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Knowi VS Objects

Compare Knowi VS Objects and see what are their differences

Knowi

Knowi is an agentic analytics platform. AI agents work inside the data layer to query SQL, NoSQL and APIs directly, join across sources without ETL, and build dashboards teams can use or embed in their own product.

Rating
0 reviews
Pricing
Free Free trial
Alternatives Is it good?
Objects

An online tool to create instructions and user manuals for providing quality customer care

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0 reviews
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SetMe
Remote support software that just works. Securely access attended or unattended computers from anywhere. sponsored
Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.

Base details

Website, pricing, platforms and company facts side by side.

Knowi
Objects
Website knowi.com objects.to
Pricing
Free Free trial Official pricing
Official pricing
Company Startup from the United States · 20 - 49 employees · 2015 —
Listed in
Analytics Product Analytics IoT Platform Business Intelligence Embedded Analytics Data Visualization Reporting & Dashboard AI Agents Agentic Analytics Analytics Dashboard Marketing Analytics +8
Customer Care Customer Support SaaS

About Knowi and Objects

In their own words, as submitted to SaaSHub.

Knowi
Objects

Knowi is the Agentic Analytics Platform. It unifies data from anywhere, adds a governed semantic layer, and deploys AI agents that analyze, monitor, and act on live business data. Traditional BI tools bolt AI onto dashboards. Knowi was built AI-first. The semantic layer defines your metrics,...

Read more about Knowi

No description of Objects yet.

Features and specs

What each product offers, as listed by its team.

Knowi 10 features
Objects 5 features
  • Agents Inside the Data Layer
    AI agents connect to schemas and the query engine directly rather than sitting on top of finished dashboards, so they build queries, widgets and dashboards instead of only answering questions about ones that already exist.
  • Native NoSQL Analytics
    Query MongoDB and Elasticsearch in their own query language, including nested documents and arrays, without a BI connector or a separate flattening step.
  • Cross-Source Joins Without ETL
    Blend MongoDB, PostgreSQL, Snowflake, Databricks SQL, Trino, Salesforce and REST APIs in a single query, without first moving the data into a warehouse.
  • Semantic Layer as a Dataset Service
    Curated datasets, governed business definitions and a shared glossary the data team controls, so plain-English questions resolve against approved metrics rather than raw tables.
  • Wide Range of Integrations
    The platform connects to relational databases, NoSQL stores, cloud warehouses, SaaS APIs and files, including MongoDB, Elasticsearch, PostgreSQL, MySQL, Snowflake, Databricks SQL, Trino, BigQuery, Redshift, Salesforce and REST endpoints.
  • Real-Time Insights
    Knowi provides real-time data processing and visualization, which enables businesses to access up-to-date insights for timely decision-making.
  • Alerts and Scheduled Reporting
    Threshold and anomaly alerts routed to Slack, email or webhooks, plus scheduled PDF and CSV delivery.
  • MCP Server for AI Assistants
    A Model Context Protocol server so assistants such as Claude can query data, build widgets and create dashboards through Knowi's governed layer.
  • Flexible Deployment
    Run on Knowi Cloud, inside your own VPC, or fully on-premise.
  • No-Code Data Analytics
    Knowi offers a no-code platform that allows users to perform data analytics tasks without needing in-depth programming skills, making it accessible to data-driven teams.
  • Decentralized Object Storage
    Objects.to provides decentralized storage solutions, allowing users to store data across distributed networks rather than relying on a single centralized server, which enhances data resilience and reduces single points of failure.
  • Web3 and Blockchain Integration
    The platform is designed with Web3 principles in mind, making it well-suited for developers building decentralized applications (dApps) that need reliable and censorship-resistant storage.
  • Simple API and Developer Experience
    Objects.to offers a straightforward API that makes it relatively easy for developers to integrate decentralized storage into their projects without needing deep expertise in the underlying protocols.
  • Content Persistence
    Data stored through Objects.to benefits from content-addressable storage mechanisms, helping ensure that files remain available and verifiable over time without risk of link rot or unauthorized modification.
  • Cost-Effective Storage
    Compared to traditional cloud storage providers, Objects.to can offer competitive pricing by leveraging decentralized storage networks, potentially reducing costs for developers and businesses storing large amounts of data.

Possible disadvantages

  • Limited Mainstream Adoption
    Objects.to is a relatively niche platform compared to established cloud storage providers like AWS S3 or Google Cloud Storage, which means fewer community resources, tutorials, and third-party integrations are available.
  • Performance and Latency Concerns
    Decentralized storage can sometimes suffer from higher latency and slower retrieval speeds compared to centralized cloud services that have globally distributed CDNs and optimized infrastructure.
  • Reliability and Uptime Uncertainty
    As a smaller and newer platform, Objects.to may not offer the same level of guaranteed uptime and SLAs that enterprise-grade centralized storage providers commit to.
  • Learning Curve for Non-Web3 Developers
    Developers unfamiliar with decentralized storage concepts, content addressing, and Web3 paradigms may face a steeper learning curve when adopting Objects.to compared to traditional storage solutions.
  • Limited Documentation and Support
    Being a smaller platform, Objects.to may have less comprehensive documentation, fewer support channels, and slower response times for troubleshooting compared to major cloud providers with dedicated support teams.

Analysis

An editorial look at what each product does well and who it suits.

Knowi
Objects

No analysis of Knowi yet.

Overall verdict

  • Objects.to is a niche link-in-bio and personal landing page tool. It appears to offer a minimalist way to consolidate links, but it has limited brand recognition compared to major competitors like Linktree, Bio.link, or Beacons, and detailed independent reviews or long-term reliability data are scarce.

Why this product is good

  • Simple, minimalist interface for creating a single landing page
  • Likely free or low-cost tier for basic use cases
  • Quick setup for consolidating multiple links in one place
  • Lightweight alternative if you dislike bloated link-in-bio tools

Recommended for

  • Individuals wanting a very basic, no-frills link page
  • Users experimenting with alternatives to mainstream link-in-bio services
  • Small creators who don't need advanced analytics or customization
  • Those prioritizing simplicity over extensive design options

Videos

Walkthroughs and reviews on video.

Knowi 1 video + Add
Objects 0 videos + Add

Knowi: End-to-End AI Analytics Platform - Architecture Overview

No Objects videos yet. You could help us improve this page by suggesting one.

Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100
Knowi
Objects
100% 100%
Analytics
0% 0%
0% 0%
SaaS
100% 100%
100% 100%
IoT Platform
0% 0%
0% 0%
Customer Support
100% 100%

Questions & Answers

As answered by people managing Knowi and Objects.

Who are some of the biggest customers of your product?

Knowi's answer

Verizon Telstra Everbridge ConvergeOne NJM Insurance Volante Systems intlx Solutions Alteas Health Hometime Tata

What makes your product unique?

Knowi's answer

Knowi runs AI agents inside the data layer rather than on top of finished dashboards. The agents reach schemas and the query engine directly, which means they can build queries, widgets and dashboards, not just answer questions about ones that already exist.

That architecture comes from what Knowi was built to do: query data where it already lives. It speaks MongoDB and Elasticsearch in their own query language, including nested documents and arrays, and it joins across MongoDB, PostgreSQL, Snowflake, Databricks SQL, Trino, Salesforce and REST APIs in a single query without first moving anything into a warehouse.

Knowi also runs its own AI by default, with OpenAI and Claude available as optional integrations rather than requirements, and it deploys to cloud, your own VPC, on-premises via Docker or Kubernetes, or air-gapped.

Why should a person choose your product over its competitors?

Knowi's answer

Three practical reasons.

Your data does not have to be relational first. Most BI tools need a warehouse and an ETL pipeline before you see a chart, which means unstructured, nested and API data either gets flattened or gets left out. Knowi queries those sources natively, so the modelling work you would normally do up front becomes optional.

The AI is part of the query path, not a chat box on the side. Many platforms added a copilot that describes existing dashboards. Knowi's agents have access to the schema and the query engine, so they can create new datasets, widgets and dashboards from a question.

You control where it runs and which model touches your data. Knowi AI is the default, third-party models are optional, and deployment can be Knowi Cloud, your own VPC, on-premises, or air-gapped. Knowi is SOC 2 Type II certified, GDPR compliant, and offers a HIPAA BAA.

How would you describe the primary audience of your product?

Knowi's answer

Data, engineering and product teams at mid-market and enterprise companies whose data is spread across more than one kind of system: NoSQL alongside SQL, warehouses alongside SaaS APIs and documents.

Two buying patterns show up most often. Internal analytics teams who need governed self-service across sources without building a pipeline for every question. And product teams who need customer-facing, multi-tenant analytics embedded inside their own application with row-level access control.

By industry, the customer base skews to telecom, healthcare, manufacturing, SaaS and adtech, proptech and e-commerce. Knowi is sold through a sales team and priced per deployment rather than by public self-serve tier.

What's the story behind your product?

Knowi's answer

Knowi was founded in 2014, at the point where a lot of production data had stopped being relational. Teams were running MongoDB and Elasticsearch, and the BI tools of the day all assumed a star schema in a warehouse. Getting a dashboard meant building a pipeline first, and anything nested or semi-structured got flattened or dropped along the way.

Knowi was built the other way round: connect to the source, query it in its own language, and join across sources at query time instead of moving the data. Cross-source joins, post-query transformation and a dataset layer that data teams could govern followed from that starting point, and embedded analytics came from customers who wanted to give the same views to their own users.

The AI work is a continuation rather than a pivot. Because Knowi already owned the query path across every connected source, AI agents could be placed inside the data layer with access to schemas and the query engine, instead of being bolted onto a finished dashboard.

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