Scaling Issues — Products Ranked

•Egress costs stack fast - small apps jump from $25 to $100+

•Real-time subscription limits cause dropped connections

•Database hits connection limits as user base grows

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•Service jams with large datasets (30M+ docs)

•Only searches first 100k of 1M+ records

•Storage bloat: 10MB data becomes 200MB indexed

•Not designed for production scale (1M+ vectors)

•Single-node architecture - no distributed scaling

•Memory constraints - index must fit in RAM

•Painfully slow with multi-second page loads

•Third-party plugins cause thread exhaustion

•Performance issues - slows down websites

•Sites crash under moderate traffic

•Inventory management lacks depth

•Struggles past 100 employees

•50,000 record limit per base blocks growth

•API rate limit of 5 requests/second breaks integrations

•Large boards become extremely laggy and slow

•Uses excessive RAM, long launching times

•Limited reporting and analytics capabilities

•Not scalable for larger organizations

•Slow performance with large files

•Crashes and instability on Windows

•Slow rendering with complex scenes

•Viewport performance degrades with many objects

•Heavy software requiring expensive hardware

•Long render times for animation/games

•High memory usage and lag with extensions

•Struggles with large monorepo projects

•High RAM usage causes system slowdown

•Performance issues on low-spec machines

•Slow performance on larger projects

•Slower startup than StackBlitz

•AI loses consistency as projects grow

•Resource and memory limitations

•High RAM requirements - data must fit in memory

•Indexing only 65-100K docs/minute on large datasets

•Performance degrades at scale (18M+ objects)

•Memory usage grows heavily with data

•Database performance degrades at 5,000+ records

•Large boards become extremely slow

•Platform is extremely slow and buggy

•Not suited for teams over 100 people

•Not suitable for large enterprises

•Limited customization restricts growing businesses

•Advanced features locked behind expensive tiers

•Slow site speed affecting SEO and conversions

•Slow page speed affecting SEO and user experience

•Slow page loading speeds hurt user experience

•User limit per plan forces expensive upgrades

•Expensive for growing teams - costs escalate quickly

•BBB complaints show pattern of unresolved issues

•Global payroll operates in disconnected silos

•Large files cause severe performance issues and crashes

•Costs escalate quickly as agents are added

•Memory hog - CPU constantly spiking at 50-60%

•Data manipulation becomes cumbersome at scale

•Slow performance with large candidate pools

•Lag and sync issues with large volumes or complex pipelines

•Event and MTU limits hit unexpectedly fast

•Daily session limits stop recording mid-day

•Serverless function timeouts at high load

•Limited integrations with only 15 major platforms

•Automatic tier upgrades create billing surprises

•Index optimization required to avoid high row reads

•Limited report customization frustrates data-driven teams

•Platform struggles when scaling past 100 employees

•Fewer integrations than enterprise competitors

•Severe lag with large files and 50+ artboards

•Resource-heavy Mac app consumes significant system resources

•Studio performance lag on complex prototypes

•Severe lag with large files and many layers

•High memory and resource consumption

•Requires high-end hardware to run properly

•Hardware demands are excessive

•Massive disk space consumption

•Processing slows during peak hours

•Heavy storage drain and memory issues

•Layer limits based on iPad RAM frustrate complex projects

•Slow performance with large or complex files

•Slow RAW file processing

•Severe slowdowns with complex or large files

•Performance degrades with complex scenes

•Requires expensive high-spec hardware

•Requires powerful hardware to avoid crashes

•CPU bottleneck causes lag

•Requires high-end hardware

•Performance degrades in complex projects

•Physics engine is under-optimized

•LSP failures in large monorepos

•Multiple projects cause severe slowdowns

•Large projects cause severe performance issues

•Very slow with many databases

•Large repositories cause slowdowns

•Slows down with large repositories

•High memory usage on large codebases

•Large repositories require iterative prompting

•Platform is extremely slow and laggy

•Limited flexibility for GPU workflows

•No BYOC (Bring Your Own Cloud) support

•Kubernetes node creation limits

•Cost optimization requires dedicated expertise

•Costs escalate quickly with scale

•Costs can get quickly out of control as you grow

•Consumes more storage than relational databases

•Cold starts plague Cloud Functions

•Resource intensive - high CPU, memory, and storage consumption

•Significant RAM requirements

•Scaling during peak loads presents challenges

•Shard management problems at scale

•Costs become expensive at scale

•Initial ingestion hiccups at large scale

•Rate limiting throttles production apps

•API rate limits frustrate production apps

•Context window glitches with long conversations

•128K context window too small for documents

•GPU queue times reach 40+ minutes on busy days

•SD3 API costs 3.4x more than SDXL

•Inconsistent results affect agency client quality