Cloud Adoption Guide for Financial Market Infrastructure
By Braincuber Team
Published on February 4, 2026
When GlobalTrade Exchange decided to migrate their matching engine to the cloud, the board had one question: "Can we achieve sub-millisecond latency while meeting our regulatory mandates for 5-day autonomous operations during a cloud outage?" The answer, backed by recent migrations from major exchanges worldwide, turned out to be yes—and with performance improvements that exceeded on-premises baselines.
This guide examines the current state of cloud adoption across Financial Market Infrastructure (FMI) providers—exchanges, clearing houses, and central securities depositories. We'll explore the architectural patterns that enable mission-critical trading systems to run in the cloud, the GenAI innovations transforming market surveillance and data analytics, and the practical lessons from institutions that have already made the transition.
Key Trends Covered:
- Trading and matching engine migrations to AWS Outposts
- Clearing and settlement system cloud architectures
- Market data distribution at petabyte scale
- GenAI applications in surveillance and compliance
- Regulatory-compliant disaster recovery patterns
The FMI Cloud Migration Landscape
Financial Market Infrastructure providers face unique challenges that differ from typical enterprise cloud migrations:
Ultra-Low Latency
Matching engines require microsecond-level determinism—far beyond typical enterprise SLAs
Regulatory Mandates
SIFMUs must demonstrate 5-day autonomous operations during complete cloud provider outages
Massive Data Volumes
Processing billions of quotes daily with petabytes of historical tick data for analytics
24/7 Availability
Global markets require continuous operation across time zones with zero tolerance for downtime
Trading Systems Architecture on AWS
The breakthrough for FMI cloud adoption has been AWS Outposts with bare-metal instances, enabling matching engines to run with performance exceeding on-premises deployments:
# Multi-Site Active/Active Trading Architecture
PRIMARY TRADING INFRASTRUCTURE
===============================
AWS Region A (Active) AWS Region B (Active)
| |
+-------- Sync Replication ----+
| |
AWS Outposts AWS Outposts
(bmsfe-2) (bmsfe-2)
| |
Matching Engine Matching Engine
(under 20 microsec) (under 20 microsec)
TERTIARY (DR) ENVIRONMENT
=========================
Disconnected Outposts Rack
(5-Day Autonomous Operations for Regulatory Compliance)
- Static stability: No dependency on AWS control plane
- Local state persistence for full market operations
- Automatic failover with order book reconciliation
Performance Metrics:
- Matching Engine Latency: 2x faster than on-premises at all percentiles
- Median Latency: 6x improvement vs. legacy infrastructure
- Failover Time: Sub-second with zero order loss
Key Architecture Patterns
| Pattern | Description | Regulatory Benefit |
|---|---|---|
| Multi-Region Active/Active | Two active regions with synchronous replication | Zero RPO, sub-second RTO |
| Static Stability | Outposts operate without AWS control plane | 5-day autonomous operations |
| Disconnected Operations | Tertiary site for complete cloud outage | Bank of England mandate compliance |
| Failure Domain Isolation | Blast radius limited per trading venue | Systemic risk mitigation |
Clearing and Settlement Migration
Central Counterparties (CCPs) are migrating critical clearing systems to AWS using Experience-Based Accelerators (EBAs) for structured migration planning:
# EBA-Driven Migration Approach for Clearing Systems
Phase 1: Foundation (3 months)
- Identity governance framework (AWS IAM Identity Center)
- Network architecture with Direct Connect
- Security baseline and compliance controls
- Monitoring and observability setup
Phase 2: Non-Critical Workloads (6 months)
- Reference data systems
- Reporting and analytics
- Development and testing environments
- Validate migration patterns
Phase 3: Mission-Critical Systems (12 months)
- Collateral Management System (CMS)
- Risk calculation engines
- Trade lifecycle processing
- Real-time margin calculation
Phase 4: Core Clearing (18 months)
- Order management systems
- Position keeping
- Settlement processing
- Regulatory reporting
# Key Metrics from Production Migrations:
# - 11 EBAs conducted across 13 cross-functional teams
# - 5 repeatable migration patterns identified
# - 26 process improvements documented
# - 50+ critical blockers removed
# - 12+ applications migrated over 3 years
Key Insight: EBAs involve cross-functional teams identifying blockers early, creating repeatable patterns that accelerate subsequent application migrations.
Market Data Distribution at Scale
Market data providers are leveraging AWS to transform how financial data is stored, processed, and distributed:
# Market Data Platform Architecture
REAL-TIME DATA INGESTION
========================
575 Global Venues --> 5-10 TB/day --> 274+ Billion Messages/Day
Ingestion Services:
- Kinesis Data Streams
- Amazon MSK (Kafka)
- EventBridge (Events)
STORAGE LAYER - Amazon S3 (75+ Petabytes)
==========================================
- Raw PCAP Data (Network Captures)
- Normalized Tick Data (10 Years History)
- Pre-Calculated Analytics (Time Bars, VWAP)
- 30+ Million Instruments
Storage Optimization:
- S3 Intelligent-Tiering: $3.5M/year savings
- S3 Glacier for cold data
- Up to 80% storage cost reduction
ANALYTICS and DISTRIBUTION
==========================
- Athena (Query-in-Place)
- Redshift (Data Warehouse)
- S3 Direct Access (No data transfer, query where stored)
Delivery SLAs:
- Data available within 2 hours of market close
- Large datasets delivered in hours (previously days)
- Time to signal: 30-45 minutes (previously 3 days)
Cost Optimization Results:
- Storage: 97% reduction
- Compute: 82% reduction
GenAI Transformations in Capital Markets
Generative AI and agentic AI are driving rapid innovation across FMI operations:
Market Surveillance and Compliance
# GenAI-Powered Market Surveillance System
import boto3
import json
bedrock = boto3.client('bedrock-runtime')
def analyze_news_sensitivity(news_article: dict) -> dict:
"""
Analyze if news article is price-sensitive for market surveillance.
Achieved: 100% accuracy for non-sensitive, 100% recall for price-sensitive.
"""
prompt = f"""
Analyze the following news article for price sensitivity.
Article:
Headline: {news_article['headline']}
Content: {news_article['content']}
Related Securities: {news_article['tickers']}
Determine:
1. Is this news price-sensitive? (YES/NO)
2. Confidence score (0-100)
3. Justification for the classification
4. Affected securities and expected impact direction
Respond in JSON format.
"""
response = bedrock.invoke_model(
modelId='anthropic.claude-3-sonnet-20240229-v1:0',
body=json.dumps({
'anthropic_version': 'bedrock-2023-05-31',
'max_tokens': 1024,
'messages': [{'role': 'user', 'content': prompt}]
})
)
result = json.loads(response['body'].read())
return json.loads(result['content'][0]['text'])
# Example output:
# {
# "price_sensitive": true,
# "confidence": 95,
# "justification": "M&A announcement directly impacts valuation",
# "affected_securities": ["TICKER1", "TICKER2"],
# "expected_impact": "positive"
# }
GenAI Impact Metrics
| Use Case | Improvement | Business Impact |
|---|---|---|
| Customer Support | 65% queries automated | 5M+ employee hours saved/year |
| Translation Services | 10x faster, 35x cheaper | More events translated to English |
| Code Generation | 40%+ code written by AI | 75,000 employee hours saved |
| Test Documentation | 80% time reduction | Faster deployment cycles |
| Content Curation | Minutes vs. hours | Earlier issue detection |
Identity Governance for Critical Infrastructure
Systemically Important FMIs require enterprise-scale identity governance. Here is an architecture pattern used by major clearing houses:
# Enterprise Identity Governance with AWS IAM Identity Center
# Step 1: Configure identity source (Azure AD, Okta, etc.)
aws sso-admin create-instance-access-control-attribute-configuration \
--instance-arn arn:aws:sso:::instance/ssoins-xxx \
--access-control-attributes '[
{"Key": "department", "Value": {"Source": ["enterprise.department"]}},
{"Key": "costCenter", "Value": {"Source": ["enterprise.costCenter"]}},
{"Key": "clearingRole", "Value": {"Source": ["enterprise.jobRole"]}}
]'
# Step 2: Create permission sets for different roles
aws sso-admin create-permission-set \
--instance-arn arn:aws:sso:::instance/ssoins-xxx \
--name "ClearingOperator" \
--session-duration "PT8H" \
--description "Access for clearing operations team"
# Step 3: Attach managed policies with least privilege
aws sso-admin attach-managed-policy-to-permission-set \
--instance-arn arn:aws:sso:::instance/ssoins-xxx \
--permission-set-arn arn:aws:sso:::permissionSet/xxx \
--managed-policy-arn arn:aws:iam::aws:policy/ReadOnlyAccess
# Benefits:
# - Reduced operational complexity
# - Strengthened security controls
# - Automated provisioning/deprovisioning
# - Audit trail for all access
Database Modernization for FMIs
Migrating mission-critical Oracle databases to Amazon RDS with minimal downtime:
# Weekend Migration Pattern for Oracle Databases
# Pre-Migration (Week before)
1. Set up AWS DMS replication instance
2. Create DMS endpoints for source and target
3. Run full load task in test mode
4. Validate row counts and data integrity
# Friday Evening (After Market Close)
5. Stop application writes to source database
6. Verify DMS CDC has zero lag
7. Perform final validation checks
aws dms describe-replication-tasks \
--filters "Name=replication-task-id,Values=quote-db-migration" \
--query 'ReplicationTasks[0].ReplicationTaskStats'
# Saturday (Cutover Window)
8. Update application connection strings
9. Run smoke tests against RDS
# Sunday (Validation)
10. Performance baseline comparison
11. Failover testing (minutes vs. days previously)
12. Rollback preparation (just in case)
# Monday (Go-Live)
13. Monitor application performance
14. Decommission source database after 30-day parallel run
# Results from Production Migration:
# - 3 Oracle databases (63 TB total)
# - 10 billion quotes/day ingested
# - Single weekend cutover
# - 2 days/month maintenance saved
# - 15% performance improvement
# - Failover: minutes vs. days
Frequently Asked Questions
Conclusion
The cloud adoption trajectory for Financial Market Infrastructure is clear: mission-critical trading and clearing systems are moving to AWS, and the results are exceeding expectations. Matching engines run faster on Outposts than on legacy hardware. Clearing houses meet stringent regulatory mandates with multi-region active/active architectures. Market data platforms process petabytes of tick data with 97% lower storage costs.
GenAI is accelerating this transformation—from surveillance systems with 100% accuracy to compliance processes that save millions of employee hours. As we move into 2026, expect the pace of adoption to increase as early movers demonstrate that cloud infrastructure delivers not just cost savings, but genuine competitive advantage in speed, resilience, and innovation capacity.
Planning Your FMI Cloud Migration?
Our AWS certified architects specialize in capital markets infrastructure. We can help you design ultra-low-latency trading architectures, implement regulatory-compliant DR strategies, and deploy GenAI solutions for surveillance and compliance.
