TopK
Unified retrieval engine for high-recall AI search and RAG
For teams that can handle a code-first setup, TopK delivers measurably better retrieval accuracy than typical vector stores, especially for RAG in finance, legal, or medical. Pricing is usage-based and transparent—but watch costs, as they scale with query memory and processing. For accuracy-obsessed teams, it's a strong choice over Pinecone or Bedrock Knowledge Base.
Verified 5d ago · liveness 70/100 · cite: rightaichoice.com/tools/topk
- Developers building RAG systems needing high-recall retrieval with multi-vector and hybrid search
- Enterprise teams in finance, legal, and medical domains requiring accurate, citation-backed answers
- AI application builders implementing agent memory with persistent, searchable context
- Teams building recommendation systems with efficient filtering and online updates
- Non-technical users needing no-code, fully managed search solutions
- Applications focused on high write throughput or real-time indexing at extreme rates
- Teams expecting a traditional relational database for transactional workloads
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Skip TopK if you need a no-code search solution, require a free self-hosted option, or expect a transactional database; it's a developer-focused, usage-priced retrieval engine.
Query memory costs $20.00 per GiB, so if you keep a large hot index, this line item can quickly dominate your bill.
With a $10 free credit and pay-as-you-go pricing, TopK suits startups and small teams that want to prototype cheaply, but costs scale with usage. Compared to Pinecone's per-pod pricing, TopK's metered model can be more cost-effective for spiky workloads, but may be pricier for constant high-volume. Enterprises with large but predictable workloads might find reserved pricing elsewhere more economical.
In short
TopK — Unified retrieval engine for high-recall AI search and RAG. Best for Developers building RAG systems needing high-recall retrieval with multi-vector and hybrid search, Enterprise teams in finance, legal, and medical domains requiring accurate, citation-backed answers, AI application builders implementing agent memory with persistent, searchable context. Free to use.
What's new in TopK
Checked 5 days agoAcross the latest 5 updates: 2 feature updates and 3 news mentions.
The Fastest Regex Is the One You Don’t Run
Explains how optimizing search often means avoiding regex by redesigning access patterns, using sparse n-grams for speed.
RAG Is Broken for Agents. Here's How We Fixed It.
Argues dense embedding based RAG fails for agents and describes how multi-vector (late interaction) retrieval fixes it.
TopK SQL: A Search Query Language
Announces TopK SQL, enabling semantic and hybrid search via the Postgres wire protocol from any Postgres client.
High-Quality Search, Out of the Box
Details how TopK provides high-quality search results by default, reducing the need for manual tuning.
SMVE: Multi-Vector Retrieval That Just Works
Introduces SMVE, TopK's multi-vector retrieval approach that works out-of-the-box for better accuracy.
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.
- +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: September 2026
How we score →Key Features
- Hybrid search combining dense, sparse, late-interaction, keywords, and filters in a single query
- Multi-vector retrieval with late interaction (SMVE)
- BM25 keyword search
- Custom scoring with multiple ranking signals
- File Search with grounded answers and precise citations
- Agentic queries for File Search with sub-queries and reasoning
- Research queries with deep multi-step citations
- Document processing with OCR for PDFs, DOCX, images, and text
- Built-in multi-vector embedding
- SQL compatibility via Postgres wire protocol
- Native Python SDK
- JavaScript SDK
- Rust SDK
- MCP server integration for agent tooling
- Private deployment to VPC or on-prem
About TopK
TopK is a search engine built for accuracy-critical AI applications, bundling retrieval, inference, and document processing into one API. It's designed for developers and enterprises in correctness-sensitive domains like finance, legal, and medical, where search quality directly impacts decisions. TopK supports structured and unstructured data with hybrid search, multi-vector retrieval, and custom scoring in a single query — all served from object storage for lower cost and unlimited scale. The platform's unified retrieval engine combines dense and sparse vectors, late-interaction multi-vector search, BM25 keyword filtering, and metadata filters in one call. This delivers up to 80% higher recall than typical vector stores, with sub-100ms latency at billion-scale and p99 as low as 17ms on 10M documents. Data lives on object storage, cutting costs by 10x and enabling scale to billions of documents per partition. A SQL compatibility layer via the Postgres wire protocol lets you run semantic and hybrid search as ordinary SQL, and native SDKs cover Python, JavaScript, and Rust. File Search turns private documents into grounded knowledge for agents, providing answers with precise citations rather than ranked results. It handles complex formats like PDFs, DOCX, and images with OCR and parsing, and supports agentic queries and deep research queries. Accuracy benchmarks show top scores: 84.59% finance, 88.22% legal, 91.39% medical, and 87% industrial, judged on ViDoRe V3. TopK positions itself against alternatives like Pinecone or Bedrock Knowledge Base with a focus on recall, cost efficiency, and developer experience. It's a strong fit for teams building RAG systems, agent memory, or semantic search that need high accuracy and are comfortable with a technical setup. Recent updates include SMVE multi-vector retrieval and the TopK SQL query language, both improving out-of-the-box accuracy and developer experience.
Behind the Verdict
TopK stands out in the crowded vector database space by focusing on retrieval quality rather than raw speed alone. The unified retrieval engine—combining dense, sparse, multi-vector, and keyword search in a single query—directly addresses a common pain point: choosing the right retrieval method for your data. The multi-vector late interaction approach (SMVE) is particularly compelling for nuanced semantic understanding, and TopK's benchmark claims (up to 80% higher recall, sub-100ms p99) are impressive if they hold up in your workload. The architecture on object storage is a differentiator. Instead of managing your own compute cluster, you store data in S3, GCP, or Azure Blob, and TopK spins up compute on demand. This can dramatically lower costs for spiky or large-scale workloads. The SQL compatibility layer (via Postgres wire protocol) is also a nice touch—you can run semantic search with the same tools you already use for SQL, lowering the learning curve. However, this is not a no-code tool. You'll be writing Python, JavaScript, or Rust code, or writing SQL queries that call semantic functions. There's a free $10 credit to test, but beyond that, costs depend on many variables (storage, write requests, query memory, compute, egress). The pricing calculator gives an estimate, but you'll need to monitor usage carefully to avoid surprises. Also, TopK is optimized for search and retrieval, not as a transactional database or a real-time data store with high write throughput. If you need a general-purpose database, look elsewhere. In terms of fit, TopK is ideal for teams building RAG applications, agent memory, or semantic search across large datasets where accuracy matters more than minimal latency. It's also a strong choice for enterprises with strict data residency requirements, thanks to private deployment options. For hobby projects or simple full-text search, simpler tools like Postgres with pgvector might be more than enough. But if you're dealing with complex documents and need citation-backed answers, TopK's File Search is a standout feature.
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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 have a set of PDFs and want to create a searchable knowledge base with citation-backed answers. You sign up, create an index, upload documents via the Python SDK, and use the File Search API to ask questions. Within a day you have a functional prototype that returns grounded answers with references.
Outcome: Your RAG agent now provides accurate answers with citations, improving user trust. You can iterate on prompts and indexing configurations to boost accuracy even further.
You need to search across thousands of earnings reports. Using TopK's SQL compatibility, you run semantic queries against your data with filters for fiscal year and custom scoring. You integrate the search into your internal dashboard with the JavaScript SDK.
Outcome: Your team can quickly find relevant financial data, with sub-100ms latency, and the custom scoring lets you weight recent or high-quality sources as needed.
Your chatbot needs to remember user preferences and past interactions. You store memories as vectors in TopK with metadata like timestamp and user ID. When a user returns, you query for relevant memories, using custom scoring to prioritize recent ones.
Outcome: Your chatbot now provides personalized responses by recalling previous sessions, improving user engagement and retention.
Use Cases
- Build accurate RAG agents that ground answers in private documents with citations.
- Implement 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.
- Perform deep multi-step research queries with citations.
Models Under the Hood
as of 2026-08-30
Limitations
- TopK is a unified retrieval engine for search and RAG, requiring developer familiarity with SDKs (Python/JS/Rust) or SQL; it is not a no-code tool.
- Pricing is usage-based with multiple line items (storage, writes, query memory, compute, egress), and the cost estimator is indicative only.
- There is a $10 free credit but no permanent free tier, and costs scale with workload.
- The platform focuses on retrieval, not general-purpose or transactional database workloads.
as of 2026-08-27
Verification history
We have re-verified TopK 6 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-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
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
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.
Plans compared
For each published TopK tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
Developers evaluating TopK with $10 of free credits, perfect for small prototypes and proof-of-concept testing without a credit card.
What this tier adds
Starter tier with $10 free credits and access to all features; no credit card required.
Usage-based
Pay-as-you-go
Ideal for
Production workloads with metered usage; ideal for startups and enterprises that want to pay only for what they use, with costs scaling predictably.
What this tier adds
Adds detailed per-use pricing for storage, writes, queries, embeddings, and document processing; no fixed monthly fee.
Where the pricing makes sense
The company stage and team size where TopK's pricing actually pencils out — and where peers do it cheaper.
With a $10 free credit and pay-as-you-go pricing, TopK suits startups and small teams that want to prototype cheaply, but costs scale with usage. Compared to Pinecone's per-pod pricing, TopK's metered model can be more cost-effective for spiky workloads, but may be pricier for constant high-volume. Enterprises with large but predictable workloads might find reserved pricing elsewhere more economical.
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.
For a developer familiar with vector databases, you can get a basic index running within minutes using the Python SDK and the $10 free credit. For more complex use cases like File Search with document processing, expect to spend a few hours integrating and tuning. Enterprise deployments to VPC or on-prem may take a few weeks to plan and execute.
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: Export your vectors and metadata, then re-upload to TopK using the SDKs. TopK's unified retrieval may require adjusting your query logic to benefit from hybrid search.
- ↗To Milvus or Qdrant: If you need more control over infrastructure, you can export your data from TopK and re-import into a self-hosted vector database, though you'll lose the managed compute and built-in embeddings.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with TopK
Common stack mates teams adopt alongside TopK, with the specific reason each pairing earns its keep.
RAGFlow
Open-source RAG engine with advanced ETL, hybrid search, and visual agent orchestration for enterprise AI.
Vespa
Vespa is a distributed serving engine that unifies vector, text, and ML ranking for AI search at scale.
LanceDB
Open-source multimodal lakehouse for AI data curation, feature engineering, search, and training.
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.
Alternatives to TopK
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