Azure AI is getting expensive for our inference workload, what are alternatives?

Yes—there are several strong alternatives to Azure AI for inference, and the best choice depends on whether you need LLMs, vision/audio models, low latency, compliance, or just lower cost.

Good alternatives

1) Other managed model APIs

If you want to keep the “API-first” experience:

• OpenAI API – often simpler and sometimes cheaper depending on model choice and usage patterns.
• Anthropic Claude API – strong for reasoning and long-context workloads.
• Google Vertex AI / Gemini API – competitive pricing and good integration if you’re already in GCP.
• AWS Bedrock – multi-model platform; useful if you want model choice and enterprise controls.
• Cohere – good for enterprise NLP/search use cases.

Best when: you want low ops overhead and are okay with vendor APIs.

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2) Self-host open-source models

This is often the biggest cost saver at scale.

Popular options:

• Llama
• Mistral / Mixtral
• Qwen
• Gemma
• Phi (smaller, cheaper models)

Serving stacks:

• vLLM – very popular for high-throughput LLM inference.
• TGI (Text Generation Inference) – solid production option.
• SGLang – good for complex serving patterns.
• Ollama – easy local/dev use, less ideal for heavy production scale.

Infrastructure options:

• GPU VMs from AWS, GCP, Azure, CoreWeave, Lambda, RunPod, Vast.ai
• On-prem GPUs if you have predictable, steady traffic

Best when: you have enough traffic to justify GPUs and want full control over cost/performance.

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3) Specialized inference providers

These are often cheaper than hyperscaler AI services and optimized for model serving:

• Together AI
• Fireworks AI
• Groq (very fast for supported models)
• Replicate
• DeepInfra
• OctoAI (depending on region/product availability)

Best when: you want cheaper hosted inference without managing GPUs.

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4) Serverless / edge inference for smaller models

If your models are small or latency-sensitive:

• Cloudflare Workers AI
• Modal
• Hugging Face Inference Endpoints
• Edge functions + quantized models

Best when: workload is bursty, smaller models are acceptable, or you need geographic distribution.

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How to lower cost without changing providers

Sometimes the biggest savings come from architecture changes:

• Use smaller or distilled models
• Quantize models (8-bit / 4-bit)
• Cache responses for repeated prompts
• Batch requests
• Use routing: small model first, large model only when needed
• Trim prompt/context aggressively
• Use embeddings + retrieval instead of stuffing everything into the prompt
• Set max tokens tightly
• Monitor token usage and per-request cost

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Quick rule of thumb

• Low volume / want convenience: OpenAI, Anthropic, Bedrock, Vertex AI
• High volume / want cost control: self-host with vLLM on GPUs
• Need best price-performance quickly: Together AI, Fireworks AI, DeepInfra
• Need ultra-low latency: Groq or edge/serverless options
• Need enterprise cloud governance: AWS Bedrock or Vertex AI

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If you want, I can help you compare options more concretely

If you share:

1. model type (LLM, vision, embeddings, etc.)
2. monthly requests or tokens
3. latency target
4. current Azure service you’re using
5. whether you can self-host GPUs

…I can suggest the cheapest realistic alternative and estimate savings.