MTS Labs

The High-Performance Database
Built for the AI Era

One Rust-native HTAP engine with ACID transactions and vector search — drop-in compatible with Cassandra & DynamoDB.

Rust-Native Elastic Cloud Architecture Zero-ETL Analytics CQL + DynamoDB Compatible AI Vector Search

Confidential — April 2026

www.mtslabs.io

02

Problem & Market Opportunity

Data infrastructure is broken at scale — and AI is making it worse

💸 Runaway Costs

DynamoDB charges per-request. Cassandra requires over-provisioned stateful nodes. Both punish growth.

🧩 Stack Sprawl

Separate systems for transactions, analytics, vector, and workflows — each with its own ops burden and ETL pipeline.

🧠 AI Demands More

RAG and agentic workflows make 10–100× more DB calls than human apps. Legacy throughput ceilings become the AI ceiling.

0
Cassandra users planning AI workloads
Community Survey 2025
0
AI use cases to triple by 2026
Community Survey 2025
0
building RAG apps
Community Survey 2025
0
addressable entry market
Cassandra + DynamoDB migration
Entry Wedge

Start with Cassandra and DynamoDB workloads where migration pain is already urgent and budgets already exist.

Expansion Tailwind

AI-native and HTAP workloads push teams to consolidate operational, analytical, and vector data into one platform.

Sources: Cassandra Community Survey 2025; DB-Engines; a16z.

03

Our Solution

One platform to replace them all

⚡ Drop-In Migration

Full CQL & DynamoDB API compatibility. Existing drivers and queries work day one — zero rewrite.

🏗️ Unified Engine

HTAP + ACID transactions + vector search in one engine. Zero ETL, zero data copies.

🦀 Rust Performance

Purpose-built in Rust. Predictable low-latency at every percentile — no GC pauses, no JVM tuning.

☁️ Instant Scale-Out

Elastic cloud architecture scales in seconds — no data rebalancing, no streaming delays.

🧠 Built-In Vector Search

Hybrid ANN + CQL filters in one query. Real-time freshness, no separate vector DB, no dual-write.

💰 60%+ Cost Reduction

Stateless compute + automatic tiering + serverless model. Pay for use, not peak capacity.

Stack Consolidation

From fragmented data stack to one operating layer

Compatibility lands the workload first. Vector, analytics, and transactions expand the account later.

Today
Cassandra / DynamoDBoperational store
Vector DatabaseRAG and semantic retrieval
OLAP / Warehouseanalytics on copied data
ETL + Dual Writessync pipelines and ops overhead
Adoption
With MTS DB

MTS DB

One cloud database with compatibility at the edge and differentiated capabilities built in.

CQL Compatible DynamoDB Compatible Vector Search ACID Transactions Zero-ETL Analytics Elastic Scale
DimensionCassandra / ScyllaDBDynamoDBVector DBMTS DB
Scale-OutHoursFast opaqueVariesSeconds
ACID TxnsNoLimitedNoFull cross-partition
AnalyticsNoNoNoBuilt-in Zero-ETL
VectorC* 5 basicNoCoreHybrid CQL+vector
Lock-inOpen CQLProprietaryVariesOpen CQL+DDB
04

Performance Proof

Benchmarked and proven

0
CQL benchmark
wins
0
CQL benchmark
ties
0
CQL benchmark
losses
0
point-read p50 lower
vs Cassandra
0
LWT p50 lower
vs ScyllaDB
0
vector bootstrap
throughput vs Cassandra
Relative Performance Advantage
Point Read p50
Cassandra 0.21 msMTS 0.08 ms
LWT Latency
Scylla 21.49 msMTS 0.74 ms
Vector Bootstrap
Cassandra 62 r/sMTS 588 r/s
Why This Matters
Operational workloads land on day one because MTS already beats Cassandra materially and runs at near-parity with the fastest point-read control.
Enterprise use cases unlock because LWT / transactional semantics no longer impose a massive latency tax.
AI workloads stay in one system because vector bootstrap and query performance remove the need for a separate vector tier.

CQL vs Cassandra 4.1 (3-node)

BenchmarkC*MTS
Point read p500.21 ms0.08 ms
Single write p500.18 ms0.10 ms
LWT p502.16 ms0.74 ms
Mixed ops/s4,0347,824
Token scan rows/s170,950357,650

CQL vs ScyllaDB 2025.1 (3-node)

BenchmarkScyllaMTS
Point read p500.09 ms0.08 ms
LWT p5021.49 ms0.74 ms
Concurrent read/s5,30310,781
Async write/s43,24055,365
Large partition rows/s5,2759,694

Vector (15/15 wins)

BenchmarkBaselineMTS
Top-1 qps (pgv)4,9807,038
Top-1 p50 (pgv)0.27 ms0.19 ms
Top-1 p95 (C*)6.03 ms0.53 ms
Bootstrap (C*)62 r/s588 r/s
Bootstrap (pgv)233 r/s774 r/s

CQL: latest sanitized 19-benchmark closeout across 3-node clusters, Apr 2026, with 15 wins, 4 ties, and 0 losses under the agreed tie rule. Vector: 15/15 wins vs pgvector (1-node) and Cassandra (3-node).

05

Go-to-Market & Traction

How we win and grow

💰 Revenue Model

Managed Cloud Primary

Usage-based: storage GB + compute + throughput. Free tier removes friction; reserved capacity for enterprise. Target 80%+ gross margin.

Enterprise License Secondary

Annual subscription for on-prem / VPC. SLA guarantees + premium support. Unlocks regulated verticals.

Expansion Flywheel

Teams land with CQL migration → activate analytics, vector, ACID → 3–5× wallet expansion per account. Revenue compounds with customer data growth.

📍 Where We Are Today

Done

Core engine complete — full CQL + Cassandra compatibility, vector search, driver suites passing

Done

Benchmarks published — 15 wins, 4 ties, 0 losses across 19 CQL workloads, plus 15/15 vector wins vs Cassandra, ScyllaDB, pgvector

Now

DynamoDB compatibility, ACID transactions, analytics OLAP

Now

Design partner pilots — onboarding first customers for managed cloud beta

Next

GA launch on public cloud — multi-tenant, self-serve, SOC 2

06

The Team

MTS Labs — Maximum Throughput Systems

CC

Conrad Chan

Co-Founder

Serial entrepreneur with five successful businesses. Deep operational experience where speed and reliability are mission-critical.

⚙️

Technical Director

Co-Founder

Main author of enterprise database solution supporting 10M+ operations per minute. Systems architecture and distributed coordination. Drives technical quality across the platform.

Platform Engineer

Co-Founder

Main author of enterprise platform solution supporting 10M+ operations per minute. Infrastructure, CI/CD, and production reliability. Keeps the engine running at scale.

Deep Domain Expertise
Distributed systems, storage engines, Rust

Builder DNA
Proven track record shipping & scaling

Operator Empathy
Built from real production pain

07

The Ask

Raising $3M Pre-Seed

Capital to take MTS DB from pilot to GA and establish the data platform for AI-native applications.

$3M
Pre-Seed Round
18–24mo
Runway
GA + First Production Customers
Key Milestone
$10B+ Addressable Entry Market
Cassandra + DynamoDB migration
Expansion Tailwind
AI-native + HTAP workloads consolidate operational, analytical, and vector data into one platform

Use of Funds

50%
Engineering
20%
Go-to-Market
20%
Cloud Infra
10%
Operations

Let's build the database for the AI era — together.

hello@mtslabs.io mtslabs.io

MTS Labs, Inc. • Confidential • April 2026