HeliosDB Case Study Template Library

Version: 1.0 Last Updated: 2025-12-08 Purpose: Comprehensive case study templates for customer success stories across industries

Template Structure
SaaS Case Studies
Financial Services Case Studies
Healthcare Case Studies
E-Commerce Case Studies
Other Industries Case Studies
Usage Guidelines

Template Structure

Each case study includes:

1-Page Summary: Executive overview with key metrics
Full 3-Page Version: Detailed technical implementation and results
Standard Sections: Profile, Challenge, Solution, Results, Quote, Technical Deep-Dive, Timeline, ROI, Lessons Learned

SaaS Case Studies

CASE_SAAS_001: Series A Marketing Analytics Platform

1-Page Summary

Company Profile

Company: StreamMetrics (anonymized)
Industry: Marketing Analytics SaaS
Stage: Series A (15M raised)
Size: 45 employees, 300 enterprise customers
Annual Recurring Revenue: $8M

The Challenge StreamMetrics faced critical database scalability issues as customer data volumes grew 10x in 6 months. Their PostgreSQL setup required:

3 read replicas costing $12,000/month
48-hour analytics query times
Manual sharding causing 2 weeks of engineering time per month
99.5% uptime (target: 99.99%)

The Solution Migrated to HeliosDB in 3 weeks with zero-downtime cutover:

PostgreSQL wire protocol compatibility enabled drop-in replacement
HTAP architecture eliminated need for separate analytics database
Multi-cloud deployment across AWS and GCP
2-person engineering team managed migration

Key Results

87% infrastructure cost reduction: $12K/month to $1,560/month
96% query performance improvement: 48 hours to 90 minutes
99.99% uptime achieved: Zero unplanned downtime in 6 months
15 engineering days/month reclaimed: Eliminated manual sharding work

Executive Quote “HeliosDB gave us enterprise-grade database capabilities at a fraction of the cost. We redirected our infrastructure savings into product development and accelerated our roadmap by 6 months.” — CTO, StreamMetrics

Full 3-Page Version

Company Background

StreamMetrics provides real-time marketing analytics for e-commerce brands, processing behavioral data from millions of end users. Founded in 2022, the company raised a $15M Series A in early 2024 to expand from mid-market to enterprise customers.

The platform ingests clickstream data, attribution events, and conversion metrics, providing customers with real-time dashboards and historical trend analysis. As enterprise customers came onboard, data volumes exploded from 50GB to 500GB per customer, straining the existing PostgreSQL infrastructure.

Detailed Challenge Statement

StreamMetrics’ rapid growth created a perfect storm of database challenges:

Performance Bottlenecks:

Analytical queries (90-day trend reports) took 24-48 hours to complete
Real-time dashboards experienced 15-30 second load times
Monthly customer-facing reports required pre-computation and caching
Peak traffic periods (Black Friday, Cyber Monday) caused 5x slowdowns

Cost Escalation:

Primary PostgreSQL instance: AWS RDS db.r6g.8xlarge at $4,200/month
Three read replicas for analytics: $7,800/month combined
Additional caching layer (Redis): $2,400/month
Total database infrastructure: $14,400/month
Projected costs at 3x growth: $43,200/month (unsustainable)

Operational Complexity:

Manual sharding required 2 engineers for 1 week per month
Schema migrations required 8-hour maintenance windows
Replica lag caused data inconsistency issues
On-call incidents: 12 per month (average 3 hours each)

Business Impact:

Customer churn: 8% annually due to slow analytics performance
Sales cycle delays: 30% longer POCs due to performance demos
Engineering velocity: 40% of backend team focused on database issues

Solution Architecture

StreamMetrics implemented HeliosDB in a phased approach:

Phase 1 - Evaluation (Week 1):

Deployed HeliosDB PostgreSQL-compatible instance on AWS
Loaded 30-day dataset snapshot (150GB)
Benchmarked query performance against existing PostgreSQL
Results: 15x improvement on analytical queries, 3x on transactional

Phase 2 - Parallel Operation (Week 2):

Set up dual-write from application to both PostgreSQL and HeliosDB
Configured logical replication for historical data migration
Validated data consistency using automated diff tools
Tested failover procedures

Phase 3 - Cutover (Week 3):

Blue-green deployment strategy for zero-downtime migration
Gradual traffic shift: 10% → 50% → 100% over 48 hours
Monitoring dashboards for query performance and error rates
Rollback plan validated (not needed)

Architecture Components:

┌─────────────────────────────────────────────────┐
│         Application Layer (Node.js)             │
└──────────────┬──────────────────────────────────┘
               │
               ▼
┌─────────────────────────────────────────────────┐
│     HeliosDB Multi-Protocol Gateway             │
│     - PostgreSQL Wire Protocol (Port 5432)      │
│     - Connection Pooling (PgBouncer compatible) │
└──────────────┬──────────────────────────────────┘
               │
      ┌────────┴────────┐
      ▼                 ▼
┌──────────┐      ┌──────────┐
│ OLTP     │      │ OLAP     │
│ Storage  │      │ Storage  │
│ (Hot)    │      │ (Tiered) │
└──────────┘      └──────────┘
      │                 │
      └────────┬────────┘
               ▼
    ┌──────────────────┐
    │ Multi-Cloud WAL  │
    │ AWS S3 + GCP GCS │
    └──────────────────┘

Key Configuration:

Instance: HeliosDB Standard on AWS c6g.4xlarge ($720/month)
Storage: 1TB SSD ($100/month) + 5TB S3 archival ($115/month)
Backup: Cloud WAL archiving to S3 ($50/month)
Monitoring: Prometheus + Grafana ($75/month)
Total: $1,560/month

Detailed Results and Metrics

Performance Improvements:

Metric	Before (PostgreSQL)	After (HeliosDB)	Improvement
90-day trend query	48 hours	90 minutes	96% faster
Real-time dashboard load	15-30 seconds	1-2 seconds	93% faster
Concurrent user capacity	500 users	5,000 users	10x scale
Write throughput	5,000 events/sec	50,000 events/sec	10x scale
Storage efficiency	500GB raw	150GB compressed	70% reduction

Cost Breakdown:

Component	PostgreSQL (Monthly)	HeliosDB (Monthly)	Savings
Primary instance	$4,200	$720	$3,480
Read replicas (3x)	$7,800	$0	$7,800
Caching layer	$2,400	$0	$2,400
Storage	$400	$265	$135
Backup/DR	$600	$75	$525
Total	$15,400	$1,560	$13,840

Annual Savings: $166,080

Operational Improvements:

Manual sharding eliminated: 80 hours/month reclaimed
On-call incidents reduced: 12/month → 1/month
Maintenance windows eliminated: 8 hours/month → 0 hours
Schema migration time: 8 hours → 15 minutes
Team velocity increase: 40% more feature development

Business Impact:

Customer churn reduced: 8% → 3% annually
Sales cycle acceleration: 30% faster POCs
Enterprise customer acquisition: 2x increase
Platform reliability: 99.99% uptime achieved
Customer satisfaction score: 72 → 89 (out of 100)

Implementation Timeline

Week 1: Evaluation

Day 1-2: HeliosDB deployment and initial configuration
Day 3-4: Data loading and schema validation
Day 5-7: Performance benchmarking and testing

Week 2: Preparation

Day 8-10: Dual-write implementation in application layer
Day 11-12: Historical data replication setup
Day 13-14: Data consistency validation and testing

Week 3: Migration

Day 15-16: Final validation and go/no-go decision
Day 17: Blue-green deployment and gradual traffic shift
Day 18-21: Monitoring, validation, PostgreSQL decommission

Week 4-6: Optimization

Fine-tuning query patterns and indexes
Implementing branch-aware MVCC for time-travel queries
Setting up automated backup and disaster recovery
Training team on HeliosDB-specific features

ROI Calculation

Investment:

HeliosDB license: $1,000/month ($12,000/year)
Migration engineering cost: 320 hours × $150/hour = $48,000
Training and onboarding: $5,000
Total Year 1 Investment: $65,000

Returns (Year 1):

Infrastructure cost savings: $166,080
Engineering time reclaimed: 80 hours/month × 12 × $150 = $144,000
Reduced churn (5% of $8M ARR): $400,000
Faster sales cycles (15% revenue acceleration): $150,000
Total Year 1 Returns: $860,080

ROI Metrics:

Net benefit Year 1: $795,080
ROI: 1,223%
Payback period: 27 days
3-year projected benefit: $2.4M

Technical Deep-Dive

HTAP Architecture Benefits:

HeliosDB’s hybrid transactional/analytical processing eliminated the need for StreamMetrics’ complex ETL pipeline:

-- Before: Required pre-computed materialized views
-- Updated nightly via 4-hour batch job

-- After: Real-time analytical queries on live data
SELECT
    customer_id,
    date_trunc('day', event_timestamp) as day,
    count(*) as events,
    count(distinct session_id) as sessions,
    sum(revenue) as total_revenue
FROM events
WHERE event_timestamp >= NOW() - INTERVAL '90 days'
    AND customer_id = $1
GROUP BY customer_id, day
ORDER BY day DESC;

-- Execution time: 48 hours (PostgreSQL) → 90 seconds (HeliosDB)

Branch-Aware MVCC for Time-Travel:

StreamMetrics uses HeliosDB’s branching feature for customer-specific analytics snapshots:

-- Create a named branch for month-end reporting
CREATE BRANCH month_end_202411 AT TIMESTAMP '2024-11-30 23:59:59';

-- Query historical data without impacting production
SET heliosdb.branch = 'month_end_202411';
SELECT * FROM customer_metrics WHERE ...;

-- Production queries continue on main branch unaffected

Multi-Cloud WAL Archiving:

Disaster recovery improved with automated cloud storage:

[storage.wal_archiving]
enabled = true
primary_backend = "s3"
secondary_backend = "gcs"
retention_days = 90
compression = "zstd"

[storage.s3]
region = "us-east-1"
bucket = "streammetrics-wal-archive"

[storage.gcs]
project = "streammetrics-dr"
bucket = "streammetrics-wal-backup"

Recovery time objective (RTO) improved from 4 hours to 15 minutes.

Automated Query Optimization:

HeliosDB’s query analyzer identified and fixed 23 suboptimal queries:

-- Before: Full table scan on 500GB table
SELECT * FROM events
WHERE customer_id = $1
ORDER BY event_timestamp DESC
LIMIT 100;

-- HeliosDB suggestion: Add composite index
CREATE INDEX idx_events_customer_time
ON events(customer_id, event_timestamp DESC);

-- Result: 15,000ms → 12ms (1,250x improvement)

Lessons Learned

What Went Well:

PostgreSQL compatibility was seamless: Zero application code changes required
Dual-write strategy de-risked migration: Enabled gradual rollback if needed
HTAP eliminated complexity: Removed 15,000 lines of ETL code
Cloud-native architecture: Multi-cloud strategy improved reliability
Performance exceeded expectations: 15x improvement vs. 5x anticipated

Challenges and Solutions:

Query plan differences: Some queries performed differently due to HeliosDB optimizer
- Solution: Used EXPLAIN ANALYZE to identify and rewrite 8 queries
Monitoring setup: Existing PostgreSQL dashboards needed updates
- Solution: HeliosDB’s Prometheus metrics integrated into Grafana in 2 hours
Team learning curve: Engineers unfamiliar with HTAP concepts
- Solution: 2-day training session covered branching, MVCC, tiered storage

Recommendations for Similar Migrations:

Start with read-heavy workloads to validate performance gains
Use dual-write for at least 1 week to build confidence
Benchmark top 20 queries before and after migration
Plan for 2-week optimization period post-migration
Engage HeliosDB support for architecture review

Conclusion

StreamMetrics’ migration to HeliosDB delivered transformational results: 87% cost reduction, 96% performance improvement, and elimination of operational toil. The company reinvested savings into product development, accelerating their roadmap and improving competitive positioning.

The success enabled StreamMetrics to scale confidently into enterprise markets while maintaining a lean infrastructure team. Six months post-migration, the platform handles 10x the original data volume with the same infrastructure footprint.

CASE_SAAS_002: Series B Collaboration Platform

1-Page Summary

Company Profile

Company: WorkflowHub (anonymized)
Industry: Team Collaboration SaaS
Stage: Series B (50M total raised)
Size: 180 employees, 5,000 business customers
Annual Recurring Revenue: $35M

The Challenge WorkflowHub’s real-time collaboration features (document editing, comments, notifications) required sub-100ms latency, but their MongoDB + PostgreSQL dual-database architecture created:

Data consistency issues between systems
$45,000/month infrastructure costs
5-engineer team dedicated to database operations
Complex application logic to maintain dual writes
15-minute deployment windows due to schema migrations

The Solution Consolidated to HeliosDB using multi-protocol support:

PostgreSQL protocol for structured data (users, permissions, billing)
MongoDB protocol for document storage and collaboration data
6-week migration with phased cutover
4-person engineering team

Key Results

73% infrastructure cost reduction: $45K/month to $12K/month
5 engineers redeployed to product development
99.995% uptime achieved: 2x improvement from 99.95%
60% deployment speed increase: 15-minute windows to 6 minutes
Zero data consistency issues since migration

Executive Quote “Consolidating from two databases to HeliosDB’s multi-protocol architecture eliminated our biggest operational headache. We can finally focus on building features instead of managing infrastructure.” — VP of Engineering, WorkflowHub

Full 3-Page Version

Company Background

WorkflowHub provides a modern collaboration platform combining project management, document editing, and team communication. Founded in 2020, the company raised a $35M Series B in 2023 to expand internationally and build AI-powered features.

The platform supports 5,000 business customers with 200,000+ end users, processing 50M API requests daily. Core features include real-time document collaboration (similar to Google Docs), task management, file storage, and integrated chat.

Detailed Challenge Statement

WorkflowHub’s dual-database architecture created mounting technical debt:

Architectural Complexity:

PostgreSQL for relational data: users, teams, permissions, billing, audit logs
MongoDB for document data: versioned documents, comments, real-time edits
Redis for session management and real-time coordination
Elasticsearch for full-text search
4 different database technologies requiring specialized expertise

Data Consistency Challenges:

Dual writes from application created race conditions
Eventual consistency caused user-facing bugs (permissions out of sync)
Cross-database transactions impossible (had to implement saga pattern)
Data migrations required coordinating changes across 2 systems

Performance Bottlenecks:

Document load times: 800ms average (target: <200ms)
Search queries: 3-5 seconds for large workspaces
Real-time collaboration: 150ms latency at peak load
Cross-database joins required application-level logic (slow and error-prone)

Cost Structure:

PostgreSQL cluster (AWS RDS): $18,000/month
MongoDB Atlas (M60 cluster): $22,000/month
Redis (ElastiCache): $3,000/month
Elasticsearch: $8,000/month
Data transfer costs: $2,500/month
Total: $53,500/month
5 engineers focused on database operations: $750,000/year

Business Impact:

Feature velocity down 40% due to database complexity
Enterprise deals delayed due to data residency requirements
12% of support tickets related to data consistency issues
Engineering morale affected by operational toil

Solution Architecture

WorkflowHub adopted HeliosDB’s multi-protocol architecture:

Phase 1 - PostgreSQL Migration (Weeks 1-2):

Deployed HeliosDB with PostgreSQL protocol enabled
Migrated relational data: users, teams, permissions, billing
Validated query performance and ACID guarantees
Updated connection strings in application config

Phase 2 - MongoDB Protocol Enablement (Weeks 3-4):

Enabled MongoDB wire protocol on same HeliosDB cluster
Migrated document collections: workspaces, documents, comments
Validated document operations and indexing
Implemented connection pooling for MongoDB clients

Phase 3 - Search Integration (Week 5):

Leveraged HeliosDB’s built-in full-text search
Migrated Elasticsearch indices to HeliosDB
Updated search API endpoints
Decommissioned Elasticsearch cluster

Phase 4 - Optimization (Week 6):

Replaced application-level joins with HeliosDB cross-protocol queries
Eliminated dual-write logic (now single transaction)
Configured branch-aware backups for compliance
Performance tuning and monitoring setup

Unified Architecture:

┌─────────────────────────────────────────────────┐
│         Application Layer (Go + Node.js)        │
└──────────┬─────────────────────┬────────────────┘
           │                     │
    ┌──────▼──────┐      ┌──────▼──────┐
    │ PostgreSQL  │      │  MongoDB    │
    │ Wire        │      │  Wire       │
    │ Protocol    │      │  Protocol   │
    │ (Port 5432) │      │  (Port 27017)│
    └──────┬──────┘      └──────┬──────┘
           │                     │
           └──────────┬──────────┘
                      ▼
        ┌─────────────────────────────┐
        │   HeliosDB Unified Engine   │
        │   - MVCC Transaction Layer  │
        │   - Full-Text Search        │
        │   - Real-Time Indexing      │
        └──────────────┬──────────────┘
                       ▼
              ┌────────────────┐
              │ Tiered Storage │
              │ Hot: NVMe SSD  │
              │ Warm: S3       │
              └────────────────┘

Configuration:

[protocols]
postgres.enabled = true
postgres.port = 5432
mongodb.enabled = true
mongodb.port = 27017

[protocols.postgres]
max_connections = 500
statement_timeout = "30s"

[protocols.mongodb]
max_connections = 1000
wire_version = "6.0"  # MongoDB 6.0 compatibility

[storage]
tier_hot_size = "500GB"
tier_warm_backend = "s3"
compression = "zstd"

[search]
enabled = true
analyzer = "standard"
max_result_window = 10000

Detailed Results and Metrics

Performance Improvements:

Metric	Before (Multi-DB)	After (HeliosDB)	Improvement
Document load time	800ms	180ms	77% faster
Search query latency	3-5 seconds	400ms	87% faster
Real-time collab latency	150ms	45ms	70% faster
Cross-entity queries	2,000ms	250ms	87% faster
Write throughput	10K ops/sec	45K ops/sec	4.5x increase

Cost Reduction:

Component	Before (Monthly)	After (Monthly)	Savings
PostgreSQL (RDS)	$18,000	$0	$18,000
MongoDB (Atlas)	$22,000	$0	$22,000
Redis	$3,000	$0	$3,000
Elasticsearch	$8,000	$0	$8,000
Data transfer	$2,500	$500	$2,000
HeliosDB	$0	$12,000	-$12,000
Total	$53,500	$12,500	$41,000

Annual Savings: $492,000

Operational Improvements:

Database team size: 5 engineers → 0 engineers (redeployed)
Deployment complexity: 15-minute windows → 6-minute windows
Data consistency incidents: 12/month → 0/month
Schema migration time: 2 hours → 20 minutes
Monitoring dashboards: 8 separate systems → 1 unified system

Development Velocity:

Code complexity reduced: 25,000 lines of database logic removed
Feature delivery speed: 40% increase
Bug rate related to databases: 85% reduction
Onboarding time for new engineers: 3 weeks → 1 week

Implementation Timeline

Week 1-2: PostgreSQL Migration

Deploy HeliosDB cluster on AWS
Set up logical replication from PostgreSQL to HeliosDB
Validate data consistency with automated testing
Cutover relational workloads with 5-minute downtime

Week 3-4: MongoDB Migration

Enable MongoDB protocol on HeliosDB
Bulk load MongoDB collections using mongodump/mongorestore
Implement dual-write to both MongoDB and HeliosDB
Gradually shift read traffic to HeliosDB
Cutover write traffic with zero downtime

Week 5: Search Migration

Configure full-text search indexes in HeliosDB
Reindex all searchable content
Test search quality and performance
Cutover search traffic
Decommission Elasticsearch

Week 6: Optimization and Cleanup

Remove dual-write logic from application
Refactor cross-database queries to use HeliosDB joins
Optimize indexes based on query patterns
Configure automated backups and monitoring
Document new architecture and runbooks

ROI Calculation

Investment:

HeliosDB license: $8,000/month ($96,000/year)
Infrastructure: $4,000/month ($48,000/year)
Migration engineering: 640 hours × $175/hour = $112,000
Training: $8,000
Total Year 1 Investment: $264,000

Returns (Year 1):

Infrastructure cost savings: $492,000
5 engineers redeployed (avoided headcount): $750,000
Reduced downtime (99.995% vs 99.95%): $100,000
Faster feature delivery (40% velocity increase): $500,000
Reduced support costs (data consistency bugs): $75,000
Total Year 1 Returns: $1,917,000

ROI Metrics:

Net benefit Year 1: $1,653,000
ROI: 626%
Payback period: 48 days
3-year projected benefit: $5.1M

Technical Deep-Dive

Cross-Protocol Transaction Example:

Before HeliosDB, cross-database operations required complex saga patterns:

// Before: Complex saga pattern with compensating transactions
func UpdateDocumentPermissions(docID, userID, permission string) error {
    // Start transaction in PostgreSQL
    pgTx, _ := pgDB.Begin()

    // Update permissions table
    _, err := pgTx.Exec("UPDATE permissions SET level = $1 WHERE doc_id = $2 AND user_id = $3",
        permission, docID, userID)
    if err != nil {
        pgTx.Rollback()
        return err
    }

    // Update MongoDB document metadata
    _, err = mongoClient.UpdateOne(ctx,
        bson.M{"_id": docID},
        bson.M{"$set": bson.M{"permissions." + userID: permission}})
    if err != nil {
        // Compensating transaction: rollback PostgreSQL
        pgTx.Rollback()
        return err
    }

    // Commit PostgreSQL transaction
    return pgTx.Commit()
}

After HeliosDB, single ACID transaction:

// After: Single ACID transaction across protocols
func UpdateDocumentPermissions(docID, userID, permission string) error {
    tx, _ := heliosDB.Begin()

    // Update PostgreSQL table
    _, err := tx.ExecPG("UPDATE permissions SET level = $1 WHERE doc_id = $2 AND user_id = $3",
        permission, docID, userID)
    if err != nil {
        tx.Rollback()
        return err
    }

    // Update MongoDB document in same transaction
    _, err = tx.ExecMongo("documents", bson.M{
        "update": bson.M{"_id": docID},
        "set": bson.M{"permissions." + userID: permission},
    })
    if err != nil {
        tx.Rollback()
        return err
    }

    return tx.Commit()
}

Unified Search with Cross-Protocol Data:

-- Search across PostgreSQL and MongoDB data in single query
SELECT
    d.id,
    d.title,
    u.name as author_name,
    ts_rank(d.content_tsv, query) as relevance
FROM documents d  -- MongoDB collection exposed as table
JOIN users u ON d.author_id = u.id  -- PostgreSQL table
WHERE d.content_tsv @@ to_tsquery('project & launch')
    AND d.workspace_id = $1
ORDER BY relevance DESC
LIMIT 20;

Branch-Aware Compliance:

For GDPR and data residency requirements:

-- Create compliance snapshot for audit
CREATE BRANCH gdpr_audit_2024Q4 AT TIMESTAMP '2024-12-31 23:59:59';

-- Query historical data for compliance reporting
SET heliosdb.branch = 'gdpr_audit_2024Q4';
SELECT user_id, data_accessed, timestamp
FROM audit_log
WHERE event_type = 'personal_data_access';

Lessons Learned

What Went Well:

Multi-protocol support eliminated complexity: Single database for all workloads
Zero-downtime migration: Blue-green deployment worked flawlessly
ACID across protocols: Simplified application logic dramatically
Performance improvements exceeded expectations: 77% faster on average
Team morale improved: Engineers excited to focus on product

Challenges and Solutions:

MongoDB query compatibility: Some MongoDB-specific features not supported
- Solution: Refactored 3% of queries to use standard operations
Learning curve for ops team: Unified system required new mental models
- Solution: 3-day intensive training with HeliosDB engineers
Initial performance regression: Some queries slower due to different optimizer
- Solution: Worked with HeliosDB support to tune indexes, resolved in 2 days

Recommendations:

Migrate PostgreSQL first to de-risk relational workloads
Plan for 10% query rewrites when consolidating MongoDB
Use branch-aware features for compliance from day one
Budget 1 week for post-migration optimization
Involve HeliosDB support early for architecture review

Conclusion

WorkflowHub’s consolidation to HeliosDB eliminated architectural complexity, reduced costs by 73%, and improved performance across the board. The engineering team now focuses on product innovation instead of database operations.

Most importantly, the unified architecture positioned WorkflowHub for future growth, enabling features like real-time AI analysis of documents that would have been impossible with the previous fragmented infrastructure.

CASE_SAAS_003: Series C Developer Tools Platform

1-Page Summary

Company Profile

Company: DevKit (anonymized)
Industry: Developer Tools SaaS
Stage: Series C (120M total raised)
Size: 350 employees, 15,000 corporate customers
Annual Recurring Revenue: $85M

The Challenge DevKit’s CI/CD analytics platform stored build logs, test results, and metrics across multiple databases:

TimescaleDB for time-series metrics (build times, test durations)
PostgreSQL for relational data (repos, users, teams)
ClickHouse for analytical queries
$125,000/month infrastructure costs
72-hour query times for quarterly trend reports
Complex ETL pipelines consuming 20% of engineering resources

The Solution Migrated to HeliosDB with native time-series and HTAP support:

PostgreSQL protocol for application compatibility
ClickHouse protocol for analytical tools
Time-series storage for metrics
8-week migration in phases
6-person migration team

Key Results

91% infrastructure cost reduction: $125K/month to $11K/month
98% analytical query improvement: 72 hours to 90 minutes
Zero ETL pipelines: Real-time analytics on live data
20% engineering capacity reclaimed: Eliminated ETL maintenance
10x data retention: 90 days to 900 days at same cost

Executive Quote “HeliosDB’s unified time-series and analytical capabilities let us retire three separate databases. We’re now providing customers with insights we couldn’t afford to compute before.” — CTO, DevKit

Full 3-Page Version

Company Background

DevKit provides CI/CD analytics and optimization tools for enterprise engineering teams. The platform ingests data from GitHub, GitLab, Jenkins, CircleCI, and other DevOps tools, analyzing build performance, test reliability, and deployment patterns.

Founded in 2019, DevKit serves 15,000 corporate customers including Fortune 500 companies. The platform processes 50M CI/CD events daily, storing build logs, test results, code metrics, and deployment data.

Detailed Challenge Statement

DevKit’s multi-database architecture created severe operational and cost challenges:

Database Sprawl:

TimescaleDB: Build duration metrics, test execution times, deployment frequency
- 3-node cluster on AWS RDS: $35,000/month
- 90-day retention due to storage costs
- Complex compression and retention policies
PostgreSQL: Users, repositories, teams, integrations, feature flags
- Primary + 2 replicas: $28,000/month
- Standard relational workload
ClickHouse: Analytical queries for dashboards and reports
- 5-node cluster: $55,000/month
- Daily ETL from TimescaleDB and PostgreSQL
- 24-48 hour data lag
Redis: Caching and session management: $7,000/month

Total infrastructure: $125,000/month

Data Pipeline Complexity:

15 separate ETL jobs orchestrated via Airflow
20% of backend engineering time spent maintaining pipelines
Data quality issues due to pipeline failures
24-hour lag for analytical queries
Schema changes required coordinating 3 database migrations

Performance Issues:

Quarterly trend reports: 48-72 hours to compute
Real-time dashboards: 5-10 second load times
Join queries across databases: Application-level, slow and error-prone
Customer-facing analytics limited to 90 days due to storage costs

Operational Burden:

8-person infrastructure team managing databases
Monthly database-related incidents: 18 average
Backup/restore complexity across 3 systems
Monitoring requires 5 different tools

Business Constraints:

Customer requests for longer retention denied (too expensive)
Advanced analytics features delayed due to query performance
Enterprise deals blocked by data residency requirements
Competitive pressure from newer tools with better analytics

Solution Architecture

DevKit consolidated to HeliosDB in a staged approach:

Phase 1 - Assessment and Planning (Weeks 1-2):

Analyzed query patterns across all 3 databases
Identified top 100 queries by frequency and cost
Designed unified schema leveraging HeliosDB time-series capabilities
Created migration runbook

Phase 2 - PostgreSQL Migration (Weeks 3-4):

Deployed HeliosDB cluster with PostgreSQL protocol
Migrated relational data using logical replication
Validated application compatibility
Cutover transactional workloads

Phase 3 - Time-Series Migration (Weeks 5-6):

Migrated TimescaleDB hypertables to HeliosDB time-series tables
Bulk loaded historical data (90 days)
Validated time-series query performance
Decommissioned TimescaleDB

Phase 4 - ClickHouse Migration (Week 7):

Enabled ClickHouse wire protocol on HeliosDB
Migrated analytical views and materialized tables
Updated BI tool connections (Metabase, Tableau)
Eliminated ETL pipelines
Decommissioned ClickHouse cluster

Phase 5 - Optimization (Week 8):

Extended retention to 900 days using tiered storage
Implemented branch-aware snapshots for compliance
Configured automated compression and archiving
Fine-tuned indexes and query performance

Unified Architecture:

┌──────────────────────────────────────────────────┐
│    Application Layer (Python/FastAPI)           │
└────────┬──────────────────────┬──────────────────┘
         │                      │
    ┌────▼─────┐          ┌────▼─────┐
    │PostgreSQL│          │ClickHouse│
    │ Protocol │          │ Protocol │
    │(Port 5432)          │(Port 9000)│
    └────┬─────┘          └────┬─────┘
         │                      │
         └──────────┬───────────┘
                    ▼
    ┌───────────────────────────────────┐
    │   HeliosDB Unified Engine         │
    │   - Time-Series Storage           │
    │   - HTAP Query Engine             │
    │   - Automated Tiering             │
    │   - Continuous Aggregation        │
    └───────────────┬───────────────────┘
                    ▼
    ┌───────────────────────────────────┐
    │     Tiered Storage                │
    │  Hot: NVMe (30 days, 500GB)       │
    │  Warm: SSD (90 days, 2TB)         │
    │  Cold: S3 (780 days, 50TB)        │
    └───────────────────────────────────┘

Key Configuration:

[protocols]
postgres.enabled = true
clickhouse.enabled = true

[timeseries]
enabled = true
default_retention = "900d"
compression = "zstd"
downsampling_enabled = true

[timeseries.tiers]
hot = { duration = "30d", storage = "nvme" }
warm = { duration = "90d", storage = "ssd" }
cold = { duration = "780d", storage = "s3" }

[timeseries.continuous_aggregates]
# Automatically downsample to 1-hour granularity after 30 days
build_duration_hourly = { source = "build_metrics", interval = "1h", after = "30d" }

Detailed Results and Metrics

Performance Improvements:

Metric	Before (Multi-DB)	After (HeliosDB)	Improvement
Quarterly trend query	72 hours	90 minutes	98% faster
Real-time dashboard	8 seconds	1.2 seconds	85% faster
Time-series ingestion	25K events/sec	150K events/sec	6x increase
Analytical join queries	45 seconds	3 seconds	93% faster
Data retention	90 days	900 days	10x increase
Storage efficiency	15TB raw	3TB compressed	80% reduction

Cost Breakdown:

Component	Before (Monthly)	After (Monthly)	Savings
TimescaleDB	$35,000	$0	$35,000
PostgreSQL	$28,000	$0	$28,000
ClickHouse	$55,000	$0	$55,000
Redis	$7,000	$0	$7,000
Airflow (ETL)	$3,500	$0	$3,500
HeliosDB compute	$0	$6,000	-$6,000
HeliosDB storage	$0	$5,000	-$5,000
Total	$128,500	$11,000	$117,500

Annual Savings: $1,410,000

Engineering Efficiency:

ETL maintenance eliminated: 7,200 hours/year reclaimed
Infrastructure team reduced: 8 → 3 engineers
Schema migration complexity: 75% reduction
Database-related incidents: 18/month → 2/month
Time to add new metrics: 2 weeks → 2 hours

Business Impact:

New analytics features shipped: 12 in 6 months (vs. 3 previous)
Customer retention improved: 94% → 97%
Enterprise deal cycle: 25% faster (data residency solved)
Customer satisfaction (analytics): 68 → 88 (out of 100)

Implementation Timeline

Weeks 1-2: Assessment

Query analysis and profiling across all databases
Schema design for unified HeliosDB model
Migration strategy and risk assessment
Stakeholder alignment and go/no-go decision

Weeks 3-4: PostgreSQL Migration

HeliosDB cluster deployment
Logical replication setup
Application compatibility testing
Cutover with 10-minute downtime

Weeks 5-6: Time-Series Migration

Time-series table creation in HeliosDB
Historical data bulk load (90 days, 8TB)
Dual-write implementation for new data
Query validation and performance testing
Cutover time-series workload

Week 7: ClickHouse Migration

ClickHouse protocol enablement
Analytical view migration
BI tool reconfiguration
ETL pipeline decommissioning
ClickHouse cluster shutdown

Week 8: Optimization

Retention policy configuration (900 days)
Continuous aggregation setup
Index tuning based on query patterns
Automated backup and monitoring
Documentation and team training

ROI Calculation

Investment:

HeliosDB license: $6,000/month ($72,000/year)
Infrastructure (compute + storage): $5,000/month ($60,000/year)
Migration engineering: 960 hours × $200/hour = $192,000
Training and documentation: $15,000
Total Year 1 Investment: $339,000

Returns (Year 1):

Infrastructure cost savings: $1,410,000
ETL engineering time reclaimed: 7,200 hours × $200 = $1,440,000
Infrastructure team reduction: 5 engineers × $180K = $900,000
Improved customer retention: $850,000
New features revenue impact: $1,200,000
Total Year 1 Returns: $5,800,000

ROI Metrics:

Net benefit Year 1: $5,461,000
ROI: 1,611%
Payback period: 21 days
3-year projected benefit: $17.2M

Technical Deep-Dive

Time-Series Optimization:

HeliosDB’s native time-series support eliminated TimescaleDB complexity:

-- Before: Complex TimescaleDB hypertable with manual compression
CREATE TABLE build_metrics (
    time TIMESTAMPTZ NOT NULL,
    repo_id INT,
    build_duration_ms INT,
    test_count INT,
    success BOOLEAN
);
SELECT create_hypertable('build_metrics', 'time');
ALTER TABLE build_metrics SET (
    timescaledb.compress,
    timescaledb.compress_segmentby = 'repo_id',
    timescaledb.compress_orderby = 'time DESC'
);

-- After: HeliosDB time-series table with automatic optimization
CREATE TIMESERIES TABLE build_metrics (
    timestamp TIMESTAMPTZ NOT NULL,
    repo_id INT,
    build_duration_ms INT,
    test_count INT,
    success BOOLEAN
) WITH (
    retention = '900d',
    partition_by = 'repo_id',
    compression = 'auto'
);

Continuous Aggregation:

Automatic downsampling for long-term storage efficiency:

-- Automatically created by HeliosDB after 30 days
-- Downsamples to 1-hour granularity for queries beyond 30 days
CREATE CONTINUOUS AGGREGATE build_duration_hourly
WITH (timeseries = 'build_metrics', interval = '1h', start_after = '30d')
AS SELECT
    time_bucket('1 hour', timestamp) as hour,
    repo_id,
    avg(build_duration_ms) as avg_duration,
    percentile_cont(0.95) WITHIN GROUP (ORDER BY build_duration_ms) as p95_duration,
    count(*) as build_count
FROM build_metrics
GROUP BY hour, repo_id;

Cross-Protocol Analytics:

ClickHouse protocol for BI tools, PostgreSQL for application:

-- BI tool (Tableau) uses ClickHouse protocol for fast aggregation
-- Query executed via ClickHouse wire protocol (port 9000)
SELECT
    toStartOfDay(timestamp) as day,
    repo_id,
    quantile(0.5)(build_duration_ms) as median_duration,
    quantile(0.95)(build_duration_ms) as p95_duration
FROM build_metrics
WHERE timestamp >= today() - INTERVAL 90 DAY
GROUP BY day, repo_id
ORDER BY day DESC, p95_duration DESC;

-- Same query optimized automatically by HeliosDB
-- Execution time: 45s (ClickHouse) → 3s (HeliosDB)

Branch-Aware Compliance:

DevKit uses branching for quarterly audit snapshots:

-- Create quarterly snapshot for compliance/audit
CREATE BRANCH audit_2024_q4 AT TIMESTAMP '2024-12-31 23:59:59';

-- Query historical data without impacting production
SET heliosdb.branch = 'audit_2024_q4';
SELECT repo_id, count(*) as builds, avg(build_duration_ms)
FROM build_metrics
WHERE timestamp >= '2024-10-01' AND timestamp < '2025-01-01'
GROUP BY repo_id;

Lessons Learned

What Went Well:

Time-series performance exceeded expectations: 6x ingestion rate improvement
10x retention increase at lower cost: Tiered storage game-changer
Zero ETL maintenance: Eliminated 15 complex pipelines
BI tool compatibility: ClickHouse protocol seamless for Tableau/Metabase
Team enthusiasm: Engineers excited to eliminate ETL toil

Challenges and Solutions:

Initial storage cost concerns: Worried about 10x retention cost
- Solution: Tiered storage (S3 cold tier) made it cheaper than before
ClickHouse query compatibility: 5% of queries needed rewrites
- Solution: HeliosDB support helped optimize, most queries faster
Historical data migration: 8TB bulk load took longer than expected
- Solution: Parallelized load across 10 workers, completed in 36 hours

Recommendations:

Start with relational migration to validate PostgreSQL compatibility
Plan for 48-72 hours for bulk time-series data loading
Test BI tool queries early (ClickHouse protocol compatibility)
Use continuous aggregates from day one for long-term efficiency
Engage HeliosDB architects for time-series schema design

Conclusion

DevKit’s consolidation to HeliosDB transformed their analytics platform from a costly, complex multi-database architecture to a unified, high-performance system. The 91% cost reduction and elimination of ETL pipelines freed engineering resources to focus on product innovation.

Most significantly, the 10x data retention increase (90 → 900 days) enabled new analytical features that customers had requested for years. DevKit now provides historical trend analysis and anomaly detection that would have been economically impossible before.

The migration positioned DevKit for continued growth, with infrastructure costs now scaling sublinearly with data volume thanks to automated tiering and compression.

CASE_SAAS_004: Growth-Stage Customer Data Platform

1-Page Summary

Company Profile

Company: Unify360 (anonymized)
Industry: Customer Data Platform (CDP)
Stage: Growth stage (15M ARR, profitable)
Size: 65 employees, 850 mid-market customers
Annual Recurring Revenue: $15M

The Challenge Unify360’s multi-tenant CDP architecture segregated customer data across 850 separate PostgreSQL databases (one per customer):

Database provisioning took 3 days per new customer
$78,000/month RDS costs for 850 small instances
Cross-customer analytics impossible
Backup/restore complexity: 850 separate processes
Onboarding friction slowing sales

The Solution Migrated to HeliosDB with branch-aware multi-tenancy:

Each customer as isolated branch (not separate database)
Shared infrastructure with strict isolation
10-second customer provisioning
4-week migration using automated tooling

Key Results

94% infrastructure cost reduction: $78K/month to $4.5K/month
Customer provisioning: 3 days to 10 seconds (99.99% faster)
Cross-customer analytics enabled: New premium feature
Backup complexity eliminated: Single backup for all customers
Sales cycle shortened: 40% faster onboarding

Executive Quote “HeliosDB’s branch-aware architecture solved our multi-tenancy nightmare. We went from 850 databases to 1, cut costs by 94%, and unlocked analytics features we couldn’t build before.” — CEO, Unify360

Full 3-Page Version

Company Background

Unify360 provides a customer data platform (CDP) for mid-market e-commerce and retail companies. The platform unifies customer data from multiple sources (website, mobile app, CRM, email, POS systems), providing segmentation, analytics, and activation capabilities.

Founded in 2021, Unify360 achieved profitability at $15M ARR by serving 850 mid-market customers. The company differentiated on ease of use and fast time-to-value, targeting the long-tail of e-commerce brands underserved by enterprise CDPs like Segment and mParticle.

Detailed Challenge Statement

Unify360’s database-per-tenant architecture created unsustainable operational complexity:

Multi-Tenant Architecture Issues:

850 separate PostgreSQL databases: One per customer for data isolation
Each database: AWS RDS db.t3.medium instance
Average database size: 5-50GB per customer
Monthly cost: $91.75 per database × 850 = $78,000/month
Projected cost at 2,000 customers: $183,500/month (unsustainable)

Provisioning Bottleneck:

New customer onboarding required:
1. Provision new RDS instance (45 minutes)
2. Run schema migrations (60 minutes)
3. Configure backup policy (15 minutes)
4. Set up monitoring and alerts (30 minutes)
5. Manual QA verification (45 minutes)
Total: 3 days from contract signature to production access
Sales team complained: “Competitors onboard in hours, we take days”

Operational Complexity:

850 separate backup/restore processes
Schema migrations required rolling out to 850 databases
Database upgrades: 3-week project to upgrade all instances
Monitoring: 850 separate CloudWatch dashboards
Incident response: “Which customer database is having issues?”

Feature Limitations:

Cross-customer benchmarking impossible (isolation model)
Aggregate analytics for market insights: not feasible
Data science/ML models: can’t train across customer base
Customers requested competitive benchmarking: “How do we compare to similar brands?”

Business Impact:

Sales velocity constrained by 3-day onboarding
Infrastructure costs growing faster than revenue (threat to profitability)
Engineering team spending 50% time on database operations
Premium features blocked by architecture limitations

Solution Architecture

Unify360 adopted HeliosDB’s branch-aware multi-tenancy model:

Conceptual Shift:

Before: 850 separate databases (physical isolation)
After: 850 branches in 1 database (logical isolation)

Branch-Based Isolation: Each customer gets a dedicated branch with:

Complete data isolation (cannot query other branches)
Independent schema evolution
Branch-specific access controls
Separate backup/restore points
Point-in-time recovery per branch

Migration Strategy (4 weeks):

Week 1: Automation Framework

Built tooling to automate 850 migrations
Schema normalization (all customers on same schema version)
Data validation framework
Parallel migration orchestration

Week 2: Pilot Migration (10 customers)

Selected 10 small customers for pilot
Created branches in HeliosDB
Migrated data using pg_dump/pg_restore
Validated data integrity
Performance testing

Week 3: Bulk Migration (840 customers)

Parallelized migration: 50 customers at a time
Automated cutover process
Continuous validation and monitoring
Rollback capability for each customer

Week 4: Decommission and Optimization

Shut down 850 RDS instances
Fine-tune HeliosDB indexes and queries
Implement cross-customer analytics features
Documentation and team training

New Architecture:

┌─────────────────────────────────────────────────┐
│     Application Layer (Node.js/Express)         │
│     - Customer context from JWT/session         │
└──────────────────┬──────────────────────────────┘
                   │
                   ▼
┌─────────────────────────────────────────────────┐
│     Branch Router (Middleware)                  │
│     - Maps customer ID to branch name           │
│     - Sets heliosdb.branch for session          │
└──────────────────┬──────────────────────────────┘
                   │
                   ▼
┌─────────────────────────────────────────────────┐
│     HeliosDB Multi-Branch Instance              │
│     - 850 customer branches                     │
│     - Shared compute resources                  │
│     - Isolated data per branch                  │
└──────────────────┬──────────────────────────────┘
                   │
                   ▼
┌─────────────────────────────────────────────────┐
│     Tiered Storage                              │
│     Hot: NVMe (active customers)                │
│     Cold: S3 (inactive customers)               │
└─────────────────────────────────────────────────┘

Application Code Changes:

// Before: Database connection per customer
const customerDB = dbPool[customerId]; // 850 separate connection pools
const result = await customerDB.query('SELECT * FROM customers WHERE id = $1', [id]);

// After: Branch context per customer
const db = heliosDBPool; // Single connection pool
await db.query('SET heliosdb.branch = $1', [`customer_${customerId}`]);
const result = await db.query('SELECT * FROM customers WHERE id = $1', [id]);

Configuration:

[branching]
enabled = true
max_branches = 5000  # Room for growth to 5,000 customers
branch_isolation = "strict"  # No cross-branch queries without explicit permission

[branching.resource_limits]
# Prevent one customer from consuming all resources
max_connections_per_branch = 20
max_memory_per_branch = "2GB"
max_cpu_per_branch = "25%"

[storage.tiering]
# Automatically tier inactive customer data to cold storage
inactive_threshold = "90d"  # No queries for 90 days
cold_storage_backend = "s3"

Detailed Results and Metrics

Cost Reduction:

Component	Before (Monthly)	After (Monthly)	Savings
850 RDS instances	$78,000	$0	$78,000
RDS backups	$4,200	$0	$4,200
HeliosDB compute	$0	$3,000	-$3,000
HeliosDB storage	$0	$1,200	-$1,200
Monitoring	$850	$50	$800
Total	$83,050	$4,250	$78,800

Annual Savings: $945,600

Provisioning Speed:

Stage	Before	After	Improvement
Infrastructure setup	45 minutes	2 seconds	99.93% faster
Schema initialization	60 minutes	8 seconds	99.78% faster
Backup configuration	15 minutes	0 seconds	100% (automatic)
Monitoring setup	30 minutes	0 seconds	100% (automatic)
Total	3 days	10 seconds	99.996% faster

Operational Improvements:

Schema migrations: 3 weeks → 15 minutes (all customers updated simultaneously)
Backup processes: 850 separate → 1 unified (with branch-specific restore)
Database upgrades: 3 weeks → 2 hours
Monitoring dashboards: 850 → 1 (with branch filtering)
Engineering time on database ops: 50% → 5%

New Features Enabled:

Competitive benchmarking: Customers compare their metrics to anonymized cohorts
Market insights: Aggregate trends across all customers (anonymized)
ML-powered recommendations: Models trained on cross-customer data
Premium analytics tier: $2M ARR from new features in 6 months

Business Impact:

Sales cycle: 45 days → 27 days (40% faster due to instant onboarding)
Customer time-to-value: 3 days → 10 seconds
Win rate: 32% → 44% (faster onboarding key differentiator)
Engineering velocity: 2x increase (freed from database ops)

Implementation Timeline

Week 1: Preparation and Tooling

Day 1-2: HeliosDB deployment and configuration
Day 3-4: Build automated migration tooling
Day 5-7: Schema normalization across 850 databases

Week 2: Pilot Migration

Day 8-9: Migrate 10 pilot customers to branches
Day 10-12: Validation and performance testing
Day 13-14: Go/no-go decision, refinement of tooling

Week 3: Bulk Migration

Day 15-19: Migrate 840 remaining customers (50 at a time)
Day 20-21: Final validation and cutover

Week 4: Decommission and Launch

Day 22-24: Shut down 850 RDS instances
Day 25-27: Launch cross-customer analytics features
Day 28: Documentation, training, retrospective

ROI Calculation

Investment:

HeliosDB license: $2,500/month ($30,000/year)
Infrastructure: $1,750/month ($21,000/year)
Migration engineering: 320 hours × $160/hour = $51,200
Training: $5,000
Total Year 1 Investment: $107,200

Returns (Year 1):

Infrastructure cost savings: $945,600
Engineering time reclaimed (50% → 5%): $540,000
New premium features revenue: $2,000,000
Sales velocity improvement (40% faster): $900,000
Total Year 1 Returns: $4,385,600

ROI Metrics:

Net benefit Year 1: $4,278,400
ROI: 3,991%
Payback period: 9 days
3-year projected benefit: $13.5M

Technical Deep-Dive

Branch-Based Isolation:

Each customer operates in a dedicated branch:

-- Provisioning a new customer (10 seconds)
CREATE BRANCH customer_acme_corp FROM main;

-- Application sets branch context per request
SET heliosdb.branch = 'customer_acme_corp';

-- All subsequent queries isolated to this customer's data
SELECT * FROM events WHERE user_id = $1;

-- Customer cannot access other branches (enforced by HeliosDB)
SET heliosdb.branch = 'customer_competitor';  -- Permission denied

Cross-Customer Analytics (Controlled):

Admin queries can aggregate across branches with permission:

-- Query aggregate metrics across all customer branches
-- Requires admin role with cross-branch permission
SELECT
    branch_name as customer,
    avg(revenue) as avg_revenue,
    count(*) as event_count
FROM heliosdb.cross_branch_query(
    query := 'SELECT sum(revenue) as revenue, count(*) FROM events WHERE date >= $1',
    branches := (SELECT array_agg(branch_name) FROM heliosdb.branches),
    params := ARRAY['2024-11-01']
)
GROUP BY branch_name;

-- Result used for anonymized competitive benchmarking feature

Automated Tiering for Inactive Customers:

-- Automatically move inactive customer data to cold storage
-- Configuration in TOML (shown earlier)

-- Example: Customer hasn't logged in for 90 days
-- HeliosDB automatically:
-- 1. Compresses branch data
-- 2. Moves to S3 cold tier
-- 3. Reduces hot storage costs by 90%

-- When customer logs back in:
-- 1. Automatically promotes data back to hot tier
-- 2. Transparent to application (slight latency on first query)

Resource Limits per Branch:

Prevent noisy neighbor problem:

-- Set resource limits for specific customer branch
ALTER BRANCH customer_acme_corp
SET max_connections = 20,
    max_memory = '2GB',
    max_cpu_percent = 25;

-- If customer exceeds limits, queries are throttled (not failed)
-- Prevents one customer from impacting others

Lessons Learned

What Went Well:

Branch model perfect for multi-tenancy: Logical isolation at fraction of cost
Migration tooling investment paid off: Automated 850 migrations flawlessly
Instant provisioning transformed sales: 40% faster deal velocity
Cross-customer features unlocked new revenue: $2M ARR in 6 months
Engineering morale improved: Team no longer manages 850 databases

Challenges and Solutions:

Schema normalization pre-migration: 850 databases had schema drift
- Solution: Spent 1 week normalizing all schemas before migration
Connection pooling complexity: Initial design used per-branch pools
- Solution: Switched to single pool with branch context setting
Monitoring migration: CloudWatch dashboards for 850 databases
- Solution: Rebuilt unified dashboard with branch filtering

Recommendations:

Normalize schemas across tenants before migration
Use branch context setting (not separate connections)
Build migration tooling early (don’t underestimate complexity)
Pilot with 10-20 small customers first
Plan for resource limits to prevent noisy neighbors

Conclusion

Unify360’s migration to HeliosDB’s branch-aware multi-tenancy transformed the business. The 94% cost reduction and instant customer provisioning resolved immediate operational pain, while cross-customer analytics unlocked $2M in new revenue.

Most importantly, the architecture positioned Unify360 for massive scale. The company can now onboard 10,000 customers with minimal infrastructure cost increase—a game-changer for unit economics and growth potential.

The engineering team shifted from reactive database operations to proactive product development, accelerating the roadmap and improving competitive positioning.

CASE_SAAS_005: Enterprise Workflow Automation Platform

1-Page Summary

Company Profile

Company: AutoFlow Enterprise (anonymized)
Industry: Workflow Automation (Zapier/Workato competitor)
Stage: Enterprise-focused (Series B, 75M raised)
Size: 220 employees, 450 enterprise customers
Annual Recurring Revenue: $48M

The Challenge AutoFlow’s workflow execution engine processed millions of tasks daily across complex integrations, requiring:

Real-time task orchestration with <50ms latency
Audit trails for SOC 2 and GDPR compliance
Multi-region deployment for data residency
PostgreSQL + Kafka + Redis architecture costing $95,000/month
12-person infrastructure team managing complexity
99.9% uptime (enterprise SLA: 99.99%)

The Solution Consolidated to HeliosDB with branch-aware compliance and multi-cloud:

HTAP for real-time execution + audit analytics
Branch snapshots for compliance and audit trails
Multi-cloud deployment (AWS + Azure + GCP)
10-week migration with zero-downtime cutover
5-person migration team

Key Results

82% infrastructure cost reduction: $95K/month to $17K/month
99.995% uptime achieved: 5x improvement from 99.9%
SOC 2 audit time reduced: 6 weeks to 2 weeks
Multi-region deployment simplified: 3 months to 3 days
40% task throughput increase: 50K/sec to 70K/sec

Executive Quote “HeliosDB’s branch-aware compliance features transformed our audit process from a nightmare to a breeze. We cut infrastructure costs by 82% and finally achieved the 99.99% uptime our enterprise customers demand.” — CTO, AutoFlow Enterprise

Full 3-Page Version

(Due to length constraints, this would continue with the same detailed structure as the previous case studies: Company Background, Detailed Challenge, Solution Architecture, Results, Timeline, ROI, Technical Deep-Dive, Lessons Learned, and Conclusion)

HeliosDB Case Study Template Library

HeliosDB Case Study Template Library

Table of Contents

Template Structure

SaaS Case Studies

CASE_SAAS_001: Series A Marketing Analytics Platform

1-Page Summary

Full 3-Page Version

CASE_SAAS_002: Series B Collaboration Platform

1-Page Summary

Full 3-Page Version

CASE_SAAS_003: Series C Developer Tools Platform

1-Page Summary

Full 3-Page Version

CASE_SAAS_004: Growth-Stage Customer Data Platform

1-Page Summary

Full 3-Page Version

CASE_SAAS_005: Enterprise Workflow Automation Platform

1-Page Summary

Full 3-Page Version