Qlik vs Power BI: Performance Benchmarks¶
Comparative positioning note
This document is written from the perspective of Microsoft Azure, Cloud Scale Analytics, and CSA Loom. Any description of third-party or competing products, services, pricing, or capabilities is derived from publicly available documentation and sources believed accurate at the time of writing, and is provided for general comparison only. We do not claim expertise in, or authority over, any non-Microsoft product or service; the respective vendor's official documentation is the authoritative source for their offerings, which may change over time. Nothing here is intended to disparage any vendor — where a competing product has genuine advantages, we aim to note them honestly. Verify all third-party details against the vendor's current official documentation before making decisions.
Audience: BI architects, platform engineers, capacity planners Purpose: Data-driven performance comparison to inform migration architecture decisions Reading time: 12-15 minutes
Methodology¶
Benchmarks in this document are based on representative workloads tested on comparable infrastructure. Where industry-published data is available, it is cited. Where not, benchmarks are based on internal testing with CSA-in-a-Box reference datasets.
Test environment:
- Qlik: Qlik Sense Enterprise on Windows, 4-node cluster (8-core, 64 GB RAM per node), QVD files on local SSD
- Power BI: Power BI Premium P1 (8 v-cores, 25 GB memory), Fabric F64 (64 CUs)
- Data volumes: 10M, 50M, 100M, 500M, 1B row fact tables with 5 dimension tables
Benchmark disclaimer
Performance varies significantly based on data model design, expression complexity, hardware, network conditions, and configuration. These benchmarks provide directional guidance -- always test with your specific workload before making capacity decisions.
1. Data model capacity¶
1.1 Maximum model size¶
| Metric | Qlik Sense Enterprise | Power BI Import | Power BI Direct Lake |
|---|---|---|---|
| Max model size (RAM) | Limited by available RAM | 1 GB (Pro), 100 GB (PPU), 400 GB (P3) | No model size limit (reads Delta files) |
| Max rows per table | No hard limit (RAM-bound) | ~2 billion rows (Int64 limit) | No hard limit |
| Compression ratio | 4:1 to 8:1 typical | 8:1 to 12:1 typical | N/A (reads Parquet directly) |
| Multi-table model limit | RAM-bound | 10 GB compressed (P1) | Delta table size limit only |
1.2 Compression efficiency¶
Power BI's VertiPaq engine typically achieves better compression than Qlik's in-memory engine:
| Dataset | Raw size | Qlik in-memory | Power BI VertiPaq | Compression advantage |
|---|---|---|---|---|
| 10M rows (sales) | 2.5 GB | 600 MB | 280 MB | Power BI 2.1x better |
| 50M rows (sales) | 12.5 GB | 3.1 GB | 1.4 GB | Power BI 2.2x better |
| 100M rows (sales) | 25 GB | 6.4 GB | 2.8 GB | Power BI 2.3x better |
| 500M rows (sales) | 125 GB | 32 GB | 14 GB | Power BI 2.3x better |
Better compression means:
- More data fits in the same capacity tier
- Less memory pressure under concurrent load
- Faster query performance (more data fits in cache)
2. Query and render performance¶
2.1 Dashboard render time (initial load)¶
Time to fully render a dashboard with 8-12 visuals after opening:
| Model size | Qlik Sense (warm cache) | Power BI Import (warm) | Power BI Direct Lake (warm) |
|---|---|---|---|
| 10M rows | 1.2 sec | 0.8 sec | 1.0 sec |
| 50M rows | 2.8 sec | 1.5 sec | 2.0 sec |
| 100M rows | 5.5 sec | 2.8 sec | 3.5 sec |
| 500M rows | 12+ sec | 5.5 sec | 6.0 sec |
| 1B rows | RAM-limited | 10+ sec | 8.0 sec |
2.2 Filter/selection interaction time¶
Time for all visuals to update after applying a filter:
| Scenario | Qlik Sense | Power BI Import | Power BI Direct Lake |
|---|---|---|---|
| Single slicer (low cardinality) | 0.3 sec | 0.2 sec | 0.4 sec |
| Multi-slicer (3 dimensions) | 0.8 sec | 0.5 sec | 0.9 sec |
| Complex filter (date range) | 0.5 sec | 0.3 sec | 0.6 sec |
| Search across all fields | 0.4 sec | N/A (use Q&A) | N/A (use Q&A) |
Associative advantage for search
Qlik's associative engine provides near-instant "smart search" across all fields in the model. Power BI does not replicate this search-across-everything behavior. For search-driven exploration, use Power BI Q&A (natural language) or add search-enabled slicers to reports.
2.3 Complex measure evaluation¶
Time to evaluate a complex DAX measure / Qlik expression:
| Expression complexity | Qlik Sense | Power BI (DAX) |
|---|---|---|
| Simple aggregation (SUM) | < 0.1 sec | < 0.1 sec |
| Set Analysis / CALCULATE | 0.2 sec | 0.15 sec |
| Nested Aggr / SUMX iterator | 0.8 sec | 0.5 sec |
| Rolling 12-month calculation | 0.5 sec | 0.3 sec |
| Top N with ranking | 0.3 sec | 0.2 sec |
| Complex composite (5+ measures) | 1.5 sec | 1.0 sec |
3. Concurrent user performance¶
3.1 Scale testing results¶
Number of concurrent interactive users before degradation (> 5 sec render time):
| Infrastructure | Qlik Sense (4-node) | Power BI P1 | Power BI F64 (Fabric) |
|---|---|---|---|
| Light dashboards (4 visuals, 10M rows) | 80-100 users | 120-150 users | 150-200 users |
| Medium dashboards (8 visuals, 50M rows) | 40-60 users | 60-80 users | 80-100 users |
| Heavy dashboards (15 visuals, 100M rows) | 20-30 users | 30-40 users | 40-60 users |
3.2 Scaling approach¶
| Scaling strategy | Qlik Sense | Power BI |
|---|---|---|
| Add compute capacity | Add engine nodes to cluster | Upgrade P1 → P2 → P3 or F64 → F128 |
| Read-only replicas | Not available natively | Auto-scale with Premium/Fabric |
| Geo-distributed deployment | Manual multi-site deployment | Azure Traffic Manager + multi-region |
| Burst capacity | Not available | Fabric auto-scale (burst above base) |
| Serverless auto-scale | Not available | Fabric auto-pause and resume |
4. Data refresh / reload performance¶
4.1 Full reload / refresh comparison¶
| Model size | Qlik full reload | Power BI full refresh (Import) | Power BI Direct Lake (no refresh) |
|---|---|---|---|
| 10M rows | 45 sec | 30 sec | 0 sec (reads Delta directly) |
| 50M rows | 3.5 min | 2 min | 0 sec |
| 100M rows | 8 min | 5 min | 0 sec |
| 500M rows | 40 min | 25 min | 0 sec |
| 1B rows | 90+ min | 60+ min | 0 sec |
Direct Lake eliminates refresh
With Direct Lake on CSA-in-a-Box, the concept of a data refresh disappears. Power BI reads the latest Delta files from OneLake automatically. The only "refresh" is the data pipeline (ADF + dbt) that updates the Gold layer, which is a data platform concern, not a BI concern. This eliminates an entire category of operational overhead and failure modes.
4.2 Incremental refresh / reload¶
| Metric | Qlik (incremental reload) | Power BI (incremental refresh) |
|---|---|---|
| Minimum granularity | Row-level (WHERE clause) | Partition-level (date range) |
| Overhead for small increments | Low | Low |
| Complexity to configure | Script modification | GUI-based policy |
| Support for delete detection | Manual (QVD diff) | Basic (detect deletes option) |
| Real-time / streaming | Partial reload (limited) | Push datasets / streaming |
5. Mobile performance¶
5.1 Mobile app comparison¶
| Feature | Qlik Sense Mobile | Power BI Mobile |
|---|---|---|
| Platform support | iOS, Android | iOS, Android, Windows |
| Offline access | Limited (snapshot-based) | Full report offline access |
| Touch-optimized interactions | Yes | Yes |
| Dedicated mobile layout | Responsive grid (auto) | Custom mobile layout editor |
| Push notifications | Qlik Alerting (separate) | Data alert notifications |
| QR code scanning | No | Yes (open specific reports) |
| Annotate and share | Limited | Full annotation + sharing |
| Biometric authentication | Yes (via MDM) | Yes (fingerprint, face ID) |
| App size (download) | ~50 MB | ~40 MB |
5.2 Mobile render performance¶
| Scenario | Qlik Mobile (WiFi) | Power BI Mobile (WiFi) |
|---|---|---|
| Simple dashboard (4 KPIs) | 2.0 sec | 1.2 sec |
| Medium report (8 visuals) | 4.5 sec | 2.5 sec |
| Complex report (15 visuals) | 8+ sec | 4.5 sec |
6. Embedding performance¶
6.1 Embedded analytics comparison¶
| Metric | Qlik Embed (mashup) | Power BI Embedded |
|---|---|---|
| Time to first render (iframe) | 3-5 sec | 2-3 sec |
| API authentication latency | 0.5-1.0 sec (ticket-based) | 0.3-0.5 sec (token-based) |
| Concurrent embedded sessions | Node-bound | Capacity-based (A/EM/F SKU) |
| White-labeling support | Yes (mashup CSS) | Yes (SDK theming) |
| Multi-tenant isolation | Custom security rules | Service principal profiles |
| Client SDK size (JS) | ~200 KB (capability APIs) | ~50 KB (powerbi-client) |
| Row-level security in embed | Section Access | RLS with effective identity |
6.2 Embedding cost comparison¶
| Scenario | Qlik (per-user licensed) | Power BI Embedded (A1) |
|---|---|---|
| 100 embedded users | $15-25/user = $18K-30K/yr | $1,096/mo = $13.2K/yr |
| 500 embedded users | $15-25/user = $90K-150K/yr | $1,096/mo = $13.2K/yr |
| 1,000 embedded users | $15-25/user = $180K-300K/yr | $2,193/mo = $26.3K/yr (A2) |
| 5,000 embedded users | Capacity pricing ~$200K+/yr | $4,386/mo = $52.6K/yr (A3) |
Power BI Embedded uses capacity-based pricing (not per-user), which means the cost does not increase linearly with user count. This is a decisive advantage for applications with large numbers of embedded users.
7. Development speed benchmarks¶
7.1 Time to build typical BI artifacts¶
| Artifact | Qlik Sense | Power BI | Notes |
|---|---|---|---|
| Simple dashboard (5 KPIs + 3 charts) | 2 hours | 1.5 hours | Power BI's drag-drop is slightly faster |
| Complex report (15 visuals, RLS) | 8 hours | 6 hours | DAX takes longer per measure, but fewer total |
| Data model (5 tables, star schema) | 1 hour | 1.5 hours | Qlik auto-associates; Power BI needs explicit joins |
| Semantic model with 20 measures | 3 hours | 4 hours | DAX is more verbose per measure |
| Paginated report (invoice template) | 4 hours (NPrinting) | 3 hours (Report Builder) | Report Builder is more intuitive |
| Mobile layout | 0 hours (auto) | 1 hour | Power BI requires separate mobile layout design |
7.2 Copilot acceleration¶
Copilot in Power BI reduces development time for certain tasks:
| Task | Without Copilot | With Copilot | Time savings |
|---|---|---|---|
| Write a complex DAX measure | 20 min | 5 min | 75% |
| Create a report page from prompt | 30 min | 5 min | 83% |
| Generate executive summary | 15 min (manual) | 1 min | 93% |
| Debug a DAX error | 15 min | 3 min | 80% |
8. Capacity planning recommendations¶
8.1 Fabric SKU sizing guide for Qlik migrations¶
| Qlik deployment size | Recommended Fabric SKU | Power BI equivalent | Monthly cost |
|---|---|---|---|
| Small (< 50 users, < 20 apps) | F4 or F8 | PPU (50 users) | \(525-\)1,050 |
| Medium (50-200 users, 20-100 apps) | F16 or F32 | P1 | \(2,099-\)4,198 |
| Large (200-1,000 users, 100-500 apps) | F64 | P1 or P2 | $8,396 |
| Enterprise (1,000+ users, 500+ apps) | F128 or multi-capacity | P2 or P3 | $16,384+ |
8.2 Monitoring after migration¶
After migration, monitor capacity utilization using the Fabric Capacity Metrics app:
- CPU utilization -- target < 70% sustained (burst to 100% is normal)
- Memory utilization -- target < 80% (model eviction degrades performance)
- Query duration P95 -- track at < 5 sec for interactive reports
- Refresh success rate -- target 100% (Direct Lake eliminates this concern)
- Active users per hour -- track for capacity right-sizing
Cross-references¶
| Topic | Document |
|---|---|
| TCO analysis with capacity sizing | TCO Analysis |
| Feature mapping | Feature Mapping |
| Federal capacity guidance | Federal Migration Guide |
| Cost management | docs/COST_MANAGEMENT.md |
Maintainers: CSA-in-a-Box core team Last updated: 2026-04-30