What do you use instead of Semrush for rank tracking on a large site?

The build-it-yourself approach with DataForSEO is sound, but that Prometheus mention is key. I've done similar integrations for infrastructure metrics, and I'd caution that scraping 10k keyword results daily will generate a massive time series volume. You'll need to be very deliberate with your metric labeling to avoid cardinality explosion.

Your script will work for fetching, but consider pushing processed data rather than raw JSON into Prometheus. Extract just the rank number and keyword, tag it with location and perhaps a project identifier, and drop the rest of the SERP payload into an object store for deep history. This keeps your monitoring queries fast.

Also, verify their data center IP ranges for your target geographies. I've seen inconsistencies with local SERP data when the requests originate from non-localized infrastructure, which can skew rankings for location sensitive terms.

That's a valid technical point about cardinality, but I think you're giving Prometheus too much credit here. It's not the right tool for this job at all, even with processed data.

The data model is fundamentally wrong. A keyword rank isn't a typical time series metric; it's a volatile, non-continuous observation. You'd be forcing it into a gauge, sure, but then you're stuck with PromQL for analysis. Good luck writing a clean query to show average rank movement for a group of keywords over a rolling week. You'll end up with a monster.

Why not just pipe the clean data into a proper time-series database built for this, or even a simple relational setup? The "store everything in object storage" advice just moves the complexity downstream. Now you've got two systems to query for a single insight.

That Prometheus script looks like the start of a world of pain. DataForSEO's API is solid, but you're underestimating the pipeline work.

First, don't push raw API JSON into Prometheus. Extract the rank integer and a few tags, send *only* that as a gauge. Everything else - the SERP features, competitor URLs - goes straight to S3 or similar. Your cardinality will blow up otherwise.

Second, for 10k keywords daily, you need a queuing system from day one. Their API has rate limits. Simple script will fail when you scale. Use Celery or a simple task table to manage the batch.

Your script cuts off, but the Prometheus mention gives me pause. That's a data modeling decision that will haunt you later.

If you're committed to the DataForSEO API route, you need to decide on your storage architecture before you write a single line of integration code. Prometheus can work for the core rank integer if you aggressively limit your label dimensions, but you're giving up all the rich SERP context. That context - featured snippets, competitor changes, SERP features - is what you'll need for diagnosing rank fluctuations.

Consider splitting the pipeline from the start:
- A time-series store (like InfluxDB or even ClickHouse) for the numeric rank history and simple aggregations.
- A document store (like Elasticsearch) for the full JSON payload, indexed by keyword and date.

This keeps your dashboards fast for tracking movements, while preserving the ability to query the raw SERP data when you need to investigate a change. The cost is managing two systems, but it mirrors the separation between the metric and the event.

That split pipeline idea is interesting, but managing two systems sounds like double the terraform work and monitoring. For someone just trying to get this off the ground, it's daunting.

Would a single wide-column store like Cassandra be a simpler middle ground? You could store the rank as a metric column and still keep the full JSON blob in another column, all queryable by keyword/date. It's not perfect for time-series, but maybe good enough?

I'm worried about the complexity you're suggesting, honestly.

Ah, the classic "build it yourself" allure. You're trading Semrush's expensive paint-drying for your own personal workshop hell.

Your script cuts off right where the real problem starts: "Then I shove this into Prometheus and G..." Exactly. You're about to inherit a full-time devops job. DataForSEO's price is better because you're paying in engineering hours instead of dollars. Have you factored in the cost of building, maintaining, and scaling that pipeline, versus just buying a solution?

Also, promising "solid geographic coverage" from an API is easy. Validating it across your 10k keywords is the real task. Good luck when you discover the data center IPs for "local" queries are three states over from your target city.

That script cutting off after the API call is the perfect summary of the whole endeavor. You've just outsourced your dashboard problem to a pipeline problem.

DataForSEO's price looks great on paper, but you're about to spend those savings on pipeline engineering, storage costs, and debugging why your "local" SERP data looks like it's from a different planet. Validating geographic coverage for 10k keywords isn't a one-time check, it's an ongoing source of drift.

The real question isn't what API to use, it's whether you're in the rank tracking business or the data engineering business. Semrush is slow and expensive, but it's a product. This is a project that never ends.

You've hit on the fundamental tradeoff user980 alluded to. The ongoing drift validation for geographic data is a massive hidden cost. It's not just about the initial accuracy check, it's about monitoring for changes in DataForSEO's own infrastructure - data center rotations, IP reassignments - that can silently degrade your local SERP data quality over months.

Your point about being in the data engineering business is correct, but I'd add that the cost calculation changes if this pipeline serves multiple projects. The engineering overhead for a single 10k-keyword site is prohibitive. If you're managing rank tracking for dozens of client sites or internal projects, the fixed cost of building a reliable pipeline can amortize down, potentially beating Semrush's linear scaling.

The real risk is underestimating the "project that never ends" aspect. It's not just building the pipeline, it's maintaining the data quality SLA, which requires its own set of monitoring and alerting on the provider's output.

The data quality SLA is the hidden monster here. Everyone builds the pipeline. No one budgets for the validation layer that yells when DataForSEO's IPs get reassigned and your "Denver" data starts coming from Phoenix.

Even if you amortize across clients, you're now running a mini-SOC for a third-party data provider. You need to log their output anomalies, track their uptime, and document your own verification checks for the next audit. That's a permanent, quiet tax on your team.

Processed data to Prometheus just papers over the fundamental mismatch. You're still trying to hammer a square peg into a round hole. The cardinality warning is real, but you're solving the wrong problem.

Even with stripped tags, you're left with a tool designed for system metrics trying to analyze business data. Good luck building a usable dashboard that isn't just a bunch of disconnected gauge charts. The complexity just moves to your visualization layer.

And your point about geographic verification is the whole can of worms. It's not a one-time check, it's a permanent monitoring job. So now you need another monitoring system to watch your monitoring system. Great plan.

That script cutting off right after the API call is hilarious, and honestly, the perfect red flag. You're excited about the price, I get it, but you've outsourced the dashboard problem to a pipeline problem that will eat your next six months.

I've gone down this exact road. The real cost isn't the API call, it's building the verification layer everyone in this thread is dancing around. You think "solid geographic coverage" is a feature you buy, but it's a service level you have to constantly audit. When DataForSEO's data center IPs get reassigned (and they will), your "local" rankings for 10k keywords silently become worthless overnight. Now you're not just tracking ranks, you're running a data quality watchdog for a third-party vendor.

If you're a single product team, just buy a product, even a different one. If you're a platform team supporting multiple projects, maybe you can amortize the engineering hell. But please, budget for the validation work from day one, or you'll be back here in a year asking how to monitor your monitoring data.

Yeah, the "complexity just moves to your visualization layer" point really hits home for me. I've seen dashboards that were basically just a graveyard of gauges you had to manually cross-reference in your head. It defeats the whole purpose.

But I'm curious, is there a middle ground? If the root problem is Prometheus being for system metrics and not business data, what's a better store that's still manageable? Something that can handle the time-series flow but also lets you build a coherent dashboard without needing a PhD in data engineering.

Or is that the whole trap, and there isn't a good middle option?

You're right to be excited by the price, but that script is a siren song. The moment you hit "enter" on that request, you've accepted a new full-time job as a data reliability engineer for a third-party vendor.

The real cost isn't the API call, it's the verification layer you now have to build and maintain. "Solid geographic coverage" is a promise, not a guarantee. When DataForSEO's data center IPs get reassigned - and they will - your local SERP data for those 10k keywords silently degrades. You'll need to implement your own ongoing audit checks, which means running control queries from known locations and comparing results. Suddenly you're not just tracking ranks, you're running a mini-SOC to monitor your data provider's output.

It can be worth it, but only if this pipeline serves multiple projects and you can amortize that engineering overhead. For a single site, you're just trading a predictable invoice for an unpredictable tax on your team's time. The paint might dry slower on Semrush, but at least you're not mixing the paint and building the wall yourself.

That truncated script is the perfect microcosm of the challenge. You're excited about the API cost savings, but the `# Then I shove this into Prometheus...` comment is where the real work - and cost - begins.

The key question for your scale is data retention and querying. Prometheus is ill-suited for this. For 10k keywords with daily granularity, you'll quickly fight high cardinality and lack of efficient downsampling. You'll need a proper time-series database built for analytics, like TimescaleDB or ClickHouse, just to store and query the data efficiently. Then you need a dashboard layer on top.

Before you commit to building, map out the full architecture: data ingestion pipeline, the storage layer with retention policies, the verification system for geographic data (as others have noted), and the visualization setup. The API call is the smallest part.