Founder @parseablehq | Distributed compute and data systems, observability & telemetry
SF / BLR
Joined October 2009
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Parseable is a telemetry datalake on object storage. Logs, metrics, traces, and AI agent telemetry go into one system, stored as open Parquet files on object store. You query with SQL or natural language. You keep data for 12 months instead of 30 days. You pay 80x lesser.
Some context on why we built this.
Observability costs grow linearly with data volume, but value doesn't. Most teams delete telemetry after a month because their platform makes retention irrational. AI workloads are making this worse — agents generate telemetry at extreme volume and cardinality, and that data is scattered across separate tools that don't correlate with each other.
We spent the last year talking to engineering leaders about this. 75% cited cost as their primary observability concern. 84% of organizations plan to consolidate tools in 2026. The pattern was clear: teams are paying more each year, retaining less data, and getting slower queries.
@parseablehq fixes the economics by fixing the architecture. Columnar Parquet format compresses telemetry up to 90%. Compute and storage scale independently. No pre-indexing, no write amplification, no JVM overhead. The entire engine is written in Rust and runs on @TigrisData , a globally distributed S3-compatible store with zero egress fees.
Pro plan is $0.39/GB ingested. 12 months retention. All AI features included — natural language queries, forecasting, anomaly detection. Unlimited hosts, users, dashboards. 14-day free trial, no credit card.
Enterprise gets bring-your-own-bucket for unlimited retention, data residency, BYOC or on-prem deployment, and Iceberg support.
Self-hosted is still available. Single binary. Same engine.
If you're running AI workloads alongside traditional infrastructure and tired of stitching together three tools to understand one incident, try out Parseable today at telemetry.new/
Once ingestion and retention is addressed, MCP really is one of the best ways to interface with telemetry data. Ask, build a hypothesis and take action.
Nitish Tiwari retweeted
RIP my index. Many great folks to chat with. But I'm also always looking for ad-hoc contributions from experienced developers outside of this particular column.
Read the contribution guide for instructions.
theconsensus.dev/contribute.…
I'm looking for someone with a PhD (or decently into one), or 10+ years in systems programming, anywhere in the world Remote allows me to pay, to publish a monthly column for @theconsensusdev selecting and putting a CS research paper in context. $600 per article.
No particular length requirement as long as it's well done. Use AI for whatever you want but you must write by hand and own correctness (I'm checking too of course).
Please pass along.
[email protected]
Nitish Tiwari retweeted
🎉 Happy 16th Birthday to @nats_io! It has been an amazing journey and we are just getting started as the central nervous system for cloud to edge systems and agentic AI as it moves closer to the physical world.
We are also open for voting to graduate from the @CloudNativeFdn - Only the TOC's votes are official but a 👍 is always welcome - github.com/cncf/toc/issues/2…
And if you find the technology valuable please consider giving us a star! github.com/nats-io/nats-serv…
![[Graduation] NATS Graduation Application · Issue #2042 · cncf/toc](https://pbs.twimg.com/card_img/2116200086040416256/0i4KAXnC?format=jpg&name=800x419)
Nitish Tiwari retweeted
Come work with me at Resend. We’re hiring a Platform Engineer.
What I love most about Resend is solving scaling problems across infra, code and processes. Seeing those solutions have a real-world impact is amazing. And I get to do it with the most talented, kind people I’ve ever worked with.
You’ll be a great fit if:
- You have 8+ years of experience across a mix of: AWS, IaC (CDK/Terraform), DevOps, security, observability (Datadog/Grafana), networking and databases like Postgres, Redis and Elasticsearch
- You have an AI mindset and are curious about how it changes the way we build
- You take ownership and use the autonomy to improve things at every opportunity
- TypeScript and Go are what we mostly use.
None of this is a hard requirement, and the full details are in the career link.
Perks:
🌍 Fully remote across the Americas/Europe time zones
🛫 Two off-sites per year
♥️ Did I mention working with incredibly talented and kind people
💰 Above average compensation, depending on location and experience
Don’t apply if you don’t love your craft or need hand-holding.
- Europe time zones: resend.com/careers/76dcf582-…
- America time zones: resend.com/careers/a9fc1d02-…
Crazy 24 hours for security teams - eMudhra, TiDB, PHP, GitLab, Railway.
It just feels like the beginning, we have a long way to go as we adjust to the new paradigms of ensuring security and privacy.
Nitish Tiwari retweeted
My cofounder Somesh @MathproBro is hosting a session Friday at 3 PM on the math behind AI: graphs, GNNs, transformers and deceptively simple questions about how they work.
If you want a path to work in applied math or at an AI lab, pls do attend. Link below
1. what?
We discovered a sophisticated recording device that was hidden inside one of our offices. We used our models to understand the device and where it had come from, identifying that a specific employee had planted it a few days earlier. We referred the matter to the FBI, and we’re grateful to the agents and prosecutors who moved quickly to make an arrest. The FBI’s investigation is ongoing and we will continue to support their work.
At this time, we have no indications of this individual inappropriately accessing or exfiltrating model weights, source code, or customer data. The employee was terminated.
We take protecting our technology, data, and people seriously, and as a matter of national importance. Our security program is designed to protect against a wide range of threat actors, from insider risks to cybercriminals to nation states, and this case is an example of how AI can help address these sophisticated security threats.
complicated setup while self hosting observability tooling is one of the biggest problems, yet least talked about. So we built Parseable as a single binary, that can be deployed in minutes on your laptop. The same simplicity then carries over when you deploy to prod, with zero external dependencies and an object store first design.
Nitish Tiwari retweeted
100M time-series/min? That's not just big data, that's a *statement*. Open observability datalakes like @ParseableHQ are the ONLY way solo devs can truly own their stack without vendor lock-in. Essential for scaling AI/data products without breaking the bank. #microSaaS #buildinp
Agents work best when grounded in real data. pb cli allows unencumbered access to the data via linux native tooling. This way the agent doesn't need to find its way around a UI while debugging something - agents are faster and accurate with CLI
sed aka stream editor allows bulk changes to unstructured text. This combined with pb offers a great way to understand what is happening while you debug
CLI tools are very helpful in slicing data points once you've identified the data you want to look at. We wrote pb to allow users to really take control of their workflows. See how to pipe pb output to awk for processing.
Self hosting observability system often means letting go of the high end functionality like anomaly detection, or sometimes stitching together different components that are fragile.
We've bundled an accurate, zero shot time series prediction model within Parseable - so you get anomaly detection built-in without sending data out.