Compare / Qrvey

Draxlr vs Qrvey for embedded analytics

Both embed multi-tenant analytics in your product. Draxlr goes further for your customers and asks less of your team.

A full analytics workspace for each customer
Live on your SQL database, nothing to load
AI built in, no LLM account to connect
Hosted for you, nothing to run in your cloud

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Short Answer

Is Draxlr a good Qrvey alternative?

Yes. Draxlr is an embedded analytics platform for SQL databases and a direct Qrvey alternative. Both embed white labeled, multi-tenant analytics in your product and let customers build their own dashboards. Draxlr gives those customers more to work with, a full analytics workspace that queries your database live, with AI built in. It also takes the infrastructure off your team. Draxlr runs the platform, while Qrvey installs into your own AWS or Azure account for your team to operate and upgrade.

Embedding. Each customer gets a full analytics workspace in your app, with the dashboards you share, their own dashboards and saved queries, and a no-code builder, SQL editor and AI assistant to build with.
Hosting. Draxlr runs the platform, so there is no cluster, search index or upgrade window for your team to manage.
Live data. Fourteen SQL databases and warehouses, connected live over read-only access with nothing loaded first.
AI. The assistant is built in and credits come with every plan, so there is no LLM provider account to connect.
Evaluation. Published pricing and a 7-day free trial with no credit card and no sales call.

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At a Glance

How Do Draxlr and Qrvey Differ?

Eight capability rows that settle most evaluations. Commercial terms have their own table further down.

Comparison	Draxlr	Qrvey
What customers can build	Dashboards and saved queries, with query builder, SQL and AI	Dashboards, reports, formulas and custom datasets, with AI
Who runs the platform	Draxlr, or self-hosted on Enterprise	Your team, in your AWS or Azure account
Row-level security	Table and row level, on every query	Row, column and schema level
Live queries on your database	Live connection, 14 SQL sources	Live Connect on 6 SQL sources, with limits
AI inside the embed	Built in, no LLM account needed	Sidekick, on an LLM provider you connect
Embed methods	HTML, React, Vue, backend API	JavaScript widgets for React, Angular and more
Reports and automation	Scheduled email reports, Slack and email alerts	Pixel-perfect reports, no-code workflows
Data sources	14 SQL databases and warehouses	SQL sources, plus MongoDB, S3 and APIs loaded into Qrvey

Qrvey details come from its own pricing page and product documentation. Last verified 30 October 2026. Sources Qrvey pricing, Qrvey AWS deployment docs, Qrvey Live Connect docs, Qrvey AI provider docs, Qrvey developers, Draxlr pricing, Draxlr full app embedding.

Embedded Analytics

What Draxlr and Qrvey each are

Both are embedded analytics platforms. Each puts dashboards inside your app under your branding, keeps every customer to their own data and lets customers build reports of their own. Where they part ways is in what sits between your database and the customer, and in whose cloud the platform runs.

Draxlr sits directly on your SQL database and queries it live. The embed token your backend signs carries the values row-level security applies to every query, which is why full app embedding can safely give each customer saved queries, a no-code query builder, a SQL editor and an AI assistant alongside the dashboards you share. Draxlr runs the platform, with self-hosting available when data has to stay in your network.

Qrvey installs into your own AWS or Azure account and works through datasets. Pro connects to an analytics-ready database you already have, and Ultra adds Qrvey's own data engine and transformation layer. On top, customers build dashboards with a drag-and-drop chart builder and custom formulas, design pixel-perfect reports, automate actions in a no-code workflow builder and ask Sidekick questions.

So the trade is concrete. Qrvey gives you control of the stack and a broad set of report and workflow tools, in exchange for running it. Draxlr removes the stack and goes deeper on self-service, down to the SQL a customer writes for a question nobody planned for.

Key Differences

Where Draxlr and Qrvey Split

Qrvey is a capable platform for multi-tenant SaaS. These are the points where teams find a different fit, and what Draxlr does about each.

Customers can write the query nobody planned

Qrvey lets customers build dashboards, charts, formulas and custom datasets, and ask Sidekick questions in plain language. None of its product pages offers them a SQL editor. Draxlr's full app embedding gives each customer a workspace with the dashboards you share, their own saved queries, a no-code query builder, a SQL editor and an AI assistant, and you choose which of those tools each embed exposes.

Nothing to install, size or upgrade

Qrvey installs with Terraform into your own cloud. On AWS that is an EKS cluster, a PostgreSQL database and a search cluster, with at least 56 vCPUs of EC2 quota, and each upgrade repeats the install and can take analytics offline for up to two hours. Draxlr runs the platform, and when data must stay in your network the Enterprise plan self-hosts with Docker instead.

Row-level security that covers ad-hoc SQL

Both platforms keep tenants apart well, and Qrvey adds column and schema rules inherited from your app. Draxlr applies row-level security from the group and attribute values your backend sends with the embed token, on every dashboard, saved query, SQL statement and AI answer. That is what makes it safe to hand a customer a SQL editor.

Live queries are the default, not a mode

Qrvey's Live Connect reaches six SQL sources, and its docs list limits on transformations, calculated fields and data views, with snapshots needing a managed dataset that copies your data into Qrvey. Draxlr connects live to all fourteen of its SQL databases and warehouses over read-only credentials, so a new column is there the moment you add it and nothing has to be kept in sync.

AI that comes with the plan

Qrvey's AI features run on an LLM provider you connect with your own account, such as OpenAI, Azure OpenAI or Claude in Amazon Bedrock, and that provider bills the usage. Draxlr AI is built in. It writes the SQL, shows the query it ran and lets customers edit it, with 300 credits a month on Power and 200 more for $10. When credits run out, the query builder and SQL editor keep working.

A price you can read before the first call

Qrvey publishes no prices, so a budget starts with a quote request and a cloud bill on top. Draxlr publishes every plan with its database, user and AI limits, bills monthly or annually and never charges for the customers who view your dashboards. The embedded analytics pricing comparison sets those plans against the wider market.

Feature by Feature

Draxlr vs Qrvey, Feature by Feature

The two overlap on embedding basics. Draxlr pulls ahead on what customers can query and on hosting, and Qrvey on reports, workflows and non-SQL data.

Feature	Draxlr	Qrvey
Embed a single dashboard	Yes	Yes
Customers write their own SQL in the embed	Yes	Not described
Customers build and save their own dashboards	Yes	Yes
Customers save their own queries	Yes	Custom datasets
AI assistant inside the embed	Yes	Yes
AI works without your own LLM account	Yes	No
Row-level security for each customer	Yes, table and row level, on every query	Yes, row, column and schema level
Live queries with nothing loaded first	Yes, live connection	Live Connect, with limits
Vendor-hosted option	Yes	No, deploys to your cloud
Run inside your own network	Yes, on Enterprise	Yes, in AWS or Azure
White labeled branding in the embed	Yes	Yes
Backend API for embed tokens	Yes	Yes
MCP server for AI assistants	Yes	Yes
Pixel-perfect report designer	No	Yes
No-code workflow builder	Slack and email alerts	Yes

Scored from each vendor's public pricing pages and product documentation. Not described means Qrvey's pages do not mention it. Last verified 30 October 2026. Sources Qrvey pricing, Qrvey AWS deployment docs, Qrvey Live Connect docs, Qrvey AI provider docs, Qrvey developers, Draxlr pricing, Draxlr full app embedding.

Hosting

Who Runs the Platform

The biggest practical difference between the two is where analytics runs and who keeps it running.

How Draxlr is hosted

Draxlr runs the platform and ships every update itself, so there is no cluster to size, no search index to tune and no upgrade window on your calendar. It connects to your SQL database over read-only credentials and queries it in place. When data has to stay in your network, the Enterprise plan runs Draxlr on a dedicated server in the region you choose, or self-hosted with Docker in your VPC or on-premise, alongside SSO and audit logs.

How Qrvey is hosted

Qrvey is built to run inside your cloud. Its guides install it with Terraform into your AWS or Azure account, and on AWS that means an EKS cluster, a PostgreSQL database on Amazon RDS and a managed OpenSearch domain or an in-cluster Elasticsearch, all billed to you. Upgrades repeat the install with a new version and can take up to two hours of downtime. Qrvey Support and its paid Embedded Services can help with setup and tuning.

Connectors

Works with the SQL database you already run

Pricing Snapshot

What Each One Costs

Both price on a flat rate with unlimited customers. Draxlr publishes its plans and includes hosting and AI, while Qrvey quotes on request and runs on your cloud and your LLM account.

Pricing	Draxlr	Qrvey
Starting price	$75 / month with embedding	Quote on request
Pricing model	Flat rate per plan	Flat rate or perpetual licence
Customer viewers	Unlimited on every plan	Unlimited tenants and users
Infrastructure	Included, Draxlr runs it	Your own cloud bill and upkeep
AI usage	300 credits a month on Power, 200 more for $10	Billed by the LLM provider you connect
Published pricing	Every plan	None, quote by email within 24 hours
Free trial	7 days, no credit card, no call	Developer Playground

Qrvey's licence covers the software. Cloud infrastructure and LLM usage are billed to your own accounts. Last verified 30 October 2026. Sources Qrvey pricing, Qrvey AWS deployment docs, Qrvey Live Connect docs, Qrvey AI provider docs, Qrvey developers, Draxlr pricing, Draxlr full app embedding.

Draxlr Pricing

What Draxlr Costs

Premium covers white labeled dashboard embedding, Power adds the full app embedding and row-level security that most Qrvey evaluations come down to, and Enterprise brings self-hosting, SSO and audit logs.

Premium

$75/ month

or $600 a year

White labeled dashboard embedding for teams putting analytics in front of customers.

Embed dashboards with HTML, React, Vue or the backend API
White labeling included
Filters and drill-downs
Unlimited external viewers
2 databases, 10 internal users

Start free trial

Power

Closest to Qrvey

$250/ month

or $2,000 a year

Full app embedding with row-level security, the multi-tenant self-service Qrvey teams are looking for.

Everything in Premium
Full app embedding for each customer
Row-level security across dashboards, queries and AI
Customer-owned dashboards, queries and SQL editor
5 databases, 30 internal users

Start free trial

Enterprise

$500/ month

or $3,800 a year

For teams that need analytics inside their own network, with governance to match.

Everything in Power
Dedicated server or self-hosting
SSO and audit logs
Custom databases, users and AI credits
SLA support

Every plan includes unlimited external viewers. See the full plan comparison for database, user and AI credit limits, or the Enterprise page for governance and deployment details.

Decision Guide

Which One Should You Pick?

Both handle multi-tenant embedding properly, so the choice rests on where analytics should run and how far customers should go on their own. If you are also weighing Luzmo, Embeddable, Reveal, GoodData or Sisense, the roundup of Qrvey alternatives compares them on the same terms.

Choose Draxlr if

Each customer should get their own analytics workspace, with a no-code query builder, saved queries and AI
You would rather not run a Kubernetes cluster and search index in your own cloud account
Your data lives in a SQL database and should be queried live, with nothing copied into a separate engine
Row-level security has to hold across ad-hoc SQL and an AI assistant
AI should work from day one, without an LLM provider account to set up and govern
You want to see pricing and start building before any sales conversation

Choose Qrvey if

Analytics must run inside your own AWS or Azure account and you have a platform team to operate it
Customers need pixel-perfect, print-ready reports
You want a no-code workflow builder that triggers actions from your data
Key data sits in MongoDB, S3 files or APIs and you want Qrvey to load it
You want to choose and pay for your own LLM provider
A perpetual licence fits how you buy software

Migration

Moving embedded analytics from Qrvey to Draxlr

There is no data engine to load and no dataset layer to recreate, so a move comes down to four steps.

Connect the database Qrvey reads

Point Draxlr at the same SQL database over read-only credentials. If Qrvey loaded a copy into managed datasets, connect to the SQL source those datasets came from instead.

Rebuild the dashboards you ship

Dashboards are rebuilt rather than imported. The no-code query builder covers most charts, the SQL editor takes anything a custom dataset was doing, and the AI assistant can draft a dashboard from one prompt. The Draxlr team can also help you build them through Professional Services.

Carry tenant rules into the embed token

The customer identifier your backend puts in Qrvey's security token is what Draxlr needs. Send it as each customer's row-level security group and attribute values with the embed token.

Swap the widgets, then retire the stack

Replace Qrvey's widget code with Draxlr's HTML snippet or the React or Vue SDK. Once the last customer has moved, the Qrvey infrastructure in your cloud account can be shut down.

FAQ

Draxlr and Qrvey, Answered

Hosting, embedding, AI, migration and pricing questions from teams weighing the two.

Is Draxlr a good Qrvey alternative?

Yes, for teams embedding analytics in their product. Draxlr is an embedded analytics platform for SQL databases that covers the same core ground as Qrvey, meaning white labeled analytics inside your app, row-level security for each customer and dashboards customers can build themselves. It goes further on what customers can do, because full app embedding gives each one saved queries, a no-code query builder, a SQL editor and an AI assistant, while Qrvey's product pages describe no SQL editor for embedded users. Draxlr also runs the platform for you, where Qrvey installs into your own AWS or Azure account for your team to operate. Qrvey is the stronger fit if you need pixel-perfect reports, a no-code workflow builder, or data loaded from MongoDB, S3 files or APIs.

Should I choose Draxlr or Qrvey?

Choose Draxlr if your data sits in a SQL database, your customers should be able to explore it themselves with a no-code query builder and AI, and you would rather not operate analytics infrastructure in your own cloud. Choose Qrvey if analytics has to run inside your own AWS or Azure account and you have a platform team to run it, if customers need pixel-perfect reports or no-code workflows, or if key data lives outside SQL. On white labeling, multi-tenant security, customer-built dashboards and an embedded AI assistant the two are close, so the decision usually turns on hosting and on how far customer self-service should go.

How does Draxlr compare to Qrvey on embedding?

Qrvey embeds as JavaScript widgets that work in React, Angular and other frameworks, covering full dashboards, single panels, pixel-perfect reports and automation, authenticated with a short-lived token from your backend. Draxlr embeds through an HTML snippet, React and Vue SDKs and a backend API. Draxlr's full app embedding puts a complete analytics workspace in your product, where each customer opens the dashboards you share, builds new reports with a no-code query builder, a SQL editor or the AI assistant, and saves them as their own queries and dashboards. You choose which of those tools each embed exposes, and row-level security applies across all of them.

Do I have to run infrastructure for Draxlr like I do for Qrvey?

No. Draxlr runs the platform, so there is no cluster to size, no search index to tune and no upgrade to schedule. Qrvey installs with Terraform into your own AWS or Azure account. Its AWS guide deploys an EKS cluster, a PostgreSQL database on Amazon RDS and an OpenSearch or Elasticsearch search cluster, asks for at least 56 available vCPUs of EC2 on-demand quota, and warns that an upgrade can take up to two hours of downtime. If your data has to stay inside your own network, Draxlr's Enterprise plan runs on a dedicated server in the region you choose or self-hosts with Docker.

How do Draxlr and Qrvey handle AI?

Draxlr's AI is built in. It writes the SQL for a question, runs it, shows the query it used and explains the result, and it can build a whole dashboard from one prompt, with row-level security applied to every answer. AI credits come with every plan, 300 a month on Power with 200 more for $10, and credits cover only AI messages, so the query builder and SQL editor keep working when they run out. Qrvey offers Sidekick, an AI assistant, along with AI agents and an MCP server, and its AI features run on an LLM provider you connect with your own account, such as OpenAI, Azure OpenAI or Claude in Amazon Bedrock, so that provider bills the usage. Draxlr has an MCP server too, which lets your team query databases, save queries and build dashboards from Claude, ChatGPT or Cursor.

How does Draxlr's pricing compare with Qrvey's?

Draxlr publishes every plan. Dashboard embedding starts on Premium at $75 a month, full app embedding and row-level security come with Power at $250 a month, and Enterprise starts at $500 a month with self-hosting, SSO and audit logs. All plans bill monthly or annually with unlimited external viewers. Qrvey publishes no prices. Its Pro and Ultra packages are flat-rate with unlimited tenants, users and dashboards, a perpetual licence is available for both, and a quote arrives by email within 24 hours. With Qrvey, budget separately for the cloud infrastructure it runs on in your account and for the LLM provider its AI uses.

Can I migrate from Qrvey to Draxlr?

Yes. Connect Draxlr over read-only credentials to the SQL database Qrvey reads through Live Connect, or to the SQL source behind any managed datasets Qrvey loaded. Dashboards don't transfer between tools, so rebuild the ones you ship with the query builder, SQL or the AI assistant. Then send each customer's row-level security group and attribute values with the Draxlr embed token, replace Qrvey's widget code with Draxlr's embed snippet or SDK, and retire the Qrvey stack in your cloud account once the last customer has moved.

Which databases does Draxlr connect to?

Draxlr connects to PostgreSQL, MySQL, MariaDB, PlanetScale, CockroachDB, YugabyteDB, Microsoft SQL Server, Redshift, BigQuery, Supabase, Neon, ClickHouse, Databricks and Snowflake, with read-only access and no data copied out of your database. Qrvey's Live Connect queries Snowflake, Redshift, PostgreSQL, Databricks, MySQL 8.0+ and SQL Server directly, and its managed datasets load data from other sources, including MongoDB and S3 files, into Qrvey's own engine.

Can I evaluate Draxlr without talking to sales?

Yes. Every new account starts with a 7-day free trial with access to the Power plan, with no credit card and no sales call, so you can embed a working dashboard on your own database before you commit. Qrvey offers a Developer Playground for trying its dashboard builder, automation and UI customization, and pricing for your own deployment starts with a quote request.

How we compare these tools

Every Qrvey detail on this page comes from Qrvey's own pricing page and its product documentation, which was on version 9.5 when we checked, and every Draxlr figure matches our published plans. Where Qrvey does something Draxlr does not, the tables say so, and where Qrvey's pages are silent we mark it as not described rather than as missing. Figures were last checked on 30 October 2026. Qrvey versions its documentation by release, so check the docs for the release you would run.

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