Nitish Tiwari

Founder & CEO @parseablehq. Forever curious. Startups ∩ open source ∩ infrastructure. Try now at 👀🚀

[Nitish Tiwari @nitisht_]

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 https://t.co/jBohLqK1kN

[Nitish Tiwari @nitisht_]

This is very cool! I wish we had this while building @parseablehq on object store(s). Compatibility remains a huge problem.

[Ovais Tariq @ovaistariq]

I am really excited for the storagesdk launch. storagesdk provides a unified storage API across all the major storage providers with first class support for snapshots and forks. A lot of care and thought has been put into thinking about the DX!

[Ovais Tariq @ovaistariq]

I am really excited for the storagesdk launch. storagesdk provides a unified storage API across all the major storage providers with first class support for snapshots and forks. A lot of care and thought has been put into thinking about the DX!

[Sam Bhagwat @calcsam]

https://t.co/zqfZZAMDX9

[Ovais Tariq @ovaistariq]

We are adding a bunch of new features to make it easier for you to use object storage. We will be releasing a few more in the next few days.

[Mario Zechner @badlogicgames]

the one thing @mitsuhiko taught me: merged client & server logs. very useful.

[Nitish Tiwari @nitisht_]

@kenwuuuu @mastra seems great - we're looking at it for our internal agents.

[Ovais Tariq @ovaistariq]

I am bullish on the decentralization of compute. We are going to need 100x more capacity at least if not 1000x and it’s not going to be the big 3 serving all that demand.

[Nitish Tiwari @nitisht_]

Zero runtime dependencies have saved us a lot of trouble at Parseable.

[Joran Dirk Greef @jorandirkgreef]

TigerBeetle has a strict zero dependencies rule.

[Jonathan Grahl @jonathangrahl]

This has taken a lot of effort to get right. I believe we're one of the few that manage to achieve this at our scale :)
Have fun!

[Nitish Tiwari @nitisht_]

Read here: https://t.co/QcYHvGkiXz

[Nitish Tiwari @nitisht_]

When should you use; wide events vs logs, metrics and traces? I wrote a decision framework.

> Metrics tell you something is wrong.
> Wide events tell you exactly what went wrong and why.
> Wide events however come at a hefty price tag.
> Cost is a storage architecture problem, not a data model problem.

[Corey Quinn @QuinnyPig]

"AI code is crap."

The shit your human engineers get up to:

[Ovais Tariq @ovaistariq]

Pretty cool to see @TigrisData come out ahead of S3 and GCS.

[dax @thdxr]

a million posts on my feed telling me how to build agents

zero people talking about building agents

[Nitish Tiwari @nitisht_]

@peer_rich @calcom long time user, not yet a customer.

[Nitish Tiwari @nitisht_]

@katiekirsch This is very well written!

[Nitish Tiwari @nitisht_]

When you get paged, and you're trying to find what's happening; most of the times the trace / log overview page is not the best place to be.

You want to see all the errors and then work your way back to the traces & logs. Because error messages tell a story of what, how and when.

Based on several users input, we built Parseable Error page.

> Errors grouped by exception type, message & stack trace
> Spark charts show if it's a burst or a chronic issue
> AI summary gives you root cause + fix checklist before you read a single stack frame
> One click to correlate with logs or escalate to Keystone

https://t.co/8Ehgfzbafi

[Nitish Tiwari @nitisht_]

@gabemonroy Congrats @gabemonroy 🎉

[Katie @katiekirsch]

Stoked to announce the @a16z Growth Engineer Fellowship!

A new 8-week cohort of growth engineers, AI-native GTM leaders, and agent builders shaping the future of company growth.

Join us! Apps went live today.

Fellowship starts in May 2026.

Featuring @andrewchen, @kirbyman01, @luclevesque, @laurynmotamedi, @ramanrmalik, @kylecnorton, @gauravvohra, @lukeharries, @egavrilenko11, and more.

Apply here: https://t.co/5oQo6fS03n

Details: https://t.co/BR4OHZJdBG

CC @david__booth, @eriktorenberg, @tdoggyholhol

[Nitish Tiwari @nitisht_]

We got great responses to our GTM Engineer post and found the right person for that role. Thank you to everyone who reached out.

Now hiring for a another key role: Founder's Office Associate with a GTM focus.

We're building the first purpose-built observability datalake for the agentic era. Backed by Surge.

Looking for someone who is:
> Keen to run experiment cycles end to end — outreach, discovery, demos.
> Can build and iterate on positioning, content, and outbound strategy
> Can own the messy middle between product, customers, and market
> Comfortable with ambiguity. Fast with AI tooling. Allergic to busywork.

This is a strategic hire for us. At our stage, GTM decisions are company-defining — who we sell to, how we position, what the first 5 minutes of the product feel like. But strategy without output is useless. You'll be measured on what you ship, not what you present.

In-person, in office, BLR. Early equity. High leverage, steep learning curve.

DM me.