rather than custom code. Spend caps per product, the kind that cut off a single module at a threshold without affecting others, are a native configuration on each meter rather than a workaround.

What Flexprice adds beyond just mirroring the structure is the operational layer that makes this pricing actually run at scale. Real-time event ingestion handles the volume that products like Analytics and Feature Flags generate. Credit wallets cover prepaid or quota-based scenarios. Multi-currency invoicing and Stripe or Razorpay integration handle collection. None of that needs to be wired together separately.

The broader point is that PostHog's pricing is not a special case; it is just a combination of patterns that appear across AI-native tools, observability platforms, infrastructure products, and developer tooling. Flexprice's MCP server lets teams describe any such combination in prompts and get a production-ready billing model out the other side, without starting from a rigid template or rebuilding logic every time the pricing changes.

Making the right call on PostHog’s pricing

PostHog’s pricing is designed to scale with you—but it’s not “set and forget.” The free quotas are generous enough for early-stage teams to run multiple modules at no cost, while the usage-based tiers reward higher volumes with step-down rates. This keeps it competitive against seat-based tools, especially for engineering-heavy teams that grow quickly in headcount.

The biggest drivers of your bill will almost always be Analytics events and Session Replays. These should be the first areas you monitor, cap, and optimize. Smaller meters like Surveys, Feature Flags, and Data Warehouse rows often stay well within budget unless they’re tied to high-frequency workflows.

If you want predictability:

• Cap spend per product to avoid surprise bills.

• Filter aggressively at ingestion so you’re only paying for meaningful data.

• Forecast with tier ladders so you know exactly how growth will affect costs.