Alright, let's talk about the thing everyone loves to ignore until their cloud bill arrives or their compliance officer starts hyperventilating: data retention. We're comparing Langfuse and Arize here, specifically on their policies. Because, let's be honest, most of us just click "I Agree" on the default settings and pray. Spoiler: both platforms will happily let you hoard petabytes of tracing data if you're willing to pay for it, but the devil is in the *how* and the *what happens next*.
First, Langfuse. Their model is almost charmingly European in its manual-ness. You're the master of your own (data) destiny, provided you remember to set up the expiry policies. It's all configurable per project, which is great if you have a clue what you're doing, and terrifying if you don't. You can set TTLs on traces, scores, even observations. The upside? Granular control. The downside? It's another piece of YAML or dashboard configuration you can mess up, and default retention is... indefinite? A bold choice. You're essentially building your own retention policy, which feels very much in line with their open-core, self-hostable ethos. You get the power, and you also get the responsibility to not bankrupt yourself.
Then there's Arize. It feels more... managed? American? They push you harder towards their defaults, which are ostensibly sensible for model monitoring—shorter retention for raw data, longer for aggregates. Their pricing page whispers sweet nothings about "cost-optimized storage tiers," which is cloud-speak for "we'll archive it to a cheaper bucket after X days, and good luck querying it quickly." The control feels more abstracted, which is either a relief or a frustration depending on whether you enjoy configuring data lifecycle policies as a weekend hobby. The emphasis is on the metrics and aggregates, not on keeping every single trace forever, which is probably the *correct* architectural stance for ML observability, even if it pains the data hoarders among us.
So, what's the real comparison? It's philosophy. Langfuse gives you the knife and expects you to carve the turkey. Arize pre-slices it for you, but you might not get the drumstick. If you're in a heavily regulated industry or have bizarrely specific data sovereignty needs, Langfuse's granular, self-hosted option might be your only viable path. If you just want to monitor your LLM and not think about S3 lifecycle policies, Arize's managed approach is the path of least resistance. Both will charge you for the privilege of keeping data, and both will charge you *differently* for deleting it too slowly. The real winner is the one whose policy you'll actually remember to configure correctly at 2 AM before your trial ends.
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I'm a project lead at a mid-sized fintech, so data retention is a serious compliance checkpoint for us, not just a cost thing. We started with Arize but switched to Langfuse's cloud offering about six months ago to track our RAG app and a couple of fine-tuned models in production.
**Pricing and Data Hoarding:** Arize's pricing felt opaque once you scaled; we were on a "contact us" plan that ballooned. Their retention is managed through support tickets or automated tiering to cheaper storage, but you pay to keep it accessible. Langfuse Cloud is clearer: $29/project/month base, and you pay $10/GB/month for hot storage, with data automatically moving to cold storage ($0.10/GB/month) after 30 days. You can set it to delete from cold storage entirely. The hidden cost is your own vigilance in setting the policies.
**Default Behavior:** Arize felt more "managed" for you, which was nice as beginners. Langfuse's default is to keep everything unless you tell it not to. This is the scariest part for newcomers - you could rack up a huge bill or compliance headache by just deploying and forgetting. You must manually configure TTLs per project, or use their batch deletion API.
**Granularity vs. Simplicity:** Langfuse wins on granular control. You can set different TTLs for traces, spans, and even individual observation types (like LLM calls). In Arize, our retention was more of a blanket policy per project. Langfuse's flexibility is perfect if you need to keep some data for 90 days for model evaluation but ditch raw traces after 7.
**Compliance Workload:** With Arize, we relied on their SOC 2 reports and their process. With self-hosted Langfuse (which we considered), you bear the full burden. Their cloud offering is a middle ground - they handle infra security, but you are solely responsible for configuring the actual data deletion to meet your internal policies. It shifted the compliance workload from vendor management to configuration management on our side.
My pick is Langfuse, but only if you have the bandwidth to actively manage the retention settings from day one. For a small team that wants a truly "set and forget" system where the vendor handles lifecycle, Arize's approach is less risky. If granular control and cost predictability for high-volume tracing are your top needs, Langfuse is the way to go. Tell us your team size and whether you have a dedicated compliance person to make the call clean.