• Forged in Blockchain. Built for Everything.

Store event data once.
Stream, query, and replay it from one source of truth.

Continuum collapses the streaming pipe, the data lake, and the replay engine into one system — with mutable-history corrections as a first-class primitive.

See Pricing

• Write

Unified storage


producer.send(
  "events",
  data
)

• Query

Query-ready


consumer.replay(
  from: "Jan 1",
  to: "Mar 31"
)

• Replay

Replayable data


SELECT *
FROM iceberg
WHERE date
  >= "2026"

One source of truth. Three access patterns.

Up to 100x Compression (Parquet, columnar)
40-60% Cheaper than Kafka alternatives
Iceberg-native Lakehouse
Built-in Correction & reorg handling

One system replaces four

Stop duplicating data across Kafka, queues, warehouses, and replay pipelines. Continuum unifies the stack.

With Continuum

Continuum architecture diagram showing one system connecting stream ingestion, Iceberg lakehouse, query engines, and replay from one source of truth — One system. One copy of the data. Many ways to access it.

Today’s stack

Queue (Rabbit, SQS)

Data warehouse (Snowflake, Databricks)

Replay / export pipelines

Four systems. Three pipelines. Data duplicated at every hop.

Why we built Continuum

Kafka wasn't designed for blockchain. Neither was anything else.

No solution handled reorganisations natively. Compression ratios were terrible for structured on-chain data. Retaining full chain history from block zero was either ruinously expensive or simply not supported. And the throughput demands of ingesting every block, every transaction, across every chain — simultaneously and in order — pushed existing tools past their limits.

We needed all of these things — so we built a platform that handles them from the ground up.

Mutable history, first-class

Chain reorgs, restatements, late-arriving corrections, retroactive patches — all handled at the storage layer as first-class primitives. Every other streaming platform is append-only. Continuum rewrites history without rebuilding pipelines. Implement two functions: `handleEvents` and `cleanUpEvents`. Everything else is automatic.

Multi-protocol access, Iceberg-native lakehouse

Write via Kafka, REST, AMQP, or SQS — whatever your producers already speak. Consume as streams, queues, or SQL tables via the Iceberg catalog — queryable directly from Snowflake, Trino, Databricks, Spark, and Athena. One source of truth, many access patterns.

Diskless, S3-native architecture

Body copy stays the same (stateless producers, no broker layer, no replication tax).

Columnar compression

Parquet format, not log segments. 3-5x more space-efficient than any Kafka-compatible system. 17x on blockchain data; structured or repetitive data typically achieves 30-100x.

Infinite retention, battle-tested

S3 is the source of truth — 11 nines of durability. Full replay from any point in history. Retention costs scale with compression, not replication. Running in production for 3+ years across 50+ chains, 11.9 PB, and 2B+ events per month.

Architecture

S3-native from the first byte

No brokers. No disks. No replication tax.
Producers write directly to S3 through stateless pods.

S3-native data streaming architecture showing producers writing directly to S3 via Continuum with consumer SDK and database integration

Corrections are first-class

Write directly to S3. No broker layer, no replication. Scale horizontally with zero coordination overhead.

Columnar Storage

Parquet format, columnar from the write path. 17-20x on blockchain event data, 30-100x on structured time-series. 3-5x advantage over Kafka-compatible systems in the general case.

Consumer SDK

`Multi-protocol reads: streams, queues, or SQL via Iceberg. Consume as JSON, Arrow, or Parquet. Bridge to Kafka, RabbitMQ, or SQS. Query directly from Snowflake, Trino, Databricks, or Spark — no CDC pipeline required.

How Continuum compares

Continuum was designed from scratch with no legacy compatibility constraints. Here's how it stacks up.

Comparison of Continuum, Kafka, WarpStream, and AutoMQ across architecture, storage, compression, replay, deployment, and cost.
Feature
Architecture	S3-native, no brokers	Broker cluster + EBS/tiered	S3-native, agents + brokers	S3-native, Kafka-compatible
Storage format	Columnar Parquet	Log segments	Log segments (Kafka format)	Log segments (Kafka format)
Compression	17-100x (Parquet)	3-5×	3-5×	3-5×
Replication	S3 durability (none needed)	RF=3 (3× storage + network)	S3 durability (none needed)	S3 durability (none needed)
Correction handling (reorgs)	Built-in
Full replay	From any point in history	Retention-limited	S3-backed	S3-backed
Protocol bridging	Kafka, RabbitMQ, SQS	Kafka only	Kafka only	Kafka only
Deployment	Managed or self-hosted	Self-managed or managed (MSK, Confluent)	BYOC (their control plane)	BYOC (their control plane)
Cost @ 200 MiB/s, 90d	$15.3K/mo	$33K–$235K/mo	$28K/mo	$25.5K/mo
Queryable from SQL engines (Iceberg)	Native	Requires CDC / ETL pipeline	Requires CDC / ETL pipeline	Requires CDC / ETL pipeline

Cost Analysis

Infrastructure cost at scale

200 MiB/s sustained throughput, 90-day retention. Same workload, different architectures.

Cheaper than Kafka alternatives

200 MiB/s uncompressed, 90-day retention, 3 AZs, us-east-1 pricing.

Continuum retention is infinite at this price — 90 days shown for comparison.

Cost Analysis

Calculate your cost

Usage-based pricing. All infrastructure included. No separate AWS bill.

Write throughput10 MiB/s

Retention period

Compare against

EBS, self-managed

Tiered Storage

BYOC

Competitor assumptions

Self-managed on AWS (us-east-1)
3 AZs, RF=3 replication
4:1 producer compression (snappy/lz4)
EBS gp3 @ $0.08/GB, 40% utilization target
m5.4xlarge brokers, 33% CPU headroom

Your Continuum Cost

$2,879

per month

Kafka — EBS, self-managed $2,846

You save Comparable

Cost breakdown

Platform fee $2,499

Write (ingestion) $304

Storage $35

Read / egress $41

Production-proven in blockchain.
Proving ground for every industry with mutable event data.

In production today

Blockchain Infrastructure

Write directly to S3. No broker layer, no replication.Scale horizontally with zero coordination overhead.

Onboarding design partners

AI & ML data infrastructure

Sensor telemetry, feature recomputation, LLM inference logs, agent traces. Late labels and mutable-history corrections handled at the storage layer — no pipeline rebuilds. Replay for retraining from any point in time.
Financial data pipelines

Corrections and restatements handled natively. No more “oops” events.Full audit trail with replay.
IoT & telemetry

Edge-to-cloud, long-retention sensor data. Columnar compression achieves 30-100x on structured time-series — storage costs drop by an order of magnitude.
Event sourcing

Immutable event log with full replay. S3 durability (11 nines). No broker state to lose. No application rewrites needed.
CDC & replication

Bridge to and from Kafka, RabbitMQ, and SQS. Migrate incrementally — no rip-and-replace required.

Production-proven at scale

Powering Moralis infrastructure: 50+ blockchain networks,
2B+ events/month, 11.9 PB retained.

Enterprise Security

S3 encryption at rest. TLS in transit. Data isolation per customer. SOC 2 compliant infrastructure.

Self-Hosted Option

Run Continuum on your infrastructure, your cloud, your terms. No control plane dependency. Full data sovereignty.

Enterprise Support

Dedicated support engineers. SLA guarantees. Architecture reviews and migration planning included.

Battle-Tested Reliability

Built to handle chain reorganisations, network forks, and sustained high throughput — 24/7, without intervention.

Ready to transform your data infrastructure?

Join teams already using Continuum for event streaming at scale with native correction handling