LLM Market Flashcards
Llama 2 (Meta
Llama 2 (Meta)
Open Sorce
Strengths:
Free for commercial use
Strong performance/cost ratio
Multiple size variants (7B-70B)
Large developer community
Limitations:
Requires significant compute
Less suited for regulated industries
No official support
Dated training cutoff
Mistral AI
Mistral
Open Source
Strengths:
Exceptional performance for size
Open weights
Commercial friendly license
Efficient architecture
Limitations:
Limited training details
Newer, less proven
Limited enterprise support
Smaller context window
BLOOM
Bloom
Open Source
Strengths:
Multilingual capabilities
Open science approach
Community-driven
Transparent development
Limitations:
Higher resource requirements
Less competitive performance
Limited commercial adoption
Slower inference
GPT-4 (OpenAI)
GPT-4 (OpenAI)
Commercial
Strengths:
Best-in-class performance
Strong safety measures
Wide API adoption
Regular updates
Limitations:
High cost
Limited customization
Closed architecture
Usage restrictions
Anthropic
Claude 3 (Anthropic)
Commercial
Strengths:
Strong reasoning capabilities
Constitutional AI focus
Long context window
High accuracy
Limitations:
Premium pricing
Limited model variants
Newer to market
Less ecosystem integration
PaLM 2 (Google)
PaLM 2 - Google
Commercial
PaLM 2 (Google)
Strengths:
Enterprise-grade security
Strong multilingual support
Vertex AI integration
Customization options
Limitations:
Limited public access
GCP platform lock-in
Higher latency
Complex pricing
Claude 2 (Anthropic)
Claude 2 (Anthropic)
Strengths:
Strong safety features
Good reasoning capabilities
Clear limitations disclosure
Professional focus
Limitations:
Limited customization
Higher costs
Platform dependencies
Limited fine-tuning
Jurassic-2 (AI21 Labs)
Jurassic-2 (AI21 Labs)
Commercial
Strengths:
Domain specialization
Custom model options
Strong multilingual
Enterprise focus
Limitations:
Smaller market share
Less community support
Higher costs
Limited ecosystem
Azure OpenAI Models
Enterprise-Focused Models
Azure OpenAI Models
Strengths:
Enterprise security
Azure integration
Compliance features
Support infrastructure
Limitations:
Azure lock-in
Application process
Higher costs
Limited customization
AWS Titan
AWS Titan
Strengths:
AWS integration
Cost-effective
Security features
Scalability
Limitations:
Performance gap
Limited features
AWS dependency
Newer offering
Cohere Command
Specialized Models
Cohere Command
Strengths:
Enterprise focus
Custom training
Document processing
API-first design
Limitations:
Niche market
Limited general tasks
Higher costs
Less flexibility
BERT
Specialized Model
BERT (Google)
Strengths:
Strong NLU capabilities
Research standard
Wide adoption
Open source
Limitations:
Not generative
Older architecture
Limited context
Resource intensive
Market Trends
Current Dynamics
Move toward specialized models
Increasing open-source adoption
Focus on efficiency
Enterprise customization
Key Differentiators
Context window size
Inference cost
Fine-tuning capabilities
Safety features
Selection Criteria
Use case requirements
Budget constraints
Security needs
Integration needs
Enterprise Considerations
Security & Compliance
Data handling
Model transparency
Audit capabilities
Deployment options
Cost Structure
Training costs
Inference costs
Integration costs
Support costs
Integration Options
API access
Cloud platforms
On-premises
Hybrid deployments
Future Outlook
Emerging Trends
Smaller, efficient models
Domain specialization
Enhanced customization
Hybrid approaches
Key Developments
Improved reasoning
Lower compute needs
Better multilingual
Enhanced safety
