Show HN: Oodle – serverless, fully-managed, drop-in replacement for Prometheus (blog.oodle.ai)
My co-founder, Vijay and I are excited to open up Oodle for everyone.
We used to be observability geeks at Rubrik. Our metrics bills grew like 20x over 4 years. We tried to control spend by getting better visibility, blocking high cardinality labels like pod_id, cluster_id, and customer_id. But that made debugging issues complicated. App engineers hated blocking metrics, and blocking others' code reviews was not fun for platform engineers either! Migrations (and lock-ins) were very painful, the first migration from Influx to Signalfx took 6+ months and the second migration from Splunk took over a year.
Oodle is taking a new approach to building a cost-efficient serverless metrics observability platform. It delivers fast performance at high scale. We leverage custom storage format on S3, tuned for metrics data. Queries are serverless. The hard part is how to achieve fast performance while optimizing for costs (every cpu cycle, storage/memory byte counts!). We've written about the architecture in more detail on our blog: https://blog.oodle.ai/building-a-high-performance-low-cost-m...
Try out our playground with 13M+ active time series/hr & 13B+ samples/day: https://play.oodle.ai
Explore all features with live data via Quick Signup: https://us1.oodle.ai/signup - Instant exploration (<5min): Run one command to stream synthetic metrics to your account - Easy integration (<15min): Explore with your data from existing Prometheus or OTel setup.
We’d love your feedback!
cheers
89 comments
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RAD is now owned by Epic Games (acquired in 2021) so they have very deep pockets.
A lot of people, including myself, were clearly initially confused that there must be some association given you are using this name in a not-entirely-unrelated field.
IANAL but I hope you're real sure that you are legally in the clear before you commit too deeply to the name.
For the same reasons op listed or for other reasons?
We think Oodle combines the benefits of open source (compatibility, no lock-in) with the operational simplicity, reliability of commercial vendors. Some products might give a false illusion of no-lock in just because it's open source, it wouldn’t mean you don't have lock-ins. We believe what really matters is "Open Source Compatible" - i.e. how easy it is to get in? How easy is it to switch out? (to a de-facto open source standard like Prometheus/Grafana should you need to disconnect ties with the vendor). Security and compliance is the other big part - we are working on adding compliance like SOC2, CCPA etc.
Protocol compatibility is a baseline requirement for a drop-in replacement. Without that, your product would need to be like 10x cheaper than Prometheus for anyone to consider devoting engineering resources to switching to it.
Existing FOSS solutions are reliable and inexpensive enough, not even considering the effort and unknowns involved with swapping out a major component of the observatory stack. Between that, your distorted view of open source, and your focus on checkbox compliance, I’m unlikely to ever consider trying your software. I wish you luck though, and I hope you learn more about the value of FOSS.
We believe there are clear benefits of open source. In fact, we believe in it so much that we made our product OSS compatible from Day 1. (btw, not every observability product is OSS compatible).
We also understand not everyone's evaluation criteria is the same. Upon speaking with number of our early users, we repeatedly found that what really matters is "is the product reliable, cost-effective?", "is the product easy to use and open source compatible?", "is the product easy to migrate in/out?". So, we tried to address those head on. We also heard some open source solutions are indeed unreliable especially at scale e.g. Prometheus has scaling challenges beyond 2M+ active time series / hour and it is not horizontally scalable. People tend to over-provision CPU/Memory, despite that queries time out at any meaningful scale high cardinality queries (this is a well understood problem).
Ability to inspect code, do patches themselves may be an evaluation criteria for some users, we found that it was not the major evaluation criteria among our users. I've led multiple evaluations at Rubrik (open source + non-open source), ability to patch software was not the most important criteria - reliability, operational overhead, cost, ease of use, and ability to switch in/out were may more important.
So its not really a drop in replacement for prometheus then, its more of a send all your data to some other bloke kind of replacement.
Software as a service is fine, but you dont need to hide it behind hip marketing terminology.
Why use a .ai domain? I love LLM but this is a turnoff to me.
We do use pre-LLM-era AI and statistical analysis to provide insights and auto create dashboards for alerts (currently in alpha).
Edit: fixed now.
This also helps find users for whom this is a huge pain point - metrics costs are so high that they’d love to talk to someone and complain about the problem.
Saying it is serverless means nothing to customers unless the serverless aspect applies to them, which in this case it doesn’t. If you’re only selling access to your product for a fee, then whether it’s serverless or not, customers couldn’t care less.
We actually started with "reduce your costs by 3-10x with infinite scalability" without talking about storing in s3 and serverless (along the lines of your thinking - only talk about benefits + what you do). But our users, engineers by their very nature, were skeptical about how Oodle works, so we ended up settling on a combination of why, what and how. This resonated better with our early customers and prospects.
Why not dump all metrics , events and logs into Clickhouse ? and purge data as necessary? For small to medium sized businesses/solution ecosystem, will this be be enough ?
Yes, you can use SQL to read metrics directly from ClickHouse tables. However, many people prefer the simplicity of PromQL compared to the flexibility of SQL. So until ClickHouse gets native PromQL support, it is in the early stages.
[1] https://clickhouse.com/docs/en/engines/table-engines/special...
ClickHouse is an analytical database (for events). Yes, you can do metrics in there (or PostgreSQL for that matter). Observability has their own needs, so specialized solutions work better, more integrations and out-of-the box tooling already provided.
With generic databases it's more like a constructor that you'd need to develop a lot to become workable. For example, let's say you have 50m active time series, with 1%/hour churn rate. What would be the database structure in ClickHouse? (my answer is that I don't know. I know mostly VictoriaMetrics, and there's no such question there, the structure is already implemented).
[1] https://www.radgametools.com/oodle.htm
Edit: Or maybe the AGPL just requires releasing any code you change? I could be confused.
Found it: https://github.com/oodle-ai/grafana
Why do this instead of just build a data source?
Edit: Not to be that guy (but I'm about to be that guy). You have links to grafana.com (which, is your competitor), all over the source in your page. This also lists the version as 11.1.0, which was released 6-21.
All of the versions in your fork repo mention 11.0.0-pre. Did I find the wrong repo, or are you using code that you haven't published?
The reason I mostly care is that this is the sort of reason that good open source projects get closed down, and that makes me a bit sad.
Here is the branch we use: https://github.com/oodle-ai/grafana/tree/v11.1.0-oodle-stabl..., which has all the changes we have made in Grafana.
Also, found a few typos and a broken link, see error report here: https://triplechecker.com/s/xEd4Hp/oodle.ai?v=uxGS1
No lock-in means it’s 100% open source (PromQL) compatible. You can swap out vendors or move to self-hosted open source solutions should you need to move away from Oodle. When you migrate out, you get to export all your data, dashboards and alerts. you don't need to make any code changes.
We support bringing your own bucket (BYOB) for large enterprise customers however, you cannot bring your own compute at this time. Our thoughts are along the lines of how Snowflake approached the problem - everything fully managed to keep the operational overhead minimal. https://jack-vanlightly.com/blog/2023/9/25/on-the-future-of-...
This translates to about $5.2 per 1k metrics, Oodle is $1 per 1k metrics!
Am I missing something?
I see $0.182/h/1M ts = $0.182 per 1k metrics (S.STARTER.A instance)
Also Oodle website says to "Contact for pricing beyond 5M". While VictoriaMetrics Cloud only starts at 1M (S.STARTER.A), and "Contact Us" starts at 125M active time series.
PS: time series is only one dimension, there's also storage size, retention, query/alerts frequency etc.
RE: Victoria Metrics Pricing: Pls see https://victoriametrics.com/products/cloud/. The pricing that you are referring doesn't seem public, looks like you've to sign up to see that pricing. $190/month is a single node pricing. For any real use cases, you need HA and victoria metrics enterprise pricing for a cluster starts at $1300/month for 250k metrics. This translates to $5.2 per 1k metrics (5x more expensive than Oodle). For a real scale about ~2.5M or ~5M time series / hour, Oodle is around half the cost of victoria metrics.
RE: Pricing dimensions, we've simplified our pricing by indexing on a single dimension. We don't require our customers to choose machine type, RAM, CPU etc. There are other limits but for the most part, they don't matter so much in our pricing.
RE: Pricing tiers, anything more than $30-50k/year (>5M time series / hour), companies usually to talk with someone for volume discounts rather than go with online pricing.
Although I see for C.XLARGE.HA they go down to $0.56 per 1k time-series per month, just not for the smallest instance. So looks like VictoriaMetrics can be 2x less expensive than Oodle on large scale.
250k metrics - Oodle is 5x cheaper, < 5M metrics - Oodle 2-3x cheaper, beyond 5M - talk to us (our pricing will be competitive and will be better)
Not to mention they offer the best metrics free tier in the entire space... Let me know if you know of a better free tier ;)
Unfortunately, Grafana Cloud only offers 10k active series, which is really easy to surpass even in a homelab; meanwhile, Oodle offers 100k.