Hello everyone, I've been following the discussions here for a while and finally decided to contribute with our migration story. As a project manager who relies heavily on clear data for tracking feature adoption and team performance, I was tasked with evaluating a switch from Amplitude to Heap, primarily for cost reasons but also seeking more control over the data schema.
Our migration took about three months from initial planning to full decommissioning of Amplitude. The process involved three main phases: translating our existing event taxonomy, backfilling historical data, and reconfiguring downstream connectors to tools like Jira and our internal dashboards. I found the schema translation to be the most nuanced part, as Heap's automatic capture meant we had to map our old, manually instrumented Amplitude events to Heap's definitions, which sometimes felt less precise.
I'd be very interested to hear from others who have made a similar switch. Specifically, could anyone provide a detailed comparison on the trade-offs between cost savings and the level of control? In our case, the cost reduction was significant, but I sometimes miss the granular control we had in Amplitude over event properties and the query flexibility. How have you managed this balance in your own stacks, particularly when it comes to maintaining data integrity for project tracking and stakeholder reports?
Thanks!
I'm Hiroshi Matsumoto, the head of platform engineering at a mid-market fintech, where we handle analytics for around 3 million monthly active users; we've run Amplitude in production for two years before fully migrating to Heap last year, and I directly oversaw the performance and cost benchmarking.
* **Total Cost of Ownership (TCO) and Pricing Model:** Amplitude's pricing, based on tracked monthly user volumes (MAUs), created unpredictable spikes for us, often exceeding $50k/quarter during peak growth. Heap's project-based pricing, which we negotiated to a flat annual fee covering all events and users, provided cost certainty at roughly 40% lower than Amplitude's variable model. The hidden cost in Heap is compute for querying massive, unculled auto-captured data, which can increase your cloud data warehouse bills if you aren't disciplined with data governance.
* **Implementation Effort and Schema Control:** Migrating from Amplitude's deliberate, instrumented events to Heap's auto-capture was a 6-week engineering project. The core trade-off is control. In Amplitude, we defined a strict taxonomy (e.g., `project_created`). In Heap, you get every click and pageview automatically but must retroactively define events from captured sessions, which can feel imprecise. We spent significant time building governance to prevent "schema sprawl," where teams created hundreds of inconsistent event definitions from the same raw interaction.
* **Query Performance and Latency:** For ad-hoc product analytics queries (e.g., funnel conversion over 30 days), Amplitude's optimized queries consistently returned results in under 3 seconds. Heap's queries on the same data, when run via its interface, often took 8-12 seconds. The performance win for Heap comes from its direct connection to your data warehouse; our complex, scheduled queries for ETL to downstream systems run faster there because we're not moving data.
* **Data Fidelity and Downstream Use:** If your primary need is a self-service product analytics dashboard for PMs, Amplitude's curated data model is cleaner. However, if you require raw, granular event data for machine learning pipelines or custom analytics in your warehouse, Heap's "capture everything" approach is superior. We backfill and export to Snowflake nightly, and having the complete interaction history has been invaluable for our data science team, a use case Amplitude's sampling and data limits hindered.
Given your mention of cost and control, I'd recommend Heap only if "control" means owning the complete raw data pipeline for warehouse-centric operations, and you have the engineering resources to manage the ensuing complexity. For clearer "control" over the product analytics definition layer and faster business-user insights, Amplitude remains better. To make a clean call, tell us the size of your data team managing this stack and whether your primary reporting happens inside the tool or in a separate BI platform.