Apache Superset vs Grafana: when each one fits
Apache Superset is a BI platform for SQL databases, and Grafana is an observability dashboard for metrics, logs and traces. Choose Superset when the people who need answers are analysts, finance or operations staff asking questions of tables in PostgreSQL, MySQL or a warehouse, and they need a SQL IDE, a no-code chart builder and a semantic layer. Choose Grafana when the question is whether a service is healthy right now, and the data arrives from Prometheus-style metrics, log stores or trace backends that Grafana queries in place and alerts on. The two tools overlap in charting, but they start from different data and different users, so the first decision is the source of the data, not the chart type. The licenses also differ, and this affects procurement. Superset is Apache-2.0 and has no paid edition. Grafana is AGPL-3.0-only since version 8.0, and Grafana Enterprise is a separate commercial edition that adds SAML, team sync and SCIM provisioning. Under the Grafana authentication model, SAML and SCIM are not available in the open-source edition, so if your identity rules require them, price Enterprise into the comparison from the start. Ask counsel whether AGPL obligations apply to your planned modifications and hosting before you commit.
Four drivers should decide the shortlist. First, the data source: Superset needs a SQL-speaking database, while Grafana is built around metrics, logs and traces. Second, who builds the dashboards: Superset suits analysts writing SQL, and Grafana suits operators who maintain panels and alert rules. Third, identity: confirm whether you need SAML or group-based role mapping, and check how Superset's AUTH_ROLES_MAPPING and Grafana's edition-specific sign-in options match your directory. Fourth, license terms, including whether Grafana Enterprise is required for your sign-in model.
Many organizations run both. A typical split is Grafana watching service latency and error rates, and Superset reporting on business tables in the same database. Plan for two sets of backups, two upgrade cycles and two access reviews. Before you finalize, check the Grafana authentication documentation against your identity provider and the Superset configuration for the sign-in method you intend to use.
Which questions each tool answers well
This table maps common questions to the tool that answers them more directly, and names the condition under which the other tool is still reasonable. Read each row as a starting point, then check the data sources and team skills in your own environment.
| Scenario | Better fit and why | When the other tool is still reasonable |
|---|---|---|
| Revenue reporting on a data warehouse | Superset. It connects to SQL-speaking databases, offers a SQL IDE and a no-code chart builder, and is built for analysts querying tables and models. | Grafana, if the revenue data already sits in a SQL source Grafana supports and the team mainly needs simple time-series panels with alerting. |
| Service latency on Prometheus data | Grafana. It is built to query, visualize and alert on metrics wherever they are stored, and its panels and alert rules fit time-series monitoring. | Superset, if an existing warehouse already holds exported latency history and the question is a periodic business review rather than live paging. |
| Incident investigation across logs and traces | Grafana. It is designed to explore metrics, logs and traces together, which suits responders moving between a spike, its log lines and the related trace. | Superset, if post-incident analysis needs joins against business tables such as customer or order records stored in a SQL database. |
| Shared executive dashboard | Superset for dashboards built on curated SQL datasets with shared filters and scheduled reports. Confirm report and alert features against your edition and deployment, since async and report features need the caching and worker components. | Grafana, if the executive view is a small set of operational health panels and the organization already runs Grafana for those metrics. |
| Row-level access to business data | Superset, where the team needs row-level restrictions on SQL datasets. Confirm the role model and mapping from your identity provider during evaluation. | Grafana, if access is controlled per data source and folder and the audience is operations staff rather than business users. |
| Single sign-on for a small internal team | Confirm carefully. Superset supports OAuth2 and LDAP with role mapping. Grafana's SAML, team sync and SCIM provisioning require Grafana Enterprise or Grafana Cloud. | Grafana OSS, if OAuth or LDAP covers your identity provider, since its open-source edition supports those methods. |
| Ad hoc exploration by engineers | Grafana for metric and log exploration in the same tool as alerts. Superset for writing SQL against warehouse tables in the SQL IDE. | The other tool, when the data lives in the other system and the engineers already know its query language. |
| Operational alerting that pages on-call staff | Grafana, since alerting is part of its core purpose. Superset's alerts and reports depend on Redis and Celery workers, so plan that operating load if you choose it. | Superset, only for scheduled business alerts such as daily threshold checks on warehouse data, not for paging. |
How Superset and Grafana are built and what that means for operations
Superset and Grafana both serve dashboards, but they carry very different operating loads. Superset asks you to run a metadata database and, for some features, a cache and workers, while Grafana's configuration store and high availability pattern decide how many nodes and databases you keep alive. The cards below cover what each component means for patching, sizing and monitoring.
Superset's application and metadata database
Superset runs as a Flask and React application backed by a metadata database that stores dashboards, charts, users and permissions. Superset is tested with PostgreSQL and MySQL for that role, and the documented configuration covers PostgreSQL 10 to 17 and MySQL 5.7 and 8.x. The SQLite default used by Quickstart and PyPI installs is not recommended for production. Check that your chosen version is in the supported range before you size the database, and back it up before every upgrade.
Superset's optional cache and workers
Superset's caching layer and Celery worker and beat processes are optional in the architecture, but alerts and reports, async queries and dashboard thumbnails depend on them. Most installations use Redis and Celery for these features. If you plan to offer scheduled reports or long-running queries, you are operating Redis, workers and a scheduler alongside the web tier. Confirm which of these features your users need before you decide on a single-process deployment.
Superset upgrades and secret keys
Superset's upgrade guidance asks you to back up the metadata database, run the database upgrade and initialization commands, test in staging and read the breaking-change notes in UPDATING.md. Superset also requires a user-specified SECRET_KEY to start, and the configuration docs describe rotating it with PREVIOUS_SECRET_KEY. Treat the secret key as a managed credential with a rotation runbook, because losing or mishandling it affects every stored connection and session.
Grafana's configuration store
Grafana stores its configuration in SQLite 3 by default. For production and high availability, the installation docs require MySQL 8.0 or later or PostgreSQL 12 or later, because SQLite does not scale for production. Plan the database as a service you back up and monitor, not as a local file on the Grafana host. Confirm which database engine your platform team already operates before you choose the Grafana layout.
Grafana active-active high availability
Grafana's high availability pattern runs two or more active-active servers behind a load balancer, all sharing one MySQL or PostgreSQL database. Database-backed auth tokens let users reach any node. The docs note that alerting needs extra configuration to avoid duplicate notifications and that Grafana Live has limitations in this mode. Sizing and monitoring must cover every node, the load balancer and the shared database, and you should test alert delivery before relying on it.
Grafana patch cadence and sizing
Grafana publishes patch releases at least monthly, minor releases every other month and one major release a year. Each minor release is supported for 9 months, and the last minor release of a major version for 15 months. The installation docs size a small deployment at under 25 concurrent users on 2 cores and 2 to 4 GB of RAM. Ask your team to budget for frequent patch windows across every node, and to measure real load before assuming the small-deployment profile holds.
Capability comparison from supplied facts
This table sets Apache Superset and Grafana side by side on the capabilities that decide fit: license, data model, identity, provisioning and database requirements. Read each row as a question to answer for your own environment, since a row marked as Enterprise or Cloud only applies if you buy that edition. The main takeaway is that the two tools were built for different questions, so the rows that matter most are the ones tied to your data sources and your identity provider.
| Capability | Apache Superset | Grafana |
|---|---|---|
| License | Apache-2.0, with no paid edition | AGPL-3.0-only since version 8.0, with Grafana Enterprise sold as a separate commercial edition |
| Primary data model | SQL datasets, charts and dashboards built on SQL-speaking databases | Metrics, logs and traces queried from wherever they are stored, with dashboards and alerts on top |
| Supported data sources | Databases that speak SQL, connected through Superset's database drivers; confirm your specific driver | Data sources that Grafana queries directly, including metrics, logs and trace stores; confirm plugin support for your backend |
| Chart and SQL authoring | No-code chart builder plus a SQL IDE for analysts writing queries directly | Dashboard panels and an explore view for querying telemetry; SQL authoring depends on the data source plugin |
| Alerting and reporting | Alerts and reports depend on the caching layer and Celery worker and beat processes | Alerting is built in; confirm how your edition handles reporting, as reporting is listed among Enterprise features |
| Identity options | OAuth2 providers such as Google and Azure, custom OAuth2 servers using the code flow, and LDAP | LDAP, generic OAuth, Entra ID, Okta, GitHub, GitLab and Google OAuth on all editions; SAML only in Enterprise and Grafana Cloud |
| Role mapping | AUTH_ROLES_MAPPING maps LDAP or OAuth groups to Superset roles; confirm the exact mapping keys for your version | Team sync is available only in Grafana Enterprise and Grafana Cloud, so group-to-team mapping needs that edition |
| Provisioning as code | Configuration through Superset's config file and deployment tooling; confirm which objects can be managed as code in your version | Data sources, dashboards, plugins and alerting resources can be provisioned from YAML or JSON at startup, with Terraform and Git Sync documented |
| Production database requirements | PostgreSQL 10 to 17 or MySQL 5.7 and 8.x as the metadata database; SQLite is not recommended for production | MySQL 8.0 or later or PostgreSQL 12 or later for production and high availability; SQLite does not scale for production |
Apache-2.0 versus AGPL and Grafana Enterprise
Apache Superset is an Apache Software Foundation project released under the Apache License 2.0, and the project has no paid edition. Every feature the project documents is in the same code you download, so there is no tier to upgrade to for SSO, embedding or auditing. The license is permissive: you can run, modify and redistribute the software, including inside a product you sell, provided you keep the license and notices. The practical question for a regulated team is less about the code and more about what your own products and customers receive.
- No paid edition: Licensing cost is zero, so the recurring spend is infrastructure, staff time and any outside support you contract.
- Embedding in products: Apache-2.0 places few restrictions on embedding dashboards in customer-facing software, but how you handle guest access and row level security is an engineering decision you own.
- Redistribution: You may ship modified builds internally or to customers, subject to attribution and notice requirements.
- Patent terms: Apache-2.0 includes an express patent grant from contributors, which many legal teams review as a positive point.
- Vendor independence: Because no commercial edition gates features, the same version can be moved between your own hosts without a license change.
Confirm the exact terms, attribution duties and any obligations for your embedded or redistributed products with qualified counsel before you commit.
Grafana is licensed under AGPL-3.0-only from version 8.0 onward. Grafana Labs relicensed Grafana, along with Loki and Tempo, from Apache License 2.0 to AGPLv3 on April 20, 2021, and plugins, SDKs and agent software kept other licenses. The AGPL is a copyleft license with a network clause, so the obligations that matter most are the ones triggered when you modify Grafana and let users interact with it over a network. Earlier Apache-2.0 releases are a separate question that your legal team should review if you hold them.
- Modified and networked use: If you change Grafana and make it available to users over a network, the AGPL can require you to offer the corresponding source of those changes to those users.
- Internal use: Running unmodified Grafana for your own staff generally carries lighter source-offer duties than running a modified build for external users, but confirm how your use case is classified.
- Plugins and agents: Plugins, SDKs and agent software are under other licenses, so each component needs its own review.
- Embedding: Embedding Grafana panels in a product you distribute raises the same network and combined-work questions, so treat it as a legal review item.
- Enterprise features: The AGPL covers the open-source code only. SAML and the other Enterprise features are a separate commercial product, not a license exception.
Ask counsel to map each Grafana component you deploy to its license and to confirm how your modifications and user access fall under AGPL-3.0-only.
Grafana Enterprise is a separate commercial edition, offered as a non-AGPL option alongside the open-source project. Its documented features include SAML authentication, team sync, active sync and SCIM provisioning, which the Grafana authentication docs say are available only in Grafana Enterprise and Grafana Cloud. Reporting and auditing are also listed as Enterprise features. Grafana Cloud has separate pricing, so the cost of a hosted service and the cost of a self-managed Enterprise license should be quoted separately.
- Identity features: SAML, team sync, active sync and SCIM provisioning are gated to Enterprise and Grafana Cloud, while LDAP, generic OAuth and other providers work in all editions.
- Reporting and auditing: Both are listed as Enterprise capabilities, so teams that need them for evidence should confirm the exact scope in the current edition documentation.
- Grafana Cloud free tier: The pricing page lists a free tier.
- Grafana Cloud Pro: The Pro tier starts with a 19 US dollar monthly platform fee plus usage, according to the Grafana pricing page.
- Grafana Cloud Enterprise: The Enterprise tier has a minimum commitment of 25,000 US dollars per year, according to the same pricing page.
Confirm current Enterprise licensing terms, the price of a self-managed Enterprise license, and what each tier includes directly with Grafana Labs, because published prices may change.
How identity integration differs between the two
Superset and Grafana both support federated sign-in, but they draw the line between editions in different places. Superset is built on Flask-AppBuilder and documents OAuth2 providers such as Google and Azure, custom OAuth2 servers using the code flow, and LDAP. Group membership from LDAP or OAuth can be mapped to Superset roles with AUTH_ROLES_MAPPING in the configuration file, so access follows your directory groups without manual role assignment for each user. Because Superset has no paid edition, these options are available in the same open-source deployment you run. Confirm the exact provider settings, callback URLs and mapping syntax against the Superset configuration documentation for the version you deploy, since the mapping keys are set in superset_config.py.
In Grafana, LDAP, generic OAuth, Entra ID, Okta, GitHub, GitLab, Google OAuth, auth proxy and JWT are available in every edition. SAML, team sync, active sync and SCIM provisioning are documented as Grafana Enterprise and Grafana Cloud features, so an organization that needs SAML sign-in or automated user and team lifecycle through SCIM must budget for Enterprise before it builds the integration. Grafana's documentation also notes that its team sync and role handling depend on the identity provider's group claims, so test how each group maps to an organization role before you roll out. Ask Grafana Labs to confirm which features your chosen edition includes, and read the Grafana authentication documentation for the current list.
Before choosing, write down your identity provider, whether you need SAML, whether users must be created and removed automatically, and which group structure should drive dashboard access in each tool. A Superset deployment that relies on group mapping and a Grafana deployment that relies on SAML or SCIM will have different licensing and testing effort, so map both against your requirements and have your identity team review the results. Confirm the exact configuration with each vendor's documentation, and test sign-in with a leaver account to verify that access is removed as expected.
What drives the running cost and implementation effort
Licence fees are only part of what each platform costs. The cards below cover the services you run, the upgrade rhythm you commit to, the skills your team needs, and the implementation work that DeployOpen would scope in writing before any quote is issued.
Services you operate
Superset needs a metadata database, and most installations add Redis and Celery workers for async queries, alerts and reports, and dashboard thumbnails. Grafana needs its own database for configuration, and high availability means two or more active-active servers behind a load balancer sharing MySQL or PostgreSQL. Grafana's documented small-deployment reference is 2 cores and 2 to 4 GB of RAM for under 25 concurrent users. Ask for the node count, database sizing and monitoring plan behind each proposed architecture.
Upgrade cadence and support windows
Grafana ships major releases yearly, minor releases about every other month and patch releases at least monthly. Each minor release is supported for 9 months and the last minor of a major for 15 months, so plan for a recurring upgrade rhythm. Superset upgrades follow a fixed sequence: back up the metadata database, run superset db upgrade and superset init, test in staging, and review UPDATING.md for breaking changes. Budget staff time for both, and confirm how your scope covers each cycle.
Edition and licence fees
Superset is Apache-2.0 with no paid edition, so its licence cost is zero. Grafana OSS is AGPL-3.0, while SAML, team sync, active sync and SCIM provisioning sit only in Grafana Enterprise, a commercial edition with a separate price. Grafana Cloud has a free tier and a Pro tier with a monthly platform fee plus usage. Ask any vendor for the exact edition that includes your identity provider, audit and reporting needs, and whether that price recurs per user, per node or per year.
Skills and on-call load
Superset operators need to be comfortable with Flask-based applications, SQL database administration and Celery queue behaviour, because a stuck worker can stop scheduled reports silently. Grafana operators need Prometheus-style data source tuning, alert routing and HA behaviour, including the extra configuration needed to avoid duplicate alert notifications. Decide which team owns each service, who gets paged, and whether your existing staff already run these components.
Data source and identity setup
Connecting Superset to SQL databases means managing credentials, permissions and the SECRET_KEY it requires at startup, plus OAuth2 or LDAP group mapping through AUTH_ROLES_MAPPING. Grafana needs data source connections for each metrics, log or trace backend, and SAML or SCIM if your identity provider requires them. Ask for a written list of data sources, identity connectors and role mappings included in the fixed scope, along with who signs off on access rules.
Dashboard build and as-code work
Building dashboards is effort in both tools. Superset dashboards are built in the chart builder and SQL Lab, so the work is query design, semantic layer definitions and review. Grafana dashboards can be defined through provisioning files loaded at startup, and Grafana documents further as-code options including Terraform and Git Sync. Ask whether the quote includes dashboard migration, version control of dashboards and handover documentation, and how many dashboards are in scope.
When an organization runs both
The common pattern is two tools with two jobs: Grafana watches whether systems are healthy and fires alerts on metrics, logs and traces, while Superset gives analysts and business owners SQL-based reporting on operational or business data. The decision that matters is not which tool is better but which questions each group must answer every day. If the question is whether a service is degraded right now, Grafana owns it. If the question is how many transactions settled last quarter by region, Superset owns it. Write that split down before installing anything, because it determines which team gets paged, which team builds dashboards and which data each one can read.
The boundary decisions are concrete. Assign one team as the owner of each instance, including its upgrades, backups and data source configuration. Decide whether both tools read from the same database or from separate replicas, since pointing an analytical dashboard at a production primary can compete with the workload it is meant to observe. Decide whether both use the same identity provider. Grafana's SAML, team sync and SCIM provisioning sit in Grafana Enterprise, while Superset maps OAuth or LDAP groups to roles through AUTH_ROLES_MAPPING, so confirm which features your edition and your identity provider actually support before you promise a single sign-on experience. Keep one source of truth for each data source definition, for example Grafana provisioning files in Git and Superset database connections managed by one administrator, so credentials and permissions are not copied by hand into two places.
Running both doubles the upgrade and access review work. Each product has its own release cadence, its own metadata or configuration database and its own role model, so every quarterly access review and every upgrade cycle covers two systems. Ask each vendor or your own platform team to confirm its supported upgrade path and staging process before committing to both. Ask the same question of your auditors: which evidence will they request for each tool. If your organization cannot staff two reviews, run only the tool that answers the more urgent question and revisit the second when the need is clear.
Moving dashboards and users from one tool to the other
This sequence produces a dated inventory, a list of what is rebuilt versus exported, and a tested cutover from one tool to the other, whichever direction the move runs. Each step ends with a check you can review before the next one starts.
Inventory dashboards, sources, alerts and users
The BI or platform owner lists every dashboard and chart in use, the data sources behind each one, every active alert or scheduled report, and the users and groups who log in. Mark which items are still used, who owns each one, and which ones feed a downstream process such as a ticket or a paging rule. Done means the inventory is signed off by the dashboard owners, with unused assets marked for retirement so they are not carried into the new tool.
Classify each asset as migrate or rebuild
The platform team and the dashboard owners sort each item into one of two groups. Superset dashboards are built on SQL datasets and charts, while Grafana panels are built on time series queries against a data source, so a SQL chart rarely maps one to one onto a metrics panel and a metrics panel rarely maps onto a SQL dataset. Items that depend on the other tool's query model are rebuilt. Done means every asset has a label and a named owner for the rebuild work.
Map identities and access to the target
The identity owner maps each group in the current tool to a role or team in the target. For Superset, check how LDAP or OAuth groups map to roles through the AUTH_ROLES_MAPPING setting. For Grafana, confirm which identity options your edition supports, because SAML, team sync and SCIM provisioning are limited to Grafana Enterprise and Grafana Cloud according to the authentication documentation. Done means a written access matrix exists and a test user from each group signs in and sees only the expected content.
Stand up the target's production database and high availability
The platform team builds the target on the production-grade metadata database its documentation names. Superset is tested with PostgreSQL and MySQL as the metadata database, and the SQLite default is not recommended for production. Grafana requires MySQL or PostgreSQL for production and high availability, and its high availability model uses two or more active-active servers behind a load balancer. Back up the database and rehearse a restore. Done means the target runs on its production database, a restore has been tested, and the owner has confirmed the supported database versions in the current release notes.
Rebuild and provision the target content
The team recreates the rebuilt items in the target, and where the target supports as-code definitions, loads them from version-controlled files. Grafana provisioning loads data sources, dashboards and alerting resources from YAML or JSON at startup, so provisioned definitions can move through a repository. Superset has no equivalent that this comparison can confirm, so treat its dashboard export and import as something to verify in the release you run. Done means each rebuilt item has a review by the dashboard owner who signed off the original.
Run both tools in parallel and compare results
Both tools stay live while users check the same questions in each. The team compares the numbers from the old and new versions of each dashboard, confirms that alerts fire on the same conditions, and checks that scheduled reports arrive. Alerts need care in both tools, because duplicate notifications are a known risk in Grafana high availability setups without extra configuration. Done means the dashboard owners have signed off on each comparison and every alert has been tested in the target.
Cut over users and retire the old instance
The team switches users and any links, embeds or automated consumers to the target on an agreed date, and keeps the old instance read-only for a defined fallback period set by the change owner. Confirm the upgrade and backup routine on the new instance before cutover, as the Superset upgrade guide requires a metadata database backup before each upgrade. Done means no traffic reaches the old tool, a final backup of its configuration is archived, and the old instance is decommissioned.
Questions buyers ask before choosing or hiring help
How much effort does it take to run Apache Superset in-house?
Running Superset in-house means operating more than the web application. The Superset architecture documentation lists four components: the Flask and React application, a metadata database, an optional caching layer and optional Celery worker and beat processes. Alerts and reports, async queries and dashboard thumbnails depend on the caching layer and workers, and most installations use Redis and Celery for them. The metadata database should be PostgreSQL or MySQL, not the SQLite default. Effort rises with the number of these services, the upgrade routine (back up the metadata database, run superset db upgrade and superset init, test in staging, and read UPDATING.md), and the Kubernetes deployment method. Confirm which deployment path your platform team will support before you size the work.
How much effort does it take to run Grafana in-house?
Grafana is lighter to start but grows with its high availability and access requirements. The installation docs size small deployments under 25 concurrent users at 2 cores and 2 to 4 GB of RAM, but say SQLite does not scale for production, so MySQL 8.0 or later or PostgreSQL 12 or later is needed for production and high availability. High availability means two or more active-active servers behind a load balancer sharing that database, and alerting needs extra configuration to avoid duplicate notifications. Upgrades are steady: major releases come once a year, minor releases every other month, and patch releases at least monthly. Effort is driven by how many teams need alerting, how many data sources you connect, and how carefully you manage provisioning as code.
Can our team self-host Superset or Grafana without outside help?
A team with Linux, database and container skills can usually self-host either tool, but the question is whether it can keep both running well. Superset needs a metadata database, cache and workers, and a documented upgrade path, so the skills are database administration and job-queue operations. Grafana needs a production database, a load balancer for high availability and careful alerting configuration. Ask yourself who will own on-call for dashboards and alerts, who reviews upgrades within the support windows, and who maintains provisioning files. If those owners are not named, an outside implementation partner can plan, build and document the setup. DeployOpen implements self-hosted open-source platforms inside your own infrastructure, under a fixed-scope written agreement.
How long does a typical implementation take, and what makes it run long?
The length depends on scope, not on the product name alone. A single-team Superset or Grafana deployment with few data sources is simpler than a multi-tenant rollout with many identity providers and databases. The main drivers are the number of data sources and their access models, the identity integration (especially if SAML or SCIM is required), the number of dashboards or alert rules to migrate, the depth of hardening, and how quickly your staging environment and sign-off owners are available. Because timelines are not supplied as fixed figures here, ask for a duration only as part of a written scope, and ask the partner which of these drivers it has assumed.
What does the implementation need from our side?
Plan for four client inputs before work starts. The first is data access: read credentials or network paths to the SQL databases Superset will query, or to the metrics, logs and traces Grafana will read. The second is identity owners who can approve group-to-role mappings, such as AUTH_ROLES_MAPPING in Superset or team and role sync in Grafana. The third is a staging environment for upgrades and testing before production. The fourth is named sign-off for dashboards, alert routes, backups and the hardening checklist. Ask any partner to list these inputs in the written scope, with an owner and a date for each.
Do we need Grafana Enterprise for SSO and SCIM?
Only if you need SAML, team sync, active sync or SCIM provisioning. According to the Grafana authentication docs, all editions support LDAP, generic OAuth, Entra ID, Okta, GitHub, GitLab and Google OAuth, auth proxy and JWT. SAML, team sync, active sync and SCIM are available only in Grafana Enterprise and Grafana Cloud. Superset, by contrast, is built on Flask-AppBuilder and supports OAuth2 providers, custom OAuth2 servers and LDAP, with group-to-role mapping through AUTH_ROLES_MAPPING. Before you decide, check which protocol your identity provider requires, and ask the Grafana vendor for current Enterprise pricing, since no price is listed here.
Does the AGPL license affect internal use of Grafana?
Running Grafana inside your organization for internal monitoring does not by itself create a source-disclosure obligation for your own dashboards, but the license terms should be read by your legal team. Grafana has been AGPL-3.0-only since version 8.0, relicensed from Apache-2.0 on April 20, 2021, while Superset is Apache-2.0 with no paid edition. The difference matters most if you modify Grafana and offer it to others, embed it in a product you distribute, or ship plugins under other licenses. Confirm your obligations with counsel for your specific use, and do not treat this answer as legal advice.
Should we choose a fixed-scope engagement or a retainer?
Choose a fixed-scope engagement when the deliverables are known: an initial deployment, a set of migrated dashboards, or a hardening pass with written acceptance criteria. Most DeployOpen engagements are fixed-scope, with scope, deliverables and timelines agreed in writing before work starts. Choose a retainer when you need ongoing support, upgrades or continuous testing across the component lifecycle, such as the Superset upgrade routine or Grafana patch cadence. A common path is a fixed-scope build followed by a retainer for operations. Ask any provider for the written scope, what is one-time and what recurs, and how upgrades within each product's support window are handled.
How DeployOpen helps choose, implement or migrate
DeployOpen is a Chainscore Labs company that plans, implements and supports self-hosted open-source platforms inside your own infrastructure. The work below follows the decision, from reviewing your data sources and identity needs to delivering the chosen tool or both under a written scope.
Reviewing data sources first
Superset reads SQL databases through its connectors, while Grafana is built around metrics, logs and traces in its own data source model. We start by inventorying where each question's data lives, how fresh it must be and who asks the questions. Teams with mostly SQL warehouses and business reporting usually have a different answer from teams whose main need is service telemetry. Mixed estates often need both, and we map which data goes to which tool before anyone builds a dashboard.
Checking identity and access requirements
Superset supports OAuth2 providers, LDAP and group-to-role mapping through its configuration. Grafana's authentication options differ by edition, and SAML, team sync and SCIM provisioning are limited to Grafana Enterprise and Grafana Cloud. We confirm which sign-in methods, group sync and provisioning your identity provider needs, and whether those features are available in the edition you plan to run. This check often decides the license and edition question before any technical design begins.
Sizing the operating model
Both platforms need a production-grade metadata database, and the choice of services changes the workload. Superset's async queries, alerts, reports and thumbnails depend on a caching layer and Celery workers, while Grafana high availability needs two or more active-active servers sharing a database. We document who will run upgrades, backups and monitoring, and whether your team can own those duties or needs a retainer. The result is a written operating model you can review before committing.
Implementing the chosen tool or both
Once the direction is agreed, we install, configure and integrate the selected platform in your cloud account, data center or air-gapped network. For Superset, that means the metadata database, role mapping and deployment method. For Grafana, it means provisioning data sources, dashboards and alerting as code, and setting up hardening. Where an organization runs both, we define how their boundaries, credentials and access paths stay separate. Most engagements are fixed-scope, so deliverables and timelines are agreed in writing before work starts.
Hardening, backup and upgrade planning
We apply the hardening guidance published for each platform, cover backups of the metadata database, and document a staged upgrade path. Superset upgrades call for a database backup, staging tests and a review of breaking changes before each release. Grafana publishes its own upgrade cadence, which we translate into a calendar your team can follow. Retainers are available for upgrades, ongoing support and continuous testing once the platform is live.
Limits of our role
DeployOpen is not an official partner, reseller or certified provider of Apache Superset or Grafana Labs unless a supplied fact says otherwise, and we do not favor either platform. We prepare systems, controls and documentation for compliance assessments, but independent auditors and certification bodies issue any audit reports or certifications. Ask any vendor, including us, to confirm licensing, edition features and support terms in writing before you rely on them.
