AI Models & Platforms

OpenAI, Anthropic, Azure, Google, and Open Source - we know the differences and build the right custom AI infrastructure for your use case

Last updated: January 2, 2025

Short answer: Elasticbrains develops AI solutions with leading LLM platforms - OpenAI (GPT-4), Anthropic (Claude), Google (Gemini), Microsoft Azure OpenAI, and open-source models like Mistral or LLaMA. Model choice depends on factors like quality, cost, latency, data protection requirements, and vendor lock-in. We provide neutral consulting and often implement multi-provider architectures that can flexibly switch between models.

Which AI Model Is Right?

The AI landscape evolves rapidly. New models appear monthly, and the differences between OpenAI, Anthropic, Google, and open-source alternatives are often opaque to decision-makers. Whether you need a Custom AI Platform, an Enterprise AI Solution, or a self-hosted AI Software Platform: we help with selection and rely on architectures not bound to a single vendor.

Leading AI Platforms Overview

OpenAI

Market Leader

Models: GPT-4o, GPT-4 Turbo, o1, o1-mini

  • Broad spectrum of capabilities
  • Established ecosystem (Plugins, Assistants API)
  • High performance on general tasks
  • Realtime API for voice applications
Note: US provider, higher costs at large volumes

Anthropic (Claude)

Strong Alternative

Models: Claude 3.5 Sonnet, Claude 3 Opus, Claude 3 Haiku

  • Long context window (200k tokens)
  • Precise instruction following
  • Fewer hallucinations
  • Available via AWS Bedrock (EU region)
Note: US provider, no realtime streaming

Azure OpenAI

Enterprise

Models: GPT-4o, GPT-4 Turbo (Microsoft-hosted)

  • EU data centers available
  • Enterprise SLAs and support
  • Integration with Microsoft 365/Azure AD
  • Known compliance certifications
Note: Higher costs, slower model updates

Google (Gemini)

Multimodal

Models: Gemini 1.5 Pro, Gemini 1.5 Flash

  • 1 million token context window
  • Native multimodality (video, audio)
  • Integration with Google Cloud
  • Competitive pricing
Note: US provider, less proven in enterprise

Open Source

Self-Hosted

Models: Mistral, LLaMA 3, Qwen, DeepSeek

  • Full data control (on-premise possible)
  • No vendor lock-in
  • Cheaper at high volumes
  • GDPR-friendly (no external API)
Note: Infrastructure effort, GPU costs

Decision Guide: Which Platform When?

The choice depends on the use case. This table provides initial orientation:

Criterion
OpenAI
Claude
Azure
Gemini
Open Source
Quality (general)
High
High
High
Medium-High
Varies
Cost (per 1M tokens)
$2.50-30
$3-75
$5-60
$0.35-7
$0 (+ GPU)
Context Window
128k
200k
128k
1M
32-128k
EU Data Residency
No
Via AWS
Yes
No
Yes
Enterprise Support
Medium
Medium
High
Medium
Community
Vendor Lock-In
Medium
Medium
High
Medium
None

Our Approach: Multi-Provider Architectures

We recommend and often implement architectures not bound to a single vendor:

  • Abstraction Layer: Unified API that can internally switch between OpenAI, Claude, Gemini, or local models
  • Model Routing: Simple requests to cheaper models, complex ones to more powerful
  • Fallback Strategies: Automatic switching during outages or rate limits
  • GDPR-Compliant:PII filtering before API call enables use of all providers

Real-World Example: AI Sales Training Platform

For an enterprise platform, we implemented a multi-provider architecture:

  • Realtime Voice: OpenAI Realtime API for real-time speech interaction
  • Analysis: Claude for precise conversation analysis (long context)
  • Fallback: Google Gemini as cost-effective alternative
  • Data Protection: GLiNER as local PII filter before all models
View Reference

Typical Use Cases

Chat Assistants & Customer Service

For customer service bots, we recommend GPT-4o or Claude 3.5 Sonnet. At high volume, a hybrid approach with cheaper models for simple requests can make sense.

Document Analysis

For long documents, Claude (200k tokens) or Gemini (1M tokens) is advantageous. For sensitive documents, we use local models or GDPR-compliant architectures.

Voice AI & Phone Assistants

OpenAI Realtime API offers the lowest latency here. For EU compliance, we combine this with local PII filtering and Azure Speech Services.

Internal Automation

At high volume and for internal use cases, open-source models (Mistral, LLaMA) can be more cost-effective - with full data control.

Which Model Fits Your Project?

We provide vendor-neutral consulting and help with selection.

Our Experience With

  • OpenAI (GPT-4, Realtime API)
  • Anthropic (Claude 3.x)
  • Azure OpenAI Service
  • Google Gemini
  • Mistral AI
  • LLaMA / Meta AI
  • Hugging Face Models

Related Services

Real-World Reference

AI Sales Training PlatformMulti-provider architecture with OpenAI, Claude, Gemini, and local PII filtering

Glossary Entries

Frequently Asked Questions

What does GPT-4 cost?

Costs vary by model and usage. GPT-4o costs approx. $2.50 per 1M input tokens, $10 per 1M output tokens. For an initial estimate, we need data on expected request volumes and text lengths.

Can I switch between providers?

Yes, if the architecture is designed for it from the start. We build abstraction layers that enable switching between providers without code changes.

Which model is best for German?

GPT-4 and Claude 3.x both deliver good results in German. For specific applications, we recommend testing with real data.

Are open-source models production-ready?

Yes, models like Mistral or LLaMA 3 are suitable for many use cases. However, they require their own infrastructure (GPUs) and more maintenance effort.

AI Platform vs. Custom AI Solution: when does which apply?

An AI Platform (like OpenAI, Azure, or Gemini) provides ready-made AI infrastructure you integrate via API. A Custom AI Solution - or Custom AI Platform - is built specifically around your business processes, typically combines multiple LLM providers, and integrates into your existing systems. Elasticbrains specializes in these tailored Enterprise AI Solutions.

Start an AI Project?

We help with model selection and implement future-proof architectures.