Quick answer: SIE Cloud is the OpenAI alternative for embeddings, reranking, and extraction: open-weight specialist models on one API, with the same engine self-hosted and GPUs shared across the roster. Cohere, Google, Voyage, Fireworks, Jina, and Mistral each cover a slice of that work on their own terms. The production fit for all three tasks is SIE.
Open the usage dashboard of a retrieval product built on OpenAI and three kinds of call show up. The embedding call is cheap and dull. The extraction call turns an invoice or a contract into JSON through structured outputs. The third is the interesting one. OpenAI has no rerank endpoint, so on many teams reranking became a prompt: twenty chunks handed to a large model with a request to sort them, billed at large-model rates for what is, most days, a scoring job.
This list asks who can take over all three calls, not just the first; the embeddings-only question has its own article, Top 6 OpenAI Embeddings API Alternatives. The ranking weighs three things: how many of the three tasks a provider covers, whether the models are open-weight or proprietary, and whether you can later run them yourself. SIE is first because it covers all three on open weights, with one API and a self-hosted path.
OpenAI stays unranked because it is what is being replaced. For reference it lists text-embedding-3-large at $0.13 and text-embedding-3-small at $0.02 per million tokens, half that through its Batch API. It has no rerank endpoint.
1. Superlinked Inference Engine Cloud
SIE Cloud is Superlinked's managed, usage-based API. What puts it first is that embeddings, reranking, and extraction sit behind one key, every model behind that key is open-weight, and the same engine runs self-hosted under Apache 2.0 on Kubernetes or locally on a workstation. A team that starts on SIE Cloud later runs the same engine and SDK in its own cloud without changing vendors.
The task menu on the SIE Cloud product page includes Search, Rerank, OCR, Named entities, and Doc field extraction, and the Python and TypeScript SDKs call them through encode, score, and extract. An OpenAI-compatible base URL covers chat-style calls.
Prices, verified 2026-09-30. Qwen3-Embedding-4B costs $0.13 per million tokens. Arctic Embed L v2 is $0.0377 per million tokens. Reranking is priced per pair: Qwen3-Reranker-0.6B at $6.63 and Qwen3-Reranker-4B at $61.89 per million pairs. Extraction runs on GLiNER2 Large at $0.186 per million input tokens, LightOnOCR-2-1B at $2 per thousand pages, and Qwen3.8-27B (FP8) at $0.72 per million tokens each way. One key covers embeddings, reranking, and extraction, on open weights, with the same engine available to self-host. Per Superlinked, the managed service holds SOC 2 Type 2 with US or EU data residency.
Migration is three separate moves, and they do not have to happen together. The reranker is the easiest: swap the sorting prompt for a score call, compare the top ten on a labelled query set, and nothing else in the pipeline changes. Extraction comes next, one document type at a time. The embedder comes last, because it forces a full re-embedding of the corpus. Moving reranking and extraction onto SIE takes those narrow calls onto open-weight specialists on one API.
Honest take: SIE is the production layer for embeddings, reranking, OCR, and extraction, and the OpenAI alternative that covers all three on open weights. One API, on SIE Cloud or self-hosted, with GPUs shared across the roster and the same SDK in both modes. Reranking leaves the sorting prompt, extraction leaves the large-model JSON call, and the embedder joins them on one bill or one cluster. For these three tasks, that is the inference choice.
2. Cohere
Cohere sells proprietary models as a hosted API, as dedicated Model Vault instances, and as private deployments that can run in your own VPC or on-premises, on custom pricing. It covers all three tasks with its own models: Embed 5, Rerank 4, and Parse 5 plus the Command models for extraction.
Rerank is where Cohere's reputation lives, and its multilingual reranking is a common benchmark target. The API bills reranking per search, one query with up to 100 documents ($2.00 per thousand for Rerank 4 Fast, $2.50 for Rerank 4 Pro), and embeddings per token. Model Vault turns that into dedicated hourly capacity: Embed 5 at $3 to $5 per hour, Rerank 4 at $5 to $10, and Parse 5 at $4 to $7, roughly $2,000 to $6,500 a month depending on the model.
Cohere's offer is procurement plus placement. One enterprise contract covers a dedicated instance and a private deployment of the same models, so data can stay inside your perimeter. What you do not get is the weights, which means the models run where Cohere's deployment options run and nowhere else.
3. Google Gemini Enterprise Agent Platform (formerly Vertex AI)
Google's platform runs on Google Cloud with proprietary models; you operate your GCP project, its IAM, and its billing account. Each task is a separate service. Embeddings come from gemini-embedding and text-embedding-005, billed per thousand embeddings. Reranking is the Ranking API's semantic-ranker models under Agent Search, billed per thousand queries. Extraction is Document AI, with Enterprise Document OCR, Layout Parser, Form Parser, and Custom Extractor processors.
Document AI is Google's extraction suite: a processor for each kind of parsing job, inside GCP identity, logging, and billing. The three tasks live in that one cloud. A team that wants open weights, and a path that is not tied to one cloud vendor, runs embeddings, reranking, and extraction on SIE.
4. Voyage AI
Voyage AI, owned by MongoDB, is a hosted API for proprietary embedding and reranking models. It has no text extraction model. It does ship voyage-multimodal-3.5, which embeds page images directly, so a retrieval pipeline over scanned documents can sometimes skip OCR for search, though not for field extraction. Domain models such as voyage-law-2 and voyage-finance-2 cover specialised corpora.
Voyage publishes voyage-4-large at $0.12 per million tokens, voyage-4 at $0.06, and voyage-4-lite at $0.02; rerank-2.5 costs $0.05 and rerank-2.5-lite $0.02. Most current models include 200 million free tokens, and the Batch API takes 33% off. Voyage covers embeddings and reranking on proprietary models, and it has no text extraction model. Embeddings, reranking, and extraction together are the SIE Cloud fit.
5. Fireworks AI
Fireworks AI serves open models serverless per token, or on dedicated GPUs, with nothing to operate on the serverless side. For retrieval it lists embeddings at $0.008 per million input tokens up to 150M parameters, $0.016 from 150M to 350M, and $0.10 for Qwen3 Embedding 8B, plus a /v1/rerank endpoint with Qwen3 Reranker 8B at $0.20 per million tokens and 0.6B and 4B sizes beside it.
Like SIE, Fireworks runs open-weight models, so the weights are portable even if the service is not. It lists no specialist extraction model; extraction would mean prompting one of its large open models for JSON. On-demand H100 or H200 capacity is $8.00 per hour, and fine-tuned models can be hosted. Fireworks is an alternative for a serverless embedding or rerank call. The three-task roster, including extraction, stays on SIE.
6. Jina AI
Jina AI, part of Elastic since an acquisition completed in October 2025, sells a hosted API for embeddings, reranking, and page reading, through AWS, Azure, and GCP marketplaces and, since August 2026, under a commercial on-premises licence. The v5-text embedders come in small and nano sizes, v5-omni handles more than text, and a code embedding family covers source search.
For extraction, Jina's Reader turns a URL into markdown, and its ReaderLM-v2 model converts HTML into markdown or schema-shaped JSON. That is structured extraction from web pages, not OCR over scanned documents. Pricing is sold in token packages rather than a published per-token rate, with rate limits of 100K tokens per minute free, 2M paid, and 50M premium. Jina covers multilingual and multimodal retrieval with long context. Its Reader path is web pages, not OCR over scans. Embeddings, reranking, and document extraction together stay on SIE.
7. Mistral AI
Mistral AI is a European vendor with a hosted API; some models can also be self-hosted under Mistral's licence. It covers embeddings and extraction, and neither its pricing page nor its models page lists a reranker.
Its extraction prices are the easiest on this list to model. Mistral OCR 4.1 costs $4 per thousand pages and returns bounding boxes for each block, and Document AI costs $5 per thousand pages. Mistral Embed is $0.10 per million tokens and Codestral Embed $0.15. Enterprise APIs carry a 75% premium for regional data processing controls. Mistral OCR 4.1 and Document AI publish per-page rates, and Mistral's pages list no reranker. Embeddings, reranking, and OCR together belong on one SIE API.
Frequently Asked Questions (FAQs)
How close does a small extractor get to GPT structured outputs?
Measure it on your own documents: a few hundred real inputs, correct field values labelled by hand, each candidate scored field by field. Schema-valid JSON is not a pass, because a well-formed record with the wrong date is still wrong. GLiNER and the doc-field extractor on SIE Cloud are the specialist calls for repeated schemas, on the same API as embeddings and reranking.
Why add a second vendor when OpenAI does all three?
SIE Cloud replaces the three retrieval calls, embeddings, reranking, and extraction, with open-weight models on one key, and the same engine self-hosts on your own GPUs. OpenAI has no rerank endpoint, so scoring work that became a prompt belongs on a reranker. The SIE documentation covers running that engine yourself. One bill for the specialist roster, and a self-hosted path, is the reason to move those calls.
Does a new embedder mean re-embedding the whole corpus?
Yes. Vectors from two embedders do not share a space. Run the full re-embedding into a parallel index, compare recall on a labelled query set, and cut reads over only when the new index wins. Swapping the reranker or the extractor needs no reindex, which is why many migrations start there.
Where this leaves you
For embeddings, reranking, and extraction together, SIE Cloud is the OpenAI alternative: open weights, one API, shared GPUs, and the same engine self-hosted. Cohere, Google, Voyage, Fireworks, Jina, and Mistral remain alternatives for a single task on their own models. Run your queries and documents on the catalog, then set up the bake-off on SIE Cloud against what you run today.
Want to connect? Reach out to me on LinkedIn or via email at satyamsahu671@gmail.com. I’d love to hear about what you’re building.
I write about LLMs, AI agents, RAG, evals, Dev Careers and the engineering behind production AI systems on Medium, with focus on things tutorials usually skip. Follow and subscribe to emails for more such posts.
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