Every data team that grows past five people hits the same wall: someone asks "where does this metric come from?" and nobody can answer without a 20-minute Slack thread. That's when the catalog conversation starts. And if you've already ruled out Collibra (too heavy, six-month implementation) and Alation (enterprise pricing, governance-first mindset), you're probably staring at Atlan and Secoda.
Both position themselves as the modern catalog. Both claim AI features. Both say they'll have you up and running in days, not months. But they're different tools built on different philosophies, and picking the wrong one costs you a year of migrations plus whatever you spent on the contract.
I've spent time in both products. Here's what I actually found.
What Atlan is building
Atlan calls itself an "active metadata platform." The distinction matters. They're not just indexing your warehouse schema and slapping a search bar on top — they're trying to be the collaboration layer that sits across your entire data stack.
The core concept is that metadata should flow bidirectionally. When someone adds a description to a column in Atlan, that context propagates. When a dbt model changes upstream, Atlan picks up the lineage change and can notify downstream owners. The "active" part means metadata isn't static documentation that rots — it's a living graph that reacts to changes in your stack.
Column-level lineage is where Atlan genuinely shines. It parses SQL across your warehouse, dbt models, BI tools, and ETL jobs to show you exactly which upstream columns feed into which downstream reports. This isn't just table-to-table arrows on a diagram. It's "this dashboard tile pulls from this specific column, which is derived from these three source columns via this transformation." When something breaks, you can trace the blast radius in seconds.
The integration marketplace is large — 50+ connectors last I checked. Snowflake, BigQuery, Redshift, dbt, Looker, Tableau, Airflow, Fivetran, the list goes on. Each connector isn't just "we can read your schema." Many of them pull execution metadata, query logs, and lineage information.
Collaboration features feel Notion-like. You can comment on assets, tag teammates, create announcements tied to specific tables or dashboards. There's a Slack integration that lets you ask questions about data assets directly from channels, though it's more of a "search Atlan from Slack" than a true conversational interface.
Atlan raised north of $450M (Series C) and has been around since 2019. They have customers running catalogs with thousands of assets and hundreds of users. The product is proven at scale.
Pricing: Expect $30k/year minimum. Enterprise contracts easily run $60-100k+ depending on connectors and seats. No self-serve pricing page — you're talking to sales.
What Secoda is building
Secoda took a different bet: AI-first from day one. While Atlan added AI features incrementally on top of their metadata graph, Secoda was built around the idea that documentation should write itself and search should be conversational.
The standout feature is AI-generated documentation. Connect your warehouse, and Secoda will analyze column names, data types, query patterns, and existing comments to auto-generate descriptions for every table and column. It's not perfect — you'll want to review and edit — but it gets you from zero documentation to 70% coverage in an afternoon. For a team that has literally nothing documented, this is a massive head start.
Natural language search is the other headline feature. You type "what tables contain customer email?" and Secoda returns relevant assets ranked by relevance, not just a keyword match on column names. It understands synonyms, relationships, and context. The quality of answers depends heavily on how much metadata you've fed it, but when it works, it's genuinely faster than browsing a catalog tree.
The UX is deliberately simpler than Atlan's. Fewer features, fewer configuration options, faster time-to-value. Secoda's bet is that most data teams don't need a full metadata platform — they need a place where people can find and understand data without filing a ticket.
Secoda came out of Y Combinator and raised a Series A. They're smaller, scrappier, and iterating fast. The downside of that scrappiness: fewer integrations, less mature lineage, and occasionally rough edges in the product.
Pricing: Starts around $10-15k/year. Still not cheap, but significantly more accessible than Atlan for a 10-person data team.
Head-to-head comparison
| Category | Atlan | Secoda |
|---|---|---|
| Lineage depth | Column-level, cross-platform, SQL parsing | Table-level primarily, column-level improving |
| AI documentation | Added later, functional but not the core UX | Built-in from day one, better out-of-box generation |
| Natural language search | Keyword + filters, AI-assisted | True NL queries, conversational interface |
| Integrations | 50+ connectors, deep metadata extraction | 30+ connectors, growing but less depth per connector |
| Collaboration | Rich — comments, announcements, Slack, Teams | Basic — comments and sharing, less embedded workflow |
| Setup time | Days to weeks (depending on connectors) | Hours to days |
| UX complexity | More features, steeper learning curve | Simpler, faster adoption |
| Pricing floor | ~$30k/year | ~$10-15k/year |
| Best for | Teams 20+ scaling toward enterprise | Teams 5-20 wanting fast wins |
| Governance features | Policies, access requests, PII tagging | Lighter — tags and ownership, less policy enforcement |
| Track record | Proven at scale, large customer base | Newer, smaller deployments |
Where Atlan wins
If your team is growing past 20 people, Atlan is the safer bet. The deeper lineage means fewer "I don't know what upstream change broke this" incidents. The richer collaboration features mean your data team can actually use the catalog as their operating system rather than a reference they check occasionally.
If you need governance, even lightweight governance — PII tagging, access request workflows, data quality annotations — Atlan has more here. Secoda can tag things, but Atlan has actual policy enforcement and approval workflows.
If you're multi-tool and complex — say you run dbt + Airflow + Snowflake + Looker + Fivetran — Atlan's integration depth matters. It's not just that they connect to more tools; it's that they extract richer metadata from each connection. The lineage graph is only as good as the metadata flowing into it.
Where Secoda wins
If your biggest problem is "nothing is documented", Secoda gets you to useful faster. The AI documentation generation is genuinely good for a first pass. You'll still need humans to validate and add business context, but starting from 70% coverage beats starting from zero.
If your team is small and cost-sensitive, the $15-20k difference in annual spend isn't trivial. That's a contractor for two months. For a team of eight, Secoda gives you catalog functionality without the enterprise price tag.
If adoption is your worry, Secoda's simpler UX means analysts and engineers actually use it. I've seen too many Atlan deployments where the data engineering team maintains the catalog but analysts never log in because the interface is overwhelming. Secoda's "just search for it" model has lower friction.
If you want AI to do the heavy lifting, Secoda's AI features feel more native. They're not bolted on — they're the primary interaction model. The natural language search is better, the auto-documentation is better, and the overall experience assumes AI is the default rather than an add-on.
The honest take on both
Here's what neither vendor will tell you: at the $10-30k/year price point, you should seriously question whether you need a dedicated catalog at all.
If your team is under 15 people and you're all in the same Slack workspace, a well-maintained dbt docs site plus a Notion page with your key metrics might be 80% of what a catalog gives you. The search problem is real, but it's not a $30k problem for every team.
The schema discovery features in modern BI tools have gotten surprisingly good. Looker's data dictionary, Metabase's model layer, even Snowflake's built-in data catalog — these aren't full replacements, but they cover the "what columns does this table have and what do they mean?" use case for free.
Where dedicated catalogs genuinely earn their keep: cross-tool lineage (showing how data flows from source through transformation to dashboard) and organizational knowledge management (tribal knowledge that lives in people's heads, not in any single tool). If those are your pain points, a catalog makes sense. If your pain point is just "analysts can't find the right table," you might be over-buying.
My recommendation
Choose Atlan if: You have 20+ people touching data, you need column-level lineage across multiple tools, you anticipate needing governance features within a year, and budget isn't the primary constraint. Atlan is the catalog you'll grow into.
Choose Secoda if: You have a smaller team, documentation is your biggest gap, you want fast time-to-value, and you'd rather spend $15k than $40k while you figure out if a catalog actually changes behavior on your team.
Choose neither if: Your team is under 10 people and your stack is simple (one warehouse, one BI tool, one orchestrator). Invest that money in writing better dbt documentation and setting up a schema browsing workflow in the tools you already have.
For teams that already have a query layer or analytics tool with schema browsing — something like Fastero, which gives you schema discovery alongside your actual analysis workflows — adding a separate catalog on top can feel redundant unless you specifically need the cross-platform lineage graph.
The catalog market is still consolidating. Both Atlan and Secoda will look different in 18 months. Pick based on your current pain point, not a feature comparison spreadsheet. If documentation is the pain, start with Secoda. If lineage and scale are the pain, start with Atlan. If neither pain is acute enough to justify five figures annually, save your money.
Related reading:
- Best Data Catalog Tools
- Atlan vs Alation: Data Catalogs Compared
- Collibra vs Atlan: Enterprise Data Governance Compared
- How to Choose a Data Catalog Without Overengineering
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