Sold by: Pinecone
Pinecone Nexus is a knowledge engine for agentic AI. It moves reasoning from retrieval time to knowledge-compilation time, turning your proprietary data into trusted, governed, task-ready knowledge that agents can query directly. Instead of dumping raw chunks into every inference call, Nexus compiles specialized contexts your agents can complete tasks against, delivering higher task completion, faster time-to-completion, per-field citations, and up to 90% less token spend. Runs on the Pinecone database that already powers 800,000+ developers.
4.4
Overview
Pinecone Nexus: the knowledge engine for agentic AI, deployable in your AWS account
Agents now consume knowledge infrastructure faster than humans ever did, and traditional retrieval systems weren't built for them. An agent burns roughly 85% of its effort in brute-force retrieval loops, with task completion stalling at 50-60%, unpredictable latency, ungovernable outputs, and runaway token costs. Pinecone Nexus fixes this at the architectural level, and runs natively on AWS.
Two core components:
Context compiler - Give it source data and a task spec, and it compiles raw data into task-optimized contexts. It's iterative: it experiments with representations, evaluates them against the task, and converges on the precise structure the agent needs. Work that used to run at inference time happens once at compile time and improves every iteration.
Composable retriever - Serves responses precisely for how each agent consumes knowledge: low-latency, grounded, composable across sources, with typed fields, per-field citations and confidence levels, and output shaped exactly as the agent specified.
KnowQL, the declarative query language for agents. Six core primitives spanning intent, filter, provenance, output shape, confidence, and budget, returning structured, grounded knowledge composable across heterogeneous sources through a single interface.
Measured impact. Task completion above 90%, up to 30x faster time-to-completion, answers that are cited, scored, and auditable, and up to 90% reduction in token spend.
Built on the Pinecone foundation. Nexus runs on the Pinecone database used by more than 800,000 developers and 9,000 customers.
Nexus is built for teams deploying agents over complex proprietary knowledge in financial services, healthcare, legal, enterprise SaaS, and beyond.
Highlights
- A knowledge engine, not a retrieval system. Nexus moves reasoning from retrieval time to compilation time, delivering task completion above 90% and up to 90% less token spend.
- Runs in your own AWS account (BYOC). The data plane deploys inside your VPC while Pinecone manages the control plane, so your data never leaves your AWS environment.
- Governed, grounded, agent-ready. Per-field citations with confidence scores, RBAC-scoped context, versioned artifacts, and full source traceability, queryable through KnowQL.
Details
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Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on the duration and terms of your contract with the vendor, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/12 months | Overage cost |
|---|---|---|
Pinecone Billing Unit | $100,000.00 |
Vendor refund policy
Please contact us at support@pinecone.io
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Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Resources
Vendor resources
Support
Vendor support
A Pinecone Enterprise plan is required for Nexus BYOC deployments. Technical support is included with all Pinecone Enterprise plans. For more information regarding support SLAs, please see each plan's details on the Pinecone pricing page.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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Customer reviews
Shashank S.
Works Great for Retrieving Relevant Context from Large Datasets
Reviewed on Oct 15, 2026
Review provided by G2
What do you like best about the product?
It works well for applications that need to retrieve relevant information from large datasets and deliver context to LLMs.
What do you dislike about the product?
Debugging search quality can take some trial and error, especially when the retrieved results do not match user expectations.
What problems is the product solving and how is that benefiting you?
Pinecone takes a lot of complexity out of working with embeddings. I like that I can get a vector search system running without spending time managing infrastructure, tuning indexes manually, or worrying too much about scaling.
Piyush T.
Pinecone Makes Production-Ready Semantic Search and RAG Easy
Reviewed on Oct 15, 2026
Review provided by G2
What do you like best about the product?
The good thing about Pinecone is how easy it makes building production-ready AI applications with semantic search and RAG.
What do you dislike about the product?
The main drawback is that costs can increase as data volume and query traffic grow.
What problems is the product solving and how is that benefiting you?
Its managed infrastructure, fast vector retrieval, metadata filtering, and automatic scaling reduce the operational effort required to build reliable AI-powered search and knowledge systems.
Computer Software
Managed Vector Database for AI Projects
Reviewed on Oct 08, 2026
Review provided by G2
What do you like best about the product?
Pinecone made it easy to get started with vector databases without dealing with complex infrastructure. I was able to create an index, add records, and explore semantic search from a clean dashboard. The interface is straightforward, and the managed setup helped me focus on understanding vector search concepts rather than database management. The built-in embedding model integration and index management features were especially useful while learning about AI search and retrieval systems.
What do you dislike about the product?
The initial learning curve can be challenging if you are new to vector databases. Concepts such as embeddings, namespaces, dimensions, and similarity search require some background knowledge. I would also like to see more beginner-focused tutorials and guided examples directly inside the dashboard to help new users understand how to upload data and run searches more quickly.
What problems is the product solving and how is that benefiting you?
Pinecone helps solve the problem of storing and searching vectorized information efficiently. Instead of relying only on keyword matching, it enables semantic search based on meaning and context. While learning retrieval and AI search concepts, I used Pinecone to create a small searchable project index and understand how vector databases support applications such as knowledge retrieval, semantic search, and RAG systems. This reduced the effort required to set up search infrastructure and helped me learn the workflow much faster.
Rahul P.
Fast, Straightforward Vector Search for RAG with Easy Integration
Reviewed on Oct 03, 2026
Review provided by G2
What do you like best about the product?
What I like most about Pinecone is how straightforward it makes vector search and retrieving relevant information based on semantic meaning, rather than relying only on keyword matching.
The setup and integration were relatively simple, and the managed infrastructure meant I didn’t have to spend much time managing database infrastructure myself. Metadata filtering is also useful when I need to narrow down search results based on specific attributes.
I found Pinecone particularly useful for RAG and knowledge-base use cases because retrieval is fast and the APIs are easy to integrate into an application.
The setup and integration were relatively simple, and the managed infrastructure meant I didn’t have to spend much time managing database infrastructure myself. Metadata filtering is also useful when I need to narrow down search results based on specific attributes.
I found Pinecone particularly useful for RAG and knowledge-base use cases because retrieval is fast and the APIs are easy to integrate into an application.
What do you dislike about the product?
The main thing I would improve is visibility around pricing and usage. As the number of vectors, storage, and queries increases, it can become difficult to estimate the overall cost without monitoring usage closely.
There’s also a learning curve around concepts such as embeddings, indexes, namespaces, dimensions, and similarity search for someone who is new to a vector database. More beginner-friendly examples, along with clearer cost-estimation tools, would make the platform easier to understand and adopt.
There’s also a learning curve around concepts such as embeddings, indexes, namespaces, dimensions, and similarity search for someone who is new to a vector database. More beginner-friendly examples, along with clearer cost-estimation tools, would make the platform easier to understand and adopt.
What problems is the product solving and how is that benefiting you?
Pinecone helps solve the challenge of efficiently searching through large amounts of unstructured information, rather than relying entirely on keyword-based search. It lets you store embeddings and then retrieve relevant information based on those embeddings.
Computer Software
Simple and Reliable Vector Search for AI Applications
Reviewed on Sep 28, 2026
Review provided by G2
What do you like best about the product?
What I like most about Pinecone is how straightforward it makes working with vector data. Creating indexes and running embedding searches is fairly easy, and overall the platform works well for semantic search and other AI applications.
What do you dislike about the product?
The initial setup can take a bit of time to wrap your head around, especially if you’re working with indexes, namespaces, and embeddings for the first time. For new users, some of these concepts can feel fairly technical at the beginning.
What problems is the product solving and how is that benefiting you?
Pinecone simplifies vector search and retrieval for AI applications. It makes it straightforward to store embeddings and quickly surface relevant information, which is especially useful when building semantic search and retrieval-augmented applications.