What should I use for real-time embedding generation in an API service?

For real-time embedding generation in an API service, the best choice depends on your priorities:

Good default options

1. Hosted embedding API

Best if you want:

• low operational burden
• fast integration
• elastic scaling
• managed latency/SLA

Examples:

• OpenAI embeddings
• Cohere embeddings
• Voyage AI
• Azure OpenAI / AWS Bedrock hosted models

Pros

• Easy to deploy
• No GPU management
• Reliable for production

Cons

• Per-request cost
• Network latency
• Vendor dependency

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2. Self-hosted embedding model

Best if you want:

• lower cost at scale
• data residency/control
• predictable latency in your infra

Common choices:

• bge-small / bge-base / bge-large
• e5-small / e5-base / e5-large
• GTE models
• SentenceTransformers variants

Typical serving stacks:

• vLLM (more common for LLMs, but can work depending on model)
• Text Embeddings Inference (TEI) by Hugging Face
• Triton Inference Server
• FastAPI + ONNX Runtime
• TorchServe / custom PyTorch service

Pros

• Full control
• Can be cheaper at scale
• Can keep data in your VPC

Cons

• You manage scaling, patching, GPUs, monitoring
• More engineering effort

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What I’d recommend

If you’re starting out:

Use a hosted embedding API.

It’s usually the fastest path to a production-ready API service unless:

• you have very high throughput,
• strict privacy/compliance requirements,
• or very tight latency/cost constraints.

If you need self-hosting:

Use Hugging Face TEI with a model like:

• bge-small-en-v1.5 for cost/latency balance
• bge-base-en-v1.5 for better quality
• e5-base-v2 if your use case fits it well

TEI is a strong default because it’s designed specifically for embedding serving and is simpler than rolling your own stack.

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Practical selection guide

Choose hosted if:

• you need to ship quickly
• traffic is variable
• you don’t want GPU ops
• you’re fine with external API calls

Choose self-hosted if:

• you have steady high volume
• embeddings are a core cost center
• you need strict data control
• you want consistent low-latency in your own region

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API service design tips

Regardless of model choice:

• batch requests when possible
• cache embeddings for repeated inputs
• enforce input length limits
• keep the embedding service stateless
• monitor p50/p95 latency, queue depth, and error rate
• use async request handling if traffic spikes

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Bottom line

If you want the simplest real-time setup: use a hosted embedding API.
If you want to self-host: use Hugging Face TEI with BGE or E5 models.

If you want, I can also give you:

1. a cost/latency comparison of hosted vs self-hosted, or
2. a reference architecture for an embedding API service.