High-Bandwidth Use Cases Now Cost-Effective on Private Cloud

Public cloud egress pricing makes bandwidth-intensive applications prohibitively expensive at scale. Video streaming platforms, software distribution services, email infrastructure, and real-time data APIs all generate massive data transfer volumes. Per-GB charges that seem reasonable for small deployments become business-limiting constraints as traffic grows.

Organizations running high-bandwidth workloads face a choice: pay escalating egress fees that can reach hundreds of thousands of dollars monthly, or implement complex optimization strategies that degrade user experience and limit features.

OpenMetal’s recent bandwidth increase changes the economics dramatically. Configurations that previously included 1-2Gbps per server now provide 4-10Gbps per server, with corresponding increases in included data transfer. A Large V4 hosted private cloud now includes approximately 3,797TB of monthly egress at no additional charge, worth roughly $228,000-$342,000 at AWS rates.

This substantial increase in included bandwidth makes previously expensive use cases cost-effective on private cloud infrastructure. Applications that required careful bandwidth management or faced prohibitive egress costs now run efficiently within included allocations.

This article examines ten high-bandwidth use cases that benefit from OpenMetal’s enhanced bandwidth allocations, with specific cost comparisons, architecture guidance, and implementation considerations for each workload type.

Understanding the Bandwidth Economics Shift

Before diving into specific use cases, understand what changed and why it matters for bandwidth-intensive applications.

The Public Cloud Bandwidth Tax

Public cloud providers charge per-GB for data transfer out of their networks. AWS charges $0.09/GB for the first 10TB monthly, $0.085/GB for the next 40TB, and $0.07/GB for transfer above 150TB. These rates apply to every gigabyte your application sends to users. AWS does provide 100 GB of free data transfer out to the internet monthly (increased from 1 GB in 2024), aggregated across all services and regions. However, this modest allowance is quickly exhausted by production applications. Once you exceed 100 GB, the tiered pricing structure applies to all additional transfer.

A video streaming platform transferring 500TB monthly pays approximately $37,500 in AWS egress fees alone. That’s $450,000 annually just for bandwidth, separate from compute, storage, and other infrastructure costs. 

The bandwidth tax compounds as applications scale. Double your user base, double your egress costs. Launch in new markets, increase egress costs proportionally. Success becomes expensive.

OpenMetal’s Included Bandwidth Model

OpenMetal includes substantial bandwidth with every server. The recent increases mean:

Medium V4: 2Gbps per server × 3 servers = 6Gbps total (~1,898TB monthly included)

Large V4: 4Gbps per server × 3 servers = 12Gbps total (~3,797TB monthly included)

XL V4: 6Gbps per server × 3 servers = 18Gbps total (~5,695TB monthly included)

XXL V4: 10Gbps per server × 3 servers = 30Gbps total (~9,491TB monthly included)

These allocations include the data transfer capacity. No per-GB charges, no surprise bills, no complex cost optimization required for applications staying within included limits.

For usage beyond included amounts, OpenMetal uses 95th percentile billing that samples bandwidth every five minutes, discards the top 5% of measurements, and bills based on sustained usage rather than every gigabyte transferred. This approach typically costs 60-80% less than per-GB charging even when additional bandwidth charges apply.

Why This Matters for Application Architecture

With public cloud’s per-GB pricing, every architectural decision revolves around minimizing egress. You implement complex caching layers, compress data aggressively at the cost of CPU cycles, serve lower quality assets, or limit features that would increase bandwidth consumption.

With substantial included bandwidth, architectural decisions optimize for user experience and application performance rather than cost avoidance. You can serve higher quality content, implement real-time features, provide comprehensive APIs, or enable data-intensive functionality without constant cost anxiety.

Video Streaming and Media Delivery

Video streaming represents one of the most bandwidth-intensive use cases in modern infrastructure. The combination of large file sizes, continuous streaming, and user expectations for high quality creates massive data transfer requirements.

Bandwidth Requirements for Video Streaming

Video quality determines bandwidth consumption per stream:

1080p HD video: Approximately 5 Mbps per stream (2.25GB per hour)

4K UHD video: Approximately 25 Mbps per stream (11.25GB per hour)

Adaptive bitrate streaming: Variable rates based on connection quality (3-15 Mbps typical)

A modest streaming platform with 10,000 concurrent viewers watching 1080p content consumes 50Gbps of sustained bandwidth. That’s 16,200TB monthly if streams average 3 hours daily per viewer.

Cost Comparison: AWS vs OpenMetal

AWS costs for 16,200TB monthly egress:

• First 10TB: $900 @ $0.09/GB
• Next 40TB: $3,400 @ $0.085/GB
• Next 100TB: $7,000 @ $0.07/GB
• Remaining 16,050TB: $112,350 @ $0.07/GB
• Total monthly egress: $123,650
• Annual egress cost: $1,483,800

OpenMetal Large V4 (scaled to 5 clouds for capacity):

• Monthly infrastructure: $21,490 (5 × $4,298)
• Included egress: 18,985TB (5 clouds × 3,797TB each)
• Usage beyond included: 0TB (16,200TB < 18,985TB included)
• Total monthly cost: $21,490
• Annual cost: $257,880

The OpenMetal deployment costs $257,880 annually including all infrastructure, while AWS egress alone costs $1,483,800. The bandwidth savings exceed $1.2 million annually, and that’s before considering AWS compute and storage costs.

Practical Implementation: Streaming Platform Architecture

A video streaming platform on OpenMetal’s infrastructure uses multiple Cloud Cores to distribute traffic and provide redundancy. This architecture is particularly well-suited for SaaS providers delivering video content to large user bases:

Origin servers handle video transcoding, storage, and initial delivery. These run on Large V4 or XL V4 configurations with substantial CPU for transcoding workloads and NVMe storage for fast asset access.

Edge caching layers run on additional Cloud Cores positioned geographically close to user populations. These servers cache frequently accessed content, reducing load on origin servers and improving user experience through lower latency.

Load balancing distributes incoming connections across available servers, ensuring no single server becomes a bottleneck and providing failover capability if hardware issues occur.

With 4Gbps per server on Large V4 hardware, each three-server cloud handles approximately 800 concurrent 1080p streams comfortably. Five clouds provide capacity for 4,000 concurrent viewers with substantial headroom for growth and traffic spikes.

The architecture scales horizontally by adding Cloud Cores as viewership grows, maintaining predictable costs while serving more concurrent streams.

Content Delivery and Software Distribution

Organizations distributing software, game assets, mobile application updates, or large files benefit enormously from OpenMetal’s bandwidth increases. These workloads are common among hosting and public cloud providers and generate traffic spikes during release events but maintain lower baseline usage between updates.

The Software Distribution Challenge

Modern software distribution involves delivering large files to many users simultaneously:

AAA game releases: 80GB+ download sizes

Mobile application updates: 200MB-2GB per update pushed to millions of devices

Enterprise software: Multi-gigabyte installer packages for business applications

Container images: Large Docker images pulled frequently during CI/CD operations

A game studio releasing a 75GB title to 50,000 players on launch day transfers 3,750TB in 24-48 hours. This concentrated traffic creates massive egress bills on public cloud while straining bandwidth allocations.

Public Cloud’s Burst Problem

Public cloud per-GB pricing penalizes traffic bursts. Your game release day costs the same per GB whether spread across a month or concentrated in 48 hours. The studio pays approximately $262,500 in AWS egress charges for that single release at $0.07/GB.

OpenMetal’s 95th percentile billing discards the top 5% of bandwidth measurements. Brief spikes during release windows don’t inflate monthly costs because those peak measurements get excluded from billing calculations. You pay for sustained usage patterns rather than being penalized for temporary traffic increases.

Cost Analysis: Game Distribution Platform

Consider a game distribution platform handling monthly releases plus ongoing updates:

Monthly distribution volumes:

• New release (first week): 3,750TB
• Patch updates: 500TB
• DLC content: 250TB
• Total monthly: 4,500TB

AWS egress costs:

• Monthly cost: $315,000 @ $0.07/GB average
• Annual cost: $3,780,000

OpenMetal XL V4 deployment (2 clouds for capacity):

• Monthly infrastructure: $14,300 (2 × $7,150)
• Included egress: 11,390TB (2 clouds × 5,695TB each)
• Usage beyond included: 0TB (4,500TB < 11,390TB included)
• Monthly cost: $14,300
• Annual cost: $171,600

The OpenMetal deployment saves $3,608,400 annually compared to AWS egress costs alone. The infrastructure supporting this distribution volume costs less than 5% of what AWS charges just for bandwidth.

Architecture for High-Volume Distribution

Software distribution platforms benefit from geographic distribution combined with intelligent caching:

Master repository stores authoritative copies of all distributable assets on XL V4 or XXL V4 hardware with substantial storage capacity and processing power for integrity checking.

Regional edge nodes cache frequently accessed assets closer to users, reducing latency and distributing bandwidth load across multiple locations. Each edge node runs on Large V4 or XL V4 hardware depending on regional traffic volumes.

Torrent/P2P hybrid for large releases optionally implements peer-assisted distribution where users help distribute content to other users, dramatically reducing infrastructure bandwidth requirements during major releases while maintaining fast downloads.

The architecture handles 10Gbps sustained bandwidth with XXL V4 hardware, supporting simultaneous downloads from thousands of users without throttling or degraded performance.

High-Volume Email Infrastructure

Email service providers and organizations running their own email infrastructure face unique bandwidth requirements. While individual emails are small, the volume compounds quickly when sending transactional emails, marketing campaigns, or providing email hosting services.

Email Bandwidth Characteristics

Email infrastructure generates bidirectional traffic with specific patterns:

Outbound email: Marketing campaigns, transactional emails, newsletters

Inbound email: User-generated messages, automated system notifications

SMTP traffic: Protocol overhead adds 20-30% to raw message sizes

Attachment handling: Large attachments can dominate bandwidth usage

An email service provider sending 10 million messages daily with average message size of 50KB (including attachments and protocol overhead) transfers approximately 500GB daily or 15TB monthly just for outbound delivery.

Why Public Cloud Restricts Email Operations

Major public cloud providers actively discourage or prohibit operating email infrastructure on their platforms. AWS restricts SMTP traffic on default instances, requires approval for dedicated IP addresses, and imposes strict rate limits on email sending. These restrictions stem from abuse concerns but create fundamental problems for legitimate email operations.

Organizations need dedicated IP addresses with established sender reputation for reliable email delivery. Building and maintaining sender reputation requires consistent sending patterns from static IPs. Public cloud’s restrictions make this impractical.

OpenMetal’s Advantage for Email Infrastructure

OpenMetal provides dedicated hardware with full control over IP addressing. You can configure reverse DNS properly, establish sender reputation, implement DKIM and SPF records correctly, and send email at whatever volume your business requires without arbitrary platform restrictions.

The bandwidth increases mean email providers can scale sending capacity without worrying about egress costs destroying unit economics.

Cost Comparison: Email Service Provider

Consider an ESP handling 300 million messages monthly:

Traffic analysis:

• Outbound messages: 300 million @ 50KB average = 15,000TB
• Inbound messages (bounces, replies): 20 million @ 30KB average = 600TB
• Total monthly bandwidth: 15,600TB

AWS costs (if they allowed this operation):

• Monthly egress: $1,092,000 @ $0.07/GB
• Dedicated IP addresses: $3,600 (100 IPs @ $3.60/month each)
• Rate limit approval process: Substantial time investment
• Total monthly: $1,095,600

OpenMetal XXL V4 deployment (2 clouds):

• Monthly infrastructure: $20,476 (2 × $10,238)
• Included egress: 18,982TB (2 clouds × 9,491TB each)
• Usage beyond included: 0TB (15,600TB < 18,982TB included)
• Dedicated IPs: Included with BYOIP
• Total monthly: $20,476

The OpenMetal deployment costs $20,476 monthly compared to AWS’s theoretical $1,095,600, saving over $1 million monthly or $12.9 million annually.

Real-Time Data APIs and Telemetry Services

Organizations providing real-time data services through APIs like stock market data, weather information, IoT telemetry aggregation, or sports scores generate substantial bandwidth through constant, small updates to many consumers.

API Traffic Patterns

Real-time data APIs differ from traditional request/response patterns:

WebSocket connections: Persistent connections maintaining state and pushing updates as data changes

Frequent polling: Clients requesting updates every few seconds or minutes

Small message sizes: Individual updates are typically small (1-10KB) but accumulate quickly

High request volumes: Popular APIs handle millions of requests per hour

A financial data API serving real-time stock quotes to 50,000 concurrent users pushing updates every second transfers approximately 1.5TB daily or 45TB monthly just for the data payload, before protocol overhead.

Why Real-Time APIs Struggle on Public Cloud

Public cloud pricing creates tension between providing real-time updates and managing costs. Each update pushed to clients incurs egress charges. The more frequently you update, the higher your bandwidth costs.

This pricing pressure encourages batching updates, reducing update frequency, or implementing complex optimization strategies that degrade user experience. Your architecture optimizes for minimizing egress rather than providing the best possible service.

Bandwidth Requirements for Data Services

Real-time data services scale based on user count and update frequency:

Stock market data API: 100,000 concurrent connections, 1KB updates every 2 seconds = 43TB daily

Weather service API: 50,000 concurrent connections, 2KB updates every 60 seconds = 7.2TB daily

IoT telemetry aggregation: 1 million devices, 500 bytes every 5 minutes = 21.6TB daily

Sports scores API: 200,000 concurrent users during games, 500 bytes every 10 seconds = 86TB daily during peak season

These volumes remain well within OpenMetal’s included bandwidth for Large V4 or XL V4 configurations, enabling real-time updates without cost anxiety.

Architecture for Real-Time Data Distribution

Real-time data services benefit from architecture optimized for connection handling and message distribution on OpenStack infrastructure:

WebSocket servers maintain persistent connections to clients, handling connection lifecycle and message routing. These run on Large V4 hardware with substantial CPU for connection management.

Data processing pipeline ingests raw data from upstream sources, processes and formats it for distribution, and publishes to WebSocket servers. This component typically runs on separate hardware optimized for processing rather than connection handling.

Redis or similar provides pub/sub infrastructure for routing messages between processing pipeline and WebSocket servers, enabling horizontal scaling without complex message routing logic.

The architecture handles 100,000+ concurrent connections per Large V4 cloud, scaling horizontally by adding clouds as connection counts grow.

Backup and Disaster Recovery Services

Organizations providing backup services or operating their own disaster recovery infrastructure generate substantial bandwidth during backup windows and restoration operations. The bandwidth requirements are predictable but can be massive during initial backups or large-scale recovery scenarios.

Backup Traffic Patterns

Backup operations create distinct traffic patterns:

Initial backup: Large one-time transfer of existing data to backup infrastructure

Incremental backups: Smaller regular transfers capturing changes since last backup

Restoration operations: Potentially massive transfers when restoring from backup

Replication traffic: Continuous replication between primary and backup sites

An organization backing up 500TB of data initially transfers that full volume, then maintains incremental backups of 10-50TB daily depending on change rate. Restoration operations can transfer the entire dataset if recovering from complete system failure.

Why Backup Economics Matter

Backup service providers face challenging economics on public cloud. You’re charging customers based on storage volume (usually a few dollars per TB monthly) but paying egress fees if customers restore their data.

AWS charges $0.09/GB to restore data from S3, meaning a customer restoring 10TB pays you perhaps $30-50 monthly for storing that data but you pay AWS $900 in egress charges to return it to them. The unit economics don’t work.

OpenMetal’s Value for Backup Infrastructure

With substantial included egress, backup providers can offer restoration services without eating egress costs that exceed storage revenue. Customers can restore their data without surprise charges, and providers maintain healthy margins using Ceph storage clusters.

Cost Analysis: Backup Service Provider

Consider a backup provider managing 5PB of customer data:

Monthly bandwidth:

• Initial backups (new customers): 100TB
• Incremental backups: 500TB
• Restoration operations: 50TB
• Total monthly: 650TB

AWS S3 costs:

• Storage: $115,000 @ $0.023/GB/month (S3 Standard)
• Egress (restorations): $4,500 @ $0.09/GB
• Total monthly: $119,500

OpenMetal Large V4 storage cluster:

• Monthly infrastructure: $4,298
• Storage capacity: Substantially exceeds 5PB with proper configuration
• Included egress: 3,797TB (650TB < included)
• Total monthly: $4,298

The OpenMetal deployment saves $115,202 monthly or $1,382,424 annually while providing customers with fast restoration operations and no egress surprise charges.

Gaming Servers and Multiplayer Infrastructure

Gaming infrastructure generates substantial bandwidth through player connections, game state synchronization, voice chat, and asset streaming. Modern multiplayer games can consume 100-300 Mbps per server instance depending on player count and game type.

Gaming Bandwidth Requirements

Different game types have different bandwidth profiles:

First-person shooters: 10-15KB per player per second for position updates, hit detection, and game state

MMORPGs: 5-8KB per player per second for less frequent updates over larger maps

Battle royale: Variable bandwidth based on proximity to other players

Racing games: 8-12KB per player per second for position and physics synchronization

A server hosting 64 players in a fast-paced shooter consumes approximately 800KB per second or 3 Mbps for game state updates. Add voice chat (64kbps per player compressed), asset streaming, and protocol overhead, and bandwidth usage reaches 5-10 Mbps per server instance.

Multiple Game Servers Per Hardware

Modern dedicated servers run multiple game server instances per physical or virtual machine. A Large V4 Cloud Core with 142 VMs at 80% capacity can host 100+ game server instances, each serving 32-64 players.

100 game servers at 7 Mbps average consumes 700 Mbps sustained bandwidth. With 12Gbps available on Large V4, the deployment handles 1,700 game server instances comfortably, supporting 54,000-108,000 concurrent players.

Cost Comparison: Game Hosting Provider

Consider a game hosting provider running 500 game servers:

Monthly bandwidth:

• 500 servers @ 7 Mbps average = 3.5 Gbps sustained
• Monthly transfer: ~1,134TB

AWS costs:

• Compute (500 t3.medium instances): $15,330
• Egress: $79,380 @ $0.07/GB average
• Total monthly: $94,710

OpenMetal Large V4 (2 clouds):

• Monthly infrastructure: $8,596 (2 × $4,298)
• Included egress: 7,594TB (1,134TB < included)
• Capacity: 280 VMs supporting 500+ game servers
• Total monthly: $8,596

The OpenMetal deployment saves $86,114 monthly or $1,033,368 annually while providing dedicated hardware with consistent performance and no noisy neighbor problems affecting game server latency.

AI Model Serving and Inference APIs

AI applications generate substantial bandwidth when serving model predictions through APIs. Large language models, image generation, and computer vision services return sizeable responses to client requests.

AI Inference Bandwidth Characteristics

AI inference services have unique bandwidth profiles:

Text generation: 1-5KB per token generated, varying by model and response length

Image generation: 500KB-5MB per generated image depending on resolution and format

Computer vision: 100KB-1MB per analyzed image including annotations and metadata

Voice synthesis: 1-5MB per minute of generated audio

An API serving 10 million text generation requests monthly with average response size of 2KB transfers 20TB. Image generation at 100,000 requests monthly with 2MB average response size adds another 200TB.

Model Inference Economics

AI inference providers charge per API call or per token generated. Bandwidth costs directly impact profit margins because they’re proportional to usage but not always proportional to revenue.

A customer paying $0.002 per 1,000 tokens (typical LLM pricing) generates $2 revenue for a 1 million token request. If that request returns 20MB of JSON (including context, metadata, and completions), egress costs $1.80 on AWS at $0.09/GB, consuming 90% of gross revenue before considering compute costs.

Cost Analysis: AI Inference API

Consider an AI service provider handling mixed workloads:

Monthly request volumes:

• Text generation: 50 million requests @ 3KB average = 150TB
• Image generation: 500,000 requests @ 3MB average = 1,500TB
• Total monthly: 1,650TB

AWS costs:

• Compute (GPU instances, p3.2xlarge equivalent): $45,000
• Egress: $115,500 @ $0.07/GB average
• Total monthly: $160,500

OpenMetal GPU Server (1× NVIDIA H100 PCIe):

• Monthly infrastructure: $2,995.20/month (1× H100 80GB HBM3)
• CPU: 2× Intel Xeon Gold 6530, 64C/128T
• Memory: 1024GB DDR5
• Storage: 1× 6.4TB NVMe, 2× 960GB boot disks
• Included egress: Approximately 380TB (1Gbps per server)
• Additional servers for scale: 5 total servers = $14,976/month
• Total included egress: 1,898TB (5 servers × ~380TB each)
• Usage: 1,650TB < 1,898TB included
• Total monthly: $14,976

Savings: $145,524 monthly or $1,746,288 annually (91% reduction)

The bandwidth savings of $115,500 monthly combined with significantly lower GPU infrastructure costs dramatically improve unit economics for AI inference providers, enabling more competitive pricing or better profit margins. OpenMetal’s dedicated NVIDIA H100 servers provide consistent performance without the noisy neighbor problems common in shared cloud GPU instances.

Container Registries and CI/CD Infrastructure

Organizations running their own container registries for internal use or providing registry services to external customers face substantial bandwidth requirements. These workloads are essential for teams running Kubernetes workloads and modern application deployments. Container images are large, and CI/CD pipelines pull images frequently.

Container Image Traffic Patterns

Container registries generate bandwidth through several operations:

Image pushes: Developers and CI systems pushing new image versions to registry

Image pulls: Deployment systems, CI runners, and local development environments pulling images

Layer deduplication: Intelligent registries minimize transfer by reusing layers but still transfer unique layers

Manifest checks: Frequent verification requests generating small amounts of traffic

A typical microservices deployment with 50 services, each building twice daily and deploying to 10 environments, generates approximately 1,000 image pulls daily. At 500MB average image size, that’s 500GB daily or 15TB monthly just for deployment operations.

CI/CD Pipeline Bandwidth

Modern CI/CD pipelines pull container images repeatedly:

Build containers: Each CI job pulls build environment images

Test containers: Integration tests spin up dependencies as containers

Deployment artifacts: Final built images pushed to deployment environments

Cache invalidation: Updates to base images propagate through dependent images

An active development team running 500 CI jobs daily, each pulling 2-3 container images averaging 300MB, transfers 300-450GB daily in CI operations alone.

Architecture for Container Registry

Container registries benefit from caching layers and geographic distribution:

Primary registry stores authoritative image layers on XL V4 or XXL V4 with substantial storage and processing power for image scanning and vulnerability analysis.

Pull-through caches in each region or data center cache frequently accessed layers, reducing load on primary registry and improving pull performance for local consumers.

Garbage collection regularly removes unused layers to manage storage growth while maintaining available capacity for new images.

The architecture handles thousands of concurrent pulls, supporting large development teams or multi-tenant registry services without throttling or performance degradation.

CDN and Edge Computing Services

Organizations operating content delivery networks or edge computing infrastructure generate massive bandwidth serving content to end users from geographically distributed locations.

CDN Traffic Characteristics

CDN operations generate traffic through content delivery to end users:

Static assets: Images, JavaScript, CSS, fonts

Video streams: Transcoded video segments for adaptive streaming

API proxying: Caching API responses at edge locations

Dynamic content acceleration: Routing and optimizing dynamic requests

A small CDN serving 10TB daily across 10 edge locations transfers 100TB daily or 3,000TB monthly. Larger CDN operations easily reach petabyte-scale monthly traffic.

CDN Economics on Private Infrastructure

Public cloud CDN services charge per-GB with volume discounts. CloudFront charges $0.085/GB for the first 10TB, decreasing to $0.020/GB above 5PB monthly. While cheaper than compute egress, these costs still accumulate substantially.

Operating your own edge infrastructure on OpenMetal hardware with included egress transforms economics. You control the full stack, implement aggressive caching strategies, and serve content without per-GB charges for traffic within included bandwidth allocations.

Multi-Region CDN Architecture

CDN infrastructure distributes content globally:

Origin servers in primary data centers store authoritative content and handle cache misses. These run on XL V4 or XXL V4 with substantial storage and bandwidth capacity, often utilizing object storage for efficient content delivery.

Edge caches in each region cache popular content close to users, reducing latency and bandwidth load on origin servers. Each edge location runs Medium V4 or Large V4 depending on regional traffic volumes.

DNS-based routing directs users to nearest edge location based on geographic proximity and server availability.

With 30Gbps available on XXL V4 hardware, a primary origin location handles 9,491TB monthly of cache misses and origin requests. Edge locations with Large V4 hardware handle 3,797TB monthly each, supporting substantial traffic volumes per location.

Data Analytics Platforms and Reporting Services

Organizations providing data analytics platforms or reporting services generate bandwidth through query results, exported reports, and dashboard updates delivered to customers. These platforms often support big data infrastructure workloads requiring substantial data processing and transfer capabilities.

Analytics Bandwidth Patterns

Analytics platforms generate traffic through several operations:

Dashboard loads: Initial data for visualizations and reports

Report exports: Large CSV, Excel, or PDF files containing query results

Real-time updates: Streaming query results as data changes

Data extracts: Bulk exports for integration with other systems

A multi-tenant analytics platform with 1,000 customers, each running 50 dashboard loads and 10 exports daily, transfers approximately 100TB monthly in result delivery.

The Export Cost Problem

Analytics platforms often charge based on data volume stored or processed, not bandwidth consumed. A customer paying $500 monthly for analytics on 1TB of data might export 500GB of results that cost you $45 in AWS egress charges. That’s 9% of revenue consumed by bandwidth costs before any infrastructure or operational expenses.

OpenMetal Advantage for Analytics

With included egress, analytics platforms can offer unlimited exports, real-time dashboard updates, and bulk data extraction without worrying that customer usage patterns will destroy unit economics.

Customers value the ability to extract and analyze their data freely. Removing bandwidth constraints differentiates your platform and enables more generous usage policies.

Implementation Guide: Migrating Bandwidth-Intensive Workloads

Moving bandwidth-intensive applications to OpenMetal requires planning but delivers immediate cost benefits and operational improvements.

Step 1: Measure Current Bandwidth Usage

Document actual bandwidth consumption patterns:

Peak bandwidth: Maximum sustained transfer rate during busy periods

Monthly volume: Total data transferred per month

Traffic patterns: Understanding daily, weekly, and seasonal variations

Directionality: Ratio of inbound to outbound traffic

Use this baseline to select appropriate OpenMetal configuration. Applications transferring 2,000TB monthly fit comfortably on Large V4. Applications exceeding 5,000TB monthly require XL V4 or XXL V4.

Step 2: Design Infrastructure Architecture

Plan how to distribute workload across OpenMetal hardware:

Single Cloud Core: Applications with bandwidth requirements under configuration limits

Multiple Cloud Cores: Large applications distributing traffic across multiple deployments

Geographic distribution: Applications benefiting from edge caching or regional presence

Scaling strategy: Plan for adding capacity as bandwidth requirements grow using cloud expansion options

Consider redundancy and failover requirements. Many bandwidth-intensive applications benefit from running multiple Cloud Cores for both capacity and reliability.

Step 3: Test with Proof of Concept

Validate performance with real traffic before full migration:

Deploy test environment: Set up OpenMetal infrastructure matching planned production architecture

Generate realistic load: Use load testing tools to simulate production traffic patterns

Measure performance: Confirm bandwidth, latency, and throughput meet requirements

Validate costs: Verify bandwidth stays within included allocations or understand expected 95th percentile charges

OpenMetal offers free 30-day trials for proof of concept testing, eliminating financial risk during validation.

Step 4: Execute Migration

Move production traffic to OpenMetal infrastructure:

Gradual cutover: Start with small percentage of traffic, increasing as confidence grows

DNS updates: Shift traffic through DNS changes with appropriate TTL management

Monitoring: Watch bandwidth usage, application performance, and error rates closely

Rollback plan: Maintain ability to revert to previous infrastructure if issues emerge

Most organizations complete migration in 2-4 weeks depending on application complexity and data volume.

Step 5: Optimize and Scale

After migration, optimize for new infrastructure capabilities and reduce cloud costs:

Remove bandwidth optimizations: Eliminate complex caching or compression strategies implemented purely for cost control

Improve user experience: Serve higher quality content, implement real-time features, or reduce artificial limitations

Scale efficiently: Add capacity through additional Cloud Cores as traffic grows

Monitor economics: Track actual bandwidth usage against projections and adjust capacity as needed

Wrapping Up: Bandwidth No Longer Limits Your Business

OpenMetal’s bandwidth increases fundamentally change what’s economically viable on private cloud infrastructure. Use cases that previously required expensive public cloud deployments or complex multi-provider architectures now run cost-effectively on straightforward private cloud configurations.

Video streaming platforms serve thousands of concurrent viewers without egress costs destroying margins. Software distribution services handle major releases without bandwidth bills spiking into six figures. Email providers scale sending operations without artificial platform restrictions or prohibitive egress charges.

The architecture simplifications matter as much as the cost savings. You can optimize for user experience and application performance rather than bandwidth cost avoidance. Remove complex caching layers implemented purely for cost control. Serve higher quality content. Implement real-time features. Enable generous API access policies with hosted private cloud infrastructure.

For organizations currently running bandwidth-intensive workloads on public cloud, the economics justify serious evaluation of private cloud alternatives. The bandwidth savings alone often exceed total OpenMetal infrastructure costs, making migration immediately profitable while improving operational characteristics.

Calculate your specific bandwidth costs and compare against OpenMetal’s included allocations. For most high-bandwidth applications, the economics strongly favor private cloud infrastructure with substantial included egress over public cloud’s per-GB charging model.

Ready to understand how OpenMetal’s infrastructure supports your bandwidth-intensive workloads? Use the cloud deployment calculator to explore configurations and included bandwidth, or contact OpenMetal’s team to discuss your specific requirements and bandwidth patterns.