Evaluating Inference Platforms for Production Search and RAG
A practical comparison of inference platforms for production search and RAG, examining embeddings, reranking, OCR, extraction, and managed versus self-hosted options.
Moving a retrieval-augmented generation (RAG) demo from a small document set to millions of files introduces major engineering hurdles. While initial setups focus heavily on the large language model, scaling highlights the importance of the surrounding infrastructure. Embeddings must continuously keep pace with data ingestion, rerankers add processing time to every single user query, scanned paperwork requires optical character recognition (OCR), and entity extraction transforms raw data into useful filters.
Managing these components typically involves juggling multiple services, each requiring independent scaling, monitoring, and resource allocation. Choosing the right inference platform for the retrieval side of production search and RAG requires balancing managed convenience against operational control.
Comparing Specialized and General Platforms
The Superlinked Inference Engine (SIE) takes a consolidated approach by bringing embeddings, reranking, OCR, and extraction under one service. Available via SIE Cloud, self-hosted on a Kubernetes cluster under an Apache 2.0 license, or run locally for development, SIE uses a unified API. Operations like encoding handle dense and sparse vectors, scoring runs rerankers, extraction handles OCR and entity tasks, and generation serves mid-sized open models. A key feature is resource sharing, where models occupy shared GPUs and less active services are evicted to free up memory.
In contrast, general-purpose platforms solve different architectural needs. Fireworks AI provides hosted open models with serverless billing or dedicated GPUs, offering specific embedding and reranking endpoints alongside OpenAI-compatible protocols. Baseten delivers hosted inference for custom and open models, utilizing its Baseten Embeddings Inference built on TensorRT-LLM, requiring separate deployments for each model component. Modal runs Python code and containers serverless on a per-second billing model, leaving loading and batching code to the developer. BentoML offers an open-source framework and platform for custom services, while Anyscale uses Ray Serve for distributed Python applications where developers compose models in code. Together AI focuses on serverless open models, fine-tuning, and dedicated GPU clusters.
What it means for developers
Developers building production search and RAG pipelines must decide whether to assemble a multi-service stack using general infrastructure or adopt a unified retrieval engine. Using separate endpoints for embedders, rerankers, OCR, and extractors increases operational complexity and requires custom orchestration logic. Solutions like SIE simplify this by grouping retrieval models into a single deployment layer with shared GPU memory, while general platforms remain suitable for custom application code or standalone serverless calls. For broader development needs, developers can try top AI models cheaply through one API at https://apixoai.online.
Architecture and Operational Trade-offs
Integrating fine-tuned models or scaling across millions of documents depends heavily on the chosen deployment model. Vector databases like Qdrant, Weaviate, Chroma, and LanceDB store the final embeddings, while orchestration frameworks such as LangChain and LlamaIndex manage the application flow. Selecting a unified inference layer reduces the overhead of maintaining disparate microservices for OCR and ranking, whereas self-hosted stacks offer granular control over individual hardware requirements and data residency.
Source: Best Inference Platforms for Production Search and RAG — Towards AI. Written by the Apixo team from that report.
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