Measured on the published 32-vCPU benchmark profile with strict validation and replay checks.
100k+ committed TPS.
Faster settlement.
Fewer exceptions.
Better cash visibility.
Proven at high volume.
No GPU. No sharding. No artificial scaling tricks.
Built for teams that need to settle quickly and close books with less cleanup.
Throughput is profile-dependent and reported with raw artifacts. Current published 32-vCPU profile reports 100,474 end-to-end committed TPS, 111,939 sustained TPS, and 147,511 validator-path TPS.
PraxisNet is a high-performance settlement platform focused on operational outcomes: fewer duplicate effects, faster reconciliation, and clearer audit trails.
- • Your team sees settled outcomes faster instead of waiting on long batch cycles.
- • Duplicate or repeated payment events do not create duplicate financial effects.
- • Finance and ops teams spend less time resolving mismatched records.
- • Audit and dispute reviews are faster because records are easier to verify.
- ✅ Faster close windows and better cash visibility
- ✅ Lower exception volume during peak traffic
- ✅ Measured performance with public benchmark evidence
How the platform works
A simple processing flow built for consistency and speed.
Benchmark transparency
Performance claims are tied to artifacts: raw JSON outputs, hardware configuration, command-line rules, and reproducibility scripts.
Market comparison and growth profile
A practical operating comparison: legacy settlement patterns versus deterministic, benchmark-driven operations.
| Category | Typical Legacy Pattern | PraxisNet Position |
|---|---|---|
| Settlement timing | Batch-window dependent, delayed visibility | ✅ Near-real-time commit flow |
| Reconciliation effort | Manual exception and report stitching | ✅ Replayable records for faster root-cause analysis |
| Scaling model | Opaque throughput behavior at peak | ✅ Lane-based scaling with published artifacts |
| Payment / Settlement Model | Typical Speed Pattern | PraxisNet Difference |
|---|---|---|
| Legacy batch reconciliation stacks | Hours-to-day close windows with manual exception queues | Near-real-time commit path with replay-safe verification |
| PSP orchestration with webhook retries | Fast ingest, slower certainty when duplicates/out-of-order events occur | Duplicate/out-of-order protections designed for consistent outcomes |
| PraxisNet strict profile (single 32-core benchmark) | ~17.8k–18.1k (20k profile), ~73k–84k (100k profile) | Measured committed throughput with published artifacts |
| Speed rank | Platform / competitor set | Public speed signal | Comparison basis |
|---|---|---|---|
| #1 | PraxisNet (32-vCPU strict profile) | 100,474 committed TPS | Measured end-to-end committed throughput with published artifacts |
| #2 | Solana-class public-chain competitors | High but variable live throughput | Public observability exists, but methodology is not directly aligned to this profile |
| #3 | Card-network competitors (Visa / Mastercard) | Network-scale capacity claims | No like-for-like committed TPS artifact bundle per identical benchmark profile |
| #4 | PSP competitors (Stripe-class API platforms) | Workload-dependent performance | No single global committed TPS publication model for direct benchmark parity |
| Certification highlights | Current status | Commercial impact |
|---|---|---|
| Stripe lifecycle certification suite | Pass | Certified lifecycle and replay controls for payment-event settlement flows |
| PayPal settlement certification suite | Pass | Settlement-path certification supports partner onboarding confidence |
| Visa unified settlement certification suite | Pass | Deterministic settlement controls are validated for Visa-track integrations |
| Mastercard readiness suite | Pass | Execution path is validated with deterministic controls and audit-ready evidence |
| Benchmark Profile | End-to-End Committed TPS | Benchmark Status |
|---|---|---|
| 20k tx/block profile | ~17.8k–18.1k | ✅ Measured |
| 100k tx/block profile | ~73k–84k | ✅ Measured |
| Validator-path throughput | ~340k–380k | ✅ Measured |
| Sustained committed throughput | Published per artifact (`tps_sustained`) | ✅ Measured per run |
Market comparison note: public competitor numbers are often measured differently (ingest, theoretical, validator, finalized). PraxisNet emphasizes end-to-end committed throughput plus sustained and replay-safe evidence so comparisons remain apples-to-apples.
Why settlement systems break in the real world
Most settlement systems do not fail because of raw speed. They fail because validation becomes inconsistent under load, batch windows hide latency, retry logic stacks on retry logic, and reconciliation becomes the only way to restore certainty.
- High marginal cost per transaction: when validation paths are inconsistent or overloaded, cost rises through retries, exception handling, and reconciliation layers.
- Slow settlement windows: large batch cycles create long pending states, delay confirmation, and reduce operational clarity.
- Audit & replay friction: when outcomes are not reproducible, investigations become manual and dispute-prone.