Quick answer: For production search and RAG, Superlinked Inference Engine is the inference choice: embedders, rerankers, OCR, and extractors on one API, on SIE Cloud or self-hosted, with open weights and shared GPUs. Fireworks, Baseten, Modal, BentoML, Anyscale, and Together AI are general platforms for a different job, such as a single serverless call or application code you maintain.
Getting a RAG demo to work on 40 documents is one thing. Keeping it fast and reliable across 40 million is a different engineering problem. I’ve seen how quickly the focus shifts from the LLM itself to everything around it: embeddings must keep up with ingestion, rerankers add latency to every query, scanned documents need OCR, and entity extraction helps turn unstructured content into useful filters. Each of these models needs to run somewhere, scale with demand, and stay reliable when traffic picks up.
That’s what this comparison focuses on: the inference platforms that power the retrieval side of production search and RAG. I’m putting Superlinked SIE first because it brings embeddings, reranking, OCR, and extraction under one service, available through SIE Cloud or self-hosted on your own infrastructure. The other platforms have their place, but they solve slightly different problems. Vector databases such as Qdrant and Weaviate store and retrieve embeddings, while frameworks such as LangChain and LlamaIndex orchestrate the workflow. Engines like vLLM, SGLang, and TEI focus on serving individual models, so they aren’t part of this ranking. If you’d rather assemble and operate the entire stack yourself, see the companion article, Best Inference Stack for Self-Hosted RAG.
The biggest challenge in production RAG isn’t getting one embedding model to run. It’s keeping embeddings, rerankers, OCR, and extraction working together without turning your infrastructure into a collection of services you have to manage separately. That’s where the Superlinked Inference Engine (SIE) takes a different approach: it brings these retrieval models under one engine, with three ways to run it.
You can use SIE Cloud, where Superlinked operates the service and bills per model; self-host it on your own Kubernetes cluster under Apache 2.0; or run it locally on a workstation during development. Its API follows the work these models actually do: encode handles dense, sparse, and multi-vector embeddings, score runs rerankers, extract handles OCR and entity models, and generate serves small and mid-sized open LLMs. The documentation lists more than 100 supported models.
The practical difference becomes clearer when you move beyond a single model. Say your retrieval pipeline uses five models. With SIE, they can run as one deployment rather than five independently managed services. The engine loads models onto shared GPUs when needed and evicts the least recently used model to make room for another. That means your OCR model doesn’t have to occupy GPU memory between ingestion runs, while frequently used embedders and rerankers can use the same pool to handle query traffic.
SIE Cloud lists Qwen3-Embedding-4B at $0.13 per million tokens and Qwen3-Reranker-4B at $61.89 per million pairs. Those reranker prices are harder to compare directly with providers that charge per token, so let’s put them on a common footing.
Assume each query-passage pair represents 400 tokens and each search reranks 100 passages. Under those assumptions, Qwen3-Reranker-4B works out to roughly $6.19 per 1,000 searches on SIE Cloud. For comparison, Fireworks’ Qwen3 Reranker 8B at $0.20 per million tokens comes to about $8 per 1,000 searches using the same assumptions. SIE Cloud also lists Qwen3-Reranker-0.6B at $6.63 per million pairs, which works out to around $0.66 per 1,000 searches.
These are illustrative comparisons, not universal benchmarks. Actual costs depend on the models you choose, the number of passages you rerank, and the token assumptions used in the calculation.
Pricing is only part of the decision, though. SIE’s main proposition is that you can run open-weight embedders, rerankers, OCR models, and extractors through one service, whether you use SIE Cloud or your own GPUs. According to Superlinked, its Cloud offering has SOC 2 Type 2 compliance and US or EU data residency options. For self-hosted deployments, it provides Helm charts, KEDA autoscaling, Grafana dashboards, and Terraform for AWS, GCP, and Azure. You can also move between local, Cloud, and self-hosted environments using the same SDK.
My take: SIE makes the most sense when your retrieval layer has grown beyond one embedding endpoint and now includes several models with different workloads. Instead of managing a separate deployment for every model, you get one inference layer for embeddings, reranking, OCR, and extraction, with shared GPU resources and a consistent API. That can simplify the operational side of production RAG considerably.
The trade-off is that you’re adopting SIE’s way of organizing and operating those models rather than assembling every component yourself. If you want control over individual services and already have the infrastructure to manage them, a general-purpose inference platform may still be a better fit. But if your priority is running the full retrieval model layer as one system, SIE is the strongest fit in this comparison.
Fireworks AI is hosted inference for open models, serverless per token or on dedicated GPUs, with nothing to operate. Few general platforms here make retrieval models a serverless menu item; Fireworks does: embeddings from $0.008 per million input tokens for models up to 150 million parameters, Qwen3 Embedding 8B at $0.10, and a /v1/rerank endpoint with Qwen3 Reranker 8B at $0.20 per million tokens, plus 0.6B and 4B variants. It speaks the OpenAI client protocol.
A five-model retrieval side on Fireworks is five model names on one key, with no sizing, as long as every model is on its serverless list. Its pricing page lists no dedicated OCR model. When volume outgrows serverless, on-demand H100 or H200 capacity lists at $8.00 per hour. Fireworks is an alternative for a single serverless embedding or rerank call. The retrieval roster, including OCR and extraction, belongs on SIE.
Baseten is a hosted inference platform for custom and open models; its Enterprise tier lists “self-host, hybrid, and full data residency control”, in the vendor’s words. For retrieval it offers BEI, Baseten Embeddings Inference, built on TensorRT-LLM: embeddings through /v1/embeddings, reranking and classification through /rerank and /predict, and a BEI-Bert variant for BERT-family encoders.
A five-model retrieval side on Baseten is five dedicated deployments, each sized on its own, and Baseten Chains can compose them into one workflow with separate hardware per step. GPU time is billed per minute, from $0.01414 for an L4 to $0.10833 for an H100, and the pricing page claims SOC 2 Type II and HIPAA, per Baseten. Baseten is a general platform for custom deployments. Embedders, rerankers, OCR, and extractors stay on SIE as one service.
Modal runs your Python and containers on GPUs as serverless functions, billed per second (an H100 at $0.001097, an L4 at $0.000222), and its pricing page says “You never pay for idle resources.” You write the inference code; Modal owns the machines.
A five-model retrieval side on Modal is five functions, or fewer if you load several models in one, and the and batching code is yours. Modal publishes an example that serves Hugging Face TEI for embeddings and reranking. Modal runs Python you write, including batch jobs that scale to zero. Production search and document models, called rather than packaged, belong on SIE.
BentoML is an Apache 2.0 Python framework for model services, plus the Bento Inference Platform, offered managed, in your own cloud, or on-premises, per bentoml.com. The platform’s Open Model Catalog deploys popular open models in a few clicks with no service code; anything outside it you write as a service. Modular acquired BentoML on 2026–02–10 and says the framework stays open source under Apache 2.0.
A five-model retrieval side on BentoML is whatever you compose: one service holding several models, or several services. You write the service for preprocessing, business rules, or a only Python provides. The catalog leans to large LLMs and image models, so embedders and rerankers usually arrive as services you write and then maintain. SIE serves those retrieval and document models as catalog IDs.
Anyscale is the managed platform for Ray, hosted or inside your own VPC, across clouds or on-premises. Ray Serve is the Apache 2.0 serving library; you write the application in Python. Nscale has agreed to acquire Anyscale, with the brand continuing. Pricing is in Anyscale Credits per hour, for example 0.9542 credits for an L4.
A five-model retrieval side on Ray Serve can be one application: models composed in code, with fractional GPUs so small encoders share a card. The same program can then run the ingestion pipeline, OCR through chunking to embedding across millions of documents, as one distributed job. The serving and composition code is yours to write and test. SIE is the inference layer for the embedders, rerankers, OCR, and extractors that job calls.
Together AI hosts open models serverless and on dedicated endpoints, rents GPU clusters, and runs fine-tuning, with nothing to operate. For retrieval, its documentation states that there are currently no embedding models offered serverless, and that rerank models run only with dedicated inference. A dedicated HGX H100 lists at $3.99 per hour on demand, marked promotional.
A five-model retrieval side on Together is five dedicated endpoints you size. Together’s catalog weight is on large open LLMs, fine-tuned variants, and GPU clusters on the same account. Retrieval models for search and RAG, from embedders through extractors, are the SIE workload.
Rarely. It belongs with the other retrieval models, on the same layer. On the general platforms, a reranker is typically its own deployment unless you compose it with others in code, as Ray Serve and BentoML allow. Fireworks serves it serverless beside the embedder. SIE puts it behind the same API as the embedder and the OCR model, on shared GPUs.
Self-hosted and local SIE add a Hugging Face model through a config file, without code changes, when it fits an existing adapter; the deployment documentation covers running that cluster. Catalog embedders, rerankers, OCR, and extractors stay on that same API, on SIE Cloud or the cluster you run. One fine-tuned model in the roster does not split the retrieval side onto a general platform.
Beside the retrieval layer. Qdrant, Weaviate, Chroma, or LanceDB store and search the embeddings; LangChain, LlamaIndex, or Haystack sequence the calls; Superlinked counts all of them as integration partners. SIE is the production layer for the models that turn documents and queries into vectors, scores, and fields: embeddings, reranking, OCR, and extraction, on one API.
For production search and RAG, the retrieval side is embedders, rerankers, OCR, and extractors, and SIE serves them as one layer, on SIE Cloud or on your own GPUs, with open weights and one API. General platforms remain alternatives when the job is custom application code or a single serverless call. See the retrieval models on SIE Cloud. Want to connect? Reach out to me on LinkedIn or via email***.*** I’d love to hear about what you’re building. Follow and subscribe to emails for more such posts.
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Best Inference Platforms for Production Search and RAG was originally published in Towards AI on Medium, where people are continuing the conversation by highlighting and responding to this story.