TopK
TopK is a unified retrieval engine that runs multi-vector, dense, sparse, regex, and metadata-filtered search in one query, billed per unit of usage.
If recall is what's capping your RAG or agent stack, TopK is one of the few engines that runs multi-vector late interaction, BM25, regex, and metadata filters in a single query without three vendors stitched together — and its published 65.09 nDCG@10 on ViDoRe-V3 beats Cohere (56.63), Voyage (60.64), Gemini (58.70), and Mixedbread (62.25). Usage-based billing with $10 in free credits lets you size a bill before committing, but query memory at $20.00 per GiB/month usually dominates it. Pick Pinecone or Bedrock Knowledge Base instead if you need a broader ecosystem, and skip TopK entirely if you want a no-code search product.
Verified 10h ago · liveness 64/100 · cite: rightaichoice.com/tools/topk
- Developers building high-recall RAG systems that need multi-vector, hybrid, and filtered retrieval in one query
- Enterprise teams in finance, legal, medical, or industrial domains measuring recall against ViDoRe-V3
- AI application builders implementing agent memory with persistent, searchable context and grounded citations
- Teams with billion-document corpora who want object-storage economics and per-unit pricing they can forecast
- Non-technical users who want a no-code, click-to-build search product
- Workloads dominated by high write throughput with no read-heavy retrieval pattern
- Applications expecting a transactional relational database rather than a retrieval engine
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Skip TopK if you want a click-to-build search product with a GUI console — TopK assumes you're comfortable issuing retrieval through an SDK, TopK SQL, or a Postgres-wire client.
Query memory is billed at $20.00 per GiB/month — the largest line on the rate card, and it scales with how much hot memory your query volume keeps resident.
At $0.10/GiB/month storage and $0.50 per 1M queries, TopK's rate card suits teams big enough to have a read-heavy retrieval workload and an engineer to tune it — mid-market through enterprise in finance, legal, medical, or industrial search. Below that, a bundled managed vector database is simpler. Above it, forecast nine metered lines before you compare against Pinecone or Bedrock Knowledge Base, because query memory at $20.00 per GiB/month moves the total more than any other line.
In short
TopK — TopK is a unified retrieval engine that runs multi-vector, dense, sparse, regex, and metadata-filtered search in one query, billed per unit of usage. Best for Developers building high-recall RAG systems that need multi-vector, hybrid, and filtered retrieval in one query, Enterprise teams in finance, legal, medical, or industrial domains measuring recall against ViDoRe-V3, AI application builders implementing agent memory with persistent, searchable context and grounded citations. Plans from $10.
What's new in TopK
Checked 2 days agoAcross the latest 5 updates: 2 feature updates, 1 launch and 2 changelog entries.
Introducing topk-embed-v1: Frontier retrieval at the lowest cost
TopK released topk-embed-v1, an open-source multimodal multi-vector embedding model with a production version in TopK priced at $0.05/1M tokens.
The Fastest Regex Is the One You Don't Run
TopK engineering post on why AI agents reach for regex search and how TopK uses sparse n-grams to speed up those queries.
RAG Is Broken for Agents. Here's How We Fixed It.
TopK research post arguing dense-embedding RAG fails agents and that multi-vector late-interaction retrieval is the fix.
TopK SQL: A Search Query Language
TopK now implements the Postgres wire protocol, letting any Postgres client run semantic, hybrid, and filtered search as SQL.
High-Quality Search, Out of the Box
TopK's semantic_index annotation ships multi-vector retrieval without a separate embedding pipeline, vector store, or reranking service.
What people actually say about TopK — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
69 mentions across 4 sources (Hacker News, Bluesky, Stack Overflow, Lemmy) · researched Jul 5, 2026.
Average across the 4 sources that answered — each source counts once, not each post.
- +Claims 10x lower cost with object storage
- +Unlimited scale with sub-100ms latency at billion-scale
- +Unified retrieval over structured and unstructured data
- +Hybrid search with vector, keyword, and custom scoring
- +Multi-vector retrieval with late interaction support
- −Zero verified user reviews or testimonials
- −All community data is keyword false positives
- −Performance claims are unverified in real-world use
- −No independent benchmarks or case studies
- −Risk that product lacks critical mass or ongoing support
- • Data egress fees not specified
- • Potential overage charges for high query volumes
Viability Score
How well maintained and how widely used is TopK? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Hybrid search combining dense, sparse, late-interaction multi-vector, regex, keyword, and metadata filters in one query
- Multi-vector retrieval with late interaction (SMVE)
- TopK SQL query language over the Postgres wire protocol for semantic and hybrid search
- TopK SQL lets any Postgres client run semantic, hybrid, or filtered retrieval
- Regex search accelerated with sparse n-grams
- semantic_index annotation ships multi-vector retrieval without a separate embedding pipeline
- topk-embed-v1 open-source multimodal multi-vector embedding model
- Built-in multi-vector text embeddings billed at $0.05 per 1M tokens
- File Search returning grounded answers with precise citations
- Agentic File Search with sub-queries and reasoning
- Document processing with OCR for PDF, DOCX, image, and text inputs
- Custom scoring across multiple ranking signals
- Multi-tenant partitioned collections with isolated namespaces
- Object-storage architecture scaling to 1B+ documents per partition
- Private deployment into your VPC or on-prem
About TopK
TopK bundles three things most RAG stacks assemble from separate vendors: a retrieval engine, multi-vector embedding models, and the inference that serves them. You can combine dense, sparse, late-interaction multi-vector (SMVE), regex, and metadata filters in a single query with custom scoring across ranking signals, rather than stitching a vector store, a BM25 index, and a reranker together. TopK SQL implements the Postgres wire protocol, so any Postgres client can issue semantic, hybrid, or filtered retrieval as ordinary SQL. The semantic_index annotation ships multi-vector retrieval without a separate embedding pipeline, vector store, or reranking service. File Search turns private PDFs, DOCX, and images into citation-backed answers, with an agentic mode that runs sub-queries and reasoning; document processing includes OCR. On quality, TopK publishes ViDoRe-V3 results of 91.39% medical, 88.22% legal, and 84.59% finance, and a 65.09 nDCG@10 against Cohere (56.63), Voyage (60.64), Gemini (58.70), and Mixedbread (62.25). On scale it reports 1B+ documents per partition on object storage, 70 MB/s writes per partition, under one second data freshness, and sub-100ms p99 query latency at 1B documents. Multi-tenant partitioned collections keep namespaces isolated, and private VPC or on-prem deployment is available. It is built for developers and enterprises in accuracy-critical work — finance, legal, medical, industrial — where a missed document becomes a wrong answer. You will be writing code: TopK is reached through SDKs, TopK SQL, or a Postgres-compatible client, not a click-to-build console. The September 2026 topk-embed-v1 release (open-source multimodal multi-vector embedding model, production version served inside TopK at $0.05 per 1M tokens) means you can skip a separate embedding vendor.
Behind the Verdict
TopK's argument is architectural. Most teams building high-accuracy retrieval end up with a dense vector store, a keyword index, a reranker, and an embedding vendor, then spend their engineering time keeping four systems consistent. TopK collapses that into one engine and one bill: dense, sparse, late-interaction multi-vector (SMVE), regex, and metadata filters execute in the same query, with custom scoring across ranking signals so you can weight them yourself. The semantic_index annotation is the practical payoff — you get multi-vector retrieval without standing up a separate embedding pipeline, vector store, or reranking service. The second differentiator is TopK SQL. Implementing the Postgres wire protocol means any Postgres client can run semantic, hybrid, or filtered search as SQL, which shortens the path from a prototype to something your existing tooling can query. That is a real engineering decision, not a marketing line, and it is why the tool fits teams already comfortable with databases. Quality claims are specific and checkable. TopK publishes ViDoRe-V3 accuracy of 91.39% medical, 88.22% legal, and 84.59% finance, and a 65.09 nDCG@10 against named competitors. It also publishes the cost side of that comparison — the homepage plots nDCG@10 against managed-provider input cost per 1M tokens from $0.04 to $2. Treat vendor benchmarks as a starting point and run your own corpus, but the specificity is a good sign. Scale numbers are equally concrete: 1B+ documents per partition, 70 MB/s writes per partition, under one second data freshness, and sub-100ms p99 at 1B documents. The object-storage foundation is what makes per-unit pricing forecastable, and multi-tenant partitioned collections with isolated namespaces matter if you serve multiple customers from one deployment. Where it gets harder: you need engineers. Reach it through SDKs (Python, JavaScript, Rust), TopK SQL, or a Postgres-compatible wire client — there is no no-code console for building search. Pricing has nine metered lines and the on-site estimator is explicitly indicative, excluding egress, get operations, and embedding usage, so read-heavy workloads will land above the headline number. PostgreSQL is the integration surfaced in the material reviewed for this refresh, so check the vendor's own integrations page for your stack rather than assuming. TopK also is not a transactional relational database, despite speaking the Postgres wire protocol. Who should look at it: teams in finance, legal, medical, or industrial search where recall is the product, teams with billion-document corpora that want object-storage economics, and regulated workloads that need private VPC or on-prem deployment. Who should not: anyone wanting click-to-build search, or a workload dominated by high write throughput with no read-heavy retrieval pattern.
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Real-world workflow fit
Concrete scenarios for the personas TopK actually fits — and what changes day-one when you adopt it.
You inherit a stack with pgvector for dense retrieval, a separate BM25 service, and a hosted reranker, and recall on contract clause lookup is the complaint. You point the semantic_index annotation at your document store so multi-vector retrieval runs without a separate embedding pipeline, then move clause queries onto TopK SQL so existing Postgres clients issue hybrid retrieval as plain SQL.
Outcome: One query returns dense, sparse, and late-interaction matches together with metadata filters on jurisdiction and date, so you tune ranking with custom scoring instead of reconciling three systems.
You need multi-tenant isolation for customer namespaces and sub-100ms p99 at scale. You set up partitioned collections per tenant on object storage and start with $10 in free credits, sizing the bill from the pricing page's per-unit rates before committing.
Outcome: Tenants stay isolated in their own namespaces, and you can model spend line by line across storage, write requests, query memory, and query compute instead of guessing at a flat plan.
Your agent needs grounded answers from private PDFs and scans, not just nearest-neighbour chunks. You route documents through OCR-backed processing into File Search, and use agentic File Search for multi-step questions that need sub-queries.
Outcome: Answers come back with precise citations, and the agent can decompose a research question across several searches rather than issuing one dense query and hoping the right page ranked.
Use Cases
- Build accurate RAG agents that ground answers in private documents with citations
- Run enterprise search across structured and unstructured data with hybrid retrieval
- Power multi-vector search applications requiring late interaction for nuance
- Create file search for knowledge bases with precise citation support
- Deploy sub-100ms search at billion scale for AI-driven products
- Give agents persistent, searchable memory with custom scoring for recency
- Build recommendation systems with filtering and online updates
- Run deep multi-step research queries with citations
Models Under the Hood
as of 2026-10-15
Limitations
- TopK is a developer-focused retrieval engine: you work through SDKs (Python, JavaScript, Rust), TopK SQL, or a Postgres-compatible wire protocol rather than a no-code interface.
- Pricing is usage-based and billed monthly across nine metered lines — storage, write requests, write data, query requests, query memory, query compute, egress, get operations, and text embedding.
- The on-site estimator is labelled an indicative estimate and explicitly excludes egress, get operations, and embedding usage, so high read volume will run above the headline figure.
- Although TopK SQL speaks the Postgres wire protocol, the engine is not a transactional relational database, and PostgreSQL is the integration surfaced in the material reviewed for this refresh — check the vendor's own integrations page for your stack before planning around a connector.
as of 2026-10-18
Verification history
We have re-verified TopK 10 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 10 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where TopK's pricing actually pencils out — and where peers do it cheaper.
At $0.10/GiB/month storage and $0.50 per 1M queries, TopK's rate card suits teams big enough to have a read-heavy retrieval workload and an engineer to tune it — mid-market through enterprise in finance, legal, medical, or industrial search. Below that, a bundled managed vector database is simpler. Above it, forecast nine metered lines before you compare against Pinecone or Bedrock Knowledge Base, because query memory at $20.00 per GiB/month moves the total more than any other line.
Setup time & first value
How long it actually takes to get something useful out of TopK — broken out by persona, not the marketing-page minute.
A developer with an existing Postgres client can run a first hybrid query through TopK SQL in an afternoon, using the $10 in free credits. Standing up OCR-backed File Search over a real document set, wiring the semantic_index annotation, and tuning custom scoring weights is a multi-day job for one engineer. Enterprise teams planning VPC or on-prem private deployment should budget weeks including
Switching to or from TopK
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Pinecone: reindex your corpus through TopK's write path, then replace dense-only queries with TopK SQL statements that add sparse and metadata filters.
- →From a pgvector + BM25 stack: collapse both into one TopK collection and move queries to TopK SQL, since the Postgres wire protocol keeps existing client code familiar.
- →From Bedrock Knowledge Base: export source documents, re-ingest through TopK document processing with OCR, and switch answers to File Search citations.
- →From a hosted embedding vendor: move to topk-embed-v1 served inside TopK at $0.05 per 1M tokens to drop the separate embedding pipeline.
- ↗To Pinecone: export vectors and metadata from your TopK collections and re-upsert, then rebuild hybrid and regex logic in Pinecone's query API.
- ↗To pgvector: dump vectors into a Postgres table and reimplement late-interaction and regex search, which means adding a reranker service back into the stack.
- ↗To Cohere or Voyage embeddings plus a separate store: keep TopK as the source corpus, re-embed with the new vendor, and accept a recall re-measurement.
- ↗To on-prem open source search: topk-embed-v1 is open source, so you can carry the embedding model with you even when leaving the hosted engine.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “TopK”, and we withheld 6: 6 could not be judged, because “TopK” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about TopK.
Official links
Tools that pair well with TopK
Common stack mates teams adopt alongside TopK, with the specific reason each pairing earns its keep.
Pinecone
Pinecone is a managed vector database and knowledge engine for RAG, semantic search, and agent retrieval.
Vespa AI
Vespa AI is an open-source AI search platform for hybrid vector, text and structured retrieval with ML ranking in one query pass.
RAGFlow
Open-source RAG engine with built-in ETL, hybrid vector/BM25 search and visual agent orchestration for enterprise AI agents.
Featured Head-to-Head Comparisons
Topk vs Spider Cloud
Choose Spider Cloud if your primary need is crawling and scraping the web to feed data into AI agents or RAG pipelines, especially with a tight budget thanks to its free tier and open-source core. Choose TopK if you need a high-performance, accurate search engine over your own data, with hybrid and multi-vector retrieval, and you're willing to pay for enterprise-grade reliability.
Topk vs Temporal Ai
Choose Temporal AI if you need durable, fault-tolerant orchestration for AI agents and workflows with automatic retries and human-in-the-loop. Choose TopK if your priority is high-quality, accuracy-critical search over structured and unstructured data for RAG systems. They solve different problems: one for execution reliability, the other for retrieval quality.
Topk vs Screenplayiq
ScreenplayIQ and TopK serve entirely different domains: ScreenplayIQ is for screenwriters needing marketability feedback, while TopK is for developers building high-accuracy search. For a screenwriter, ScreenplayIQ’s box office prediction is unique; for a developer, TopK’s hybrid search and sub-100ms latency at scale are unmatched. Choose based on your core problem—not comparable.
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