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Real-Time Estimator // v4.1.0 // Live

50 Billion Events.
One millisecond.

Configure your workload below. Watch Pulse recalculate your infrastructure reality in real time.

EstimatorDrag to configure
5.0B eps
10K eps50B eps
Multi-Window Join
Simple AggGraph Traversal
50ms
1ms500ms
OutputRecalculating live

p99 Latency

5.5ms

At configured load

Infra Cost

$112.32/hr

Headroom

56%

Nodes

26

RAM

541GB

Now imagine it 40% faster.

BenchmarksIndependent test · 2026-01-14 · AWS r7g.16xlarge

Head-to-Head Latency

All systems benchmarked at 10B events/second with identical multi-window join queries on c6i.32xlarge clusters. Methodology available on request.

System
p99 Latency (lower is better)
p99
Pulse
0.8ms p99
0.8ms
Kafka Streams
12.4ms
Apache Flink
18.7ms
Spark Structured
47.2ms
Redpanda + KSQL
9.1ms
⚡

Pulse achieves 15.5× lower p99 latency than Kafka Streams and 23.4× lower than Apache Flink at equivalent throughput. Full methodology and raw data available in the published benchmark report.

Run a Full Benchmark on Your Stack

Send us your current infrastructure. We'll run Pulse against it on identical hardware and publish the results — win or lose.

Use Cases

Three industries. One engine.

Real deployments with before/after throughput numbers. No synthetic benchmarks.

Financial trading screens showing real-time market data and charts in a dark trading room
Quant Finance

Signal stress-testing before market open

Hedge fund managers run 50,000 scenario simulations in the 30 minutes before the NYSE bell rings.

Before: Kafka Streams

47min / run

After: Pulse

2.8min / run

"We went from running 3 scenarios to 127 before open. Alpha decay is now a choice, not a constraint."

Marcus ThielHead of Quant Research, Meridian Capital

Pulse ingests tick data from 14 exchanges simultaneously, applies 200+ factor models in parallel, and outputs ranked signal strength with p99 < 1.2ms. Pre-market stress tests that took 47 minutes now complete in under 3 minutes — enough time to act on what you find.

Engineer monitoring multiple screens with graphs and real-time data dashboards in a dimly lit server room
AdTech

Bid-response latency debugging at 2 a.m.

AdTech engineers diagnosing why p99 bid responses spiked from 8ms to 340ms on a Tuesday night.

Before: ELK Stack

340ms spike

After: Pulse

Isolated in 4s

"The root cause was a single misconfigured DSP timeout. Pulse showed us the exact request chain in 4 seconds flat."

Priya KrishnamurthyPrincipal Engineer, BidStream Labs

Pulse correlates bid request logs, auction outcomes, and DSP response times across 12 billion daily events. When latency spikes, the anomaly detection engine pinpoints the offending request chain within seconds — not hours of log digging.

Circuit board close-up with glowing electronic components representing IoT sensor infrastructure
Industrial IoT

Watching 10,000 sensor feeds for the one anomaly

IoT platform leads monitoring turbine sensor arrays across 47 wind farms for the vibration signature that precedes catastrophic failure.

Before: Spark Streaming

22s detection lag

After: Pulse

0.6s detection

"We caught a bearing failure signature 18 minutes before it would have taken down a 5MW turbine. That's $2.3M of prevented downtime in one alert."

Lena OkaforIoT Platform Director, NordWind Energy

Pulse processes 10,000+ concurrent sensor streams with sub-second windowed anomaly detection. Vibration, temperature, and RPM data are correlated in real time against 3 years of failure pattern training data. Mean time to detection dropped from 22 seconds to 0.6 seconds.

Not ready to talk? Take the data.

Download our 47-page latency whitepaper. Raw benchmark data, methodology, and reproducibility instructions included.

Architecture

How it's actually built.

No abstraction layers. No JVM. No GC pauses. Expand each card for full technical specs.

Architecture

Zero-copy streaming topology

Pulse's ingest layer accepts events from any source protocol — Kafka, Kinesis, Pulsar, gRPC, WebSocket — without serialization overhead. Data flows through the processing DAG without ever touching disk.

KafkaKinesisIngest LayerStream ProcessorML InferenceState StoreAlertsDashboardAPI
Serialization overhead0 bytes copied
Ingest protocolsKafka · Kinesis · Pulsar · gRPC
State backendCustom LSMT (RocksDB fork)
Serialization overhead0 bytes copied
Ingest protocolsKafka · Kinesis · Pulsar · gRPC
Compute Model

NUMA-aware thread scheduling

Each processing core is pinned to a NUMA node, eliminating cross-socket memory access. Query operators are compiled to native code at plan time via LLVM, not interpreted at runtime.

Thread modelNUMA-pinned, zero context switch
Query compilationLLVM JIT · < 2ms plan time
Memory modelOff-heap · no GC pauses
Thread modelNUMA-pinned, zero context switch
Query compilationLLVM JIT · < 2ms plan time
Resilience

Sub-millisecond failover

State is continuously replicated to a hot standby via our custom WAL protocol. If a processing node fails, the standby takes over in under 800μs — imperceptible to downstream consumers.

Failover time< 800μs (p99)
Replication lag< 50μs
DurabilityZero data loss · exactly-once
Failover time< 800μs (p99)
Replication lag< 50μs
Scaling

Elastic sharding without repartition storms

Pulse uses consistent hashing with virtual nodes to redistribute load when cluster size changes. Adding a node rebalances 1/N of shards, not all of them. No backpressure, no latency spike.

Scale-out latency impact< 3% p99 increase
Rebalance scope1/N shards only
Max cluster size tested4,096 nodes
Scale-out latency impact< 3% p99 increase
Rebalance scope1/N shards only

Run a Full Benchmark on Your Stack

Send us your current infrastructure. We'll run Pulse against it on identical hardware and publish the results — win or lose.

Trusted By

Meridian Capital

Quant Fund

BidStream Labs

AdTech

NordWind Energy

Industrial IoT

Apex Clearing

Financial Infra

DataBridge AI

ML Platform

ClearSky Markets

Crypto Exchange

Research

Peer-reviewed. Reproducible.

Every benchmark claim is backed by published research. Download raw data, reproduce results, challenge our numbers.

VLDB 2025PDF

Zero-Copy Stream Processing at Planetary Scale

Proceedings of the VLDB Endowment · 2025

Chen, W. · Nakamura, R. · Osei, K. · Patel, S.

We present a novel approach to stream processing that eliminates serialization overhead entirely through a shared memory architecture with hardware-enforced isolation. At 50B eps, p99 latency remains below 1.1ms.

Citations

247

Reproduced by

14 labs

SIGMOD 2025PDF

NUMA-Aware Query Compilation for Streaming Workloads

ACM SIGMOD International Conference · 2025

Müller, T. · Singh, A. · Adeyemi, F.

LLVM-based JIT compilation of streaming query plans reduces operator overhead by 8.3× compared to interpreted execution. NUMA pinning eliminates 94% of cross-socket memory traffic.

Citations

183

Artifact award

ACM Distinguished

IEEE Big Data 2024PDF

Sub-Millisecond Failover via Continuous WAL Replication

IEEE International Conference on Big Data · 2024

Larsson, E. · Zhao, Y. · Ibarra, C.

Our custom WAL protocol achieves 800μs failover with zero data loss through speculative execution on hot standbys. Tested across 4,096-node clusters at AWS, GCP, and on-premise.

Citations

129

Best paper

Finalist