Instant Access to11,000+ AI ModelsNo Setup. No Worry. Just Results.
Your Data. Your Control. Our Expertise.
🚀 Azure AI Foundry Models
Get instant access to thousands of enterprise-ready AI models hosted securely within Azure. Your data never leaves your cloud environment—zero setup, maximum security.
- âś“ Data stays in your Azure tenant
- âś“ Enterprise-grade security & compliance
- âś“ Instant deployment, no infrastructure
đź”’ External Models with Guardrails
Need models outside Azure? We support external providers with robust data security policies and controls that you define and enforce.
- âś“ Configurable data security policies
- âś“ Full audit trails & monitoring
- âś“ Your rules, consistently enforced
Models Our Clients Love
While we support thousands of AI models, these are the standout performers that have earned their place as favorites among our team and clients for their reliability, performance, and results.
Audition AI supports popular AI models across providers, ensuring you can deploy the most effective technology for each business need.
You can enable models from Google, Anthropic, AWS, and other providers alongside your existing solutions. We ensure your teams can configure and run the models you require, wherever they reside.
Operational Considerations
When enabling models outside your primary cloud environment, we work with your team to address:
Data security and leakage prevention
Cost optimization, including data transfer and exfiltration
Compliance, governance, and monitoring
Defending Your Cloud
For organizations operating primarily in a non-Azure cloud (such as AWS), Audition AI enforces your corporate AI strategy and policies — maintaining enterprise-grade security and strict adherence to your governance framework.
With Audition AI, you have the flexibility to turn on the models that deliver the best results for your organization — supported by the security, policies, and controls you define.
Model Hosting in Azure: “Direct from Azure” vs Other Types
1. “Direct from Azure” AI Models
These are pre-built, fully managed AI models provided and hosted directly by Microsoft Azure. They’re available as-a-service and accessed via Azure endpoints.
Examples:
- Azure OpenAI models: GPT-4, GPT-3.5, DALL·E, etc., served directly from Azure’s infrastructure.
- Azure Cognitive Services: Vision, Speech, Language, and more.
Key characteristics:
- Managed hosting: Microsoft runs, patches, and scales the models.
- No custom training: Use the model as-is (with options to ground/fine‑tune in some cases).
- Fast deployment: No setup—just call the API.
- Data stays in Azure: Prompts and outputs remain within Azure for compliance and residency.
2. Other Types of Models (Not “Direct from Azure”)
This category covers any AI model not hosted directly by Microsoft as a managed service.
a) Open source or third‑party models
- Deployed on Azure: Bring your own models (e.g., Llama, Mistral, Falcon) via Azure ML/AI Studio/AKS.
- Managed by you: You handle deployment, scaling, patching, and monitoring.
b) Fine‑tuned/custom models
- Start from a base model and fine‑tune with your data.
- Deployed to your own Azure endpoints; lifecycle is your responsibility.
c) Third‑party model providers
- Azure AI Studio may enable calling external provider APIs (e.g., Hugging Face, AI21, Cohere).
- Requests may be routed outside Azure depending on the integration.
| Feature | Direct from Azure AI | Other Types (Custom/Third‑Party) |
|---|---|---|
| Hosting | Managed by Microsoft | Managed by you or a third‑party |
| Setup | Instant (API) | Requires deployment/configuration |
| Model choice | Limited to Azure’s catalog | Any model you can deploy |
| Updates/Security | Handled by Microsoft | Your responsibility |
| Compliance/Data | Data stays in Azure | Depends on deployment |
| Customization | Limited (prompting/some fine‑tuning) | Full control (train/fine‑tune) |
When to use Direct from Azure
- Fast integration and time‑to‑value.
- Strong reliability with no infra to manage.
- Compliance and data residency within Azure.
When to use Other Types
- Need custom behavior or niche/open‑source models.
- Desire fine‑tuning or full control over lifecycle.
- Willing to manage deployment, scaling, and security.
Models FAQ
Tokens are pieces of text—often a word or part of a word (for example, “un” in “understand”). For English, 1 token is roughly 4 characters or ~0.75 words.
Context Window: Input vs Output
- The context window is the sum of input and output tokens in a single exchange.
- Input tokens: Everything you send (system messages, your prompt, prior conversation).
- Output tokens: The model’s response.
If a model has a 200,000-token window, then (input tokens + output tokens) ≤ 200,000. If your prompt is 199,000 tokens, the model can generate ~1,000 tokens.
Comparing Models
- GPT-4.1 (some enterprise variants) can process up to ~1,000,000 tokens total per exchange.
- GPT-5 with 200,000 tokens works the same way—total context, not just input.
Multimodal models can accept more than one input type (for example, text and images) and sometimes produce multiple output types. Check each model’s supported inputs/outputs via the info icon.
- Common inputs: Text, Image, sometimes Audio.
- Common outputs: Text; some models also generate Images or Video.
- Task fit: Match capabilities (reasoning, coding, vision) to your use case.
- Latency & cost: “Mini/Small” variants trade quality for speed and price.
- Security & residency: Prefer models marked “Direct from Azure” when data locality matters.
- Context length: Larger windows help with long documents and chats; see the tokens link above.
- Capabilities describe strengths like Multimodal, Multilingual, Reasoning, Conversation.
- Input/Output lists the media types a model accepts and produces (Text, Image, Audio, Video).
- Use both: pick capabilities for quality fit, and I/O for data formats you need.
Ready to Get Started?
Ready to see how access to modern AI models can empower you? Schedule a demo or speak with our sales team today.