e6data

Agent-native data foundations for scaling your AI apps, analytics, and agents. With e6data, you can do more, run on your terms, and account for every action.

[e6data @e6data]

Another example of model training optimizations solving token costs

As the dust settles, we’re building the data foundations with the price-performance capabilities agent-scale velocity will demand

Query, ingest, ETL
Agent workflows -> agent traces

Reach out if you’d like to learn more

[Brian Armstrong @brian_armstrong]

We were able to fine tune an open weight model (Qwen3.5-9B) on proprietary internal data at @coinbase, and it out performed frontier models, at much lower cost and speed, for a specialized task (fraud prevention in this case).

I think we'll see more of this pattern inside companies. The frontier models are amazing generalists. But increasingly you'll see specialist models out perform on more narrow tasks, either working on teams, or by themselves.

We're making it easy to fine tune models internally for our employees. We even have a skill for it in our internal harness that makes it easy to spin these up and start training them:

"/fine-tune-and-serve-llm I want to fine-tune Qwen for payments risk review. help me get started"

I imagine we'll have some backend services that could be replaced entirely by this new architecture.

[e6data @e6data]

@levie One of the many reasons we’re making compute cheaper and faster.

[e6data @e6data]

@guilleflorvs Point #1 is why we exist.

[e6data @e6data]

We love the builders who challenge assumptions because when your team starts saying, “Yep, all good” to basically everything, eventually you develop trust issues, so why are we trusting AI agents who grade their own work this way too?

This is why Jev is getting so much attention.

Our teammate Prakalp’s post breaks down how Jev grades itself with more confidence. Jev returns a probability you can test against your own data. You decide how sure the agent needs to be before it acts, and that bar can differ from one action to the next.

In one benchmark, putting Jev in front of the agent’s tool choice cut wrong picks from 16.8% to 7.3%.

His advice for anyone building agents is to stop asking yours if it’s sure. Like a ‘yes man’ coworker, it will always say yes.

[Prakalp Choubey @ChoubeyPrakalp]

@typesafeai's Jev returns decisions with probabilities you can actually test. Here's how building agents might just have changed forever https://t.co/AiUxuXlJDs

[e6data @e6data]

Can AI data readiness be avoided? Actually, yes.

We’ve seen this before. You’re eager to bring AI into your enterprise, and ROI sounds incredible… until your tech diligence uncovers the overhaul your data platform needs to achieve the outcomes you want. The reasons are common:
- Context is scattered.
- Freshness varies.
- Access gets complicated.
- Governance adds constraints.

... and more. The reality is, most data platforms have blockers for you to get AI right.

If this sounds familiar, and you or someone you know has taken on AI work that created infrastructure modernization and possibly migration requirements that will take quarters to deliver, we have the playbook to skip the delay and leapfrog your readiness to faster ROI.

If you start with one workflow, the one you think matters most. Our stack gives it the evidence, context, and controls it actually needs, on your existing environment. We work around the gaps blocking that outcome, and leave your data platform alone.

Join us at Gartner Data & Analytics Summit, Mumbai, where Vignesh Ganesan is hosting a session to pull back the curtain on how enterprises can take a lean approach to leapfrog unnecessary stages that block you, and prove ROI from your agents in production in weeks.

‘ROI from agents despite gaps and imperfections in data platforms.’
21 September | 3:50 PM
Theatre, Exhibit Showcase

And if you can’t make it, you can find us at Booth 305. See you there!

[e6data @e6data]

Agreed. Measuring actual outcomes will need to extend into the compute layer too.

The vCPU billing and scaling of the new compute engine architecture we've built will be a key input to finer grain attribution of outcomes, rather than the fuzzier t-shirt size warehouse compute % allocations we typically see today.

[e6data @e6data]

Agent querying lives in a different league. If tomorrow, your CEO required every team to run agentic analytics on your data, how much of your infrastructure would break?

Find us at Booth H68 at Big Data LDN next week for the playbook to scale without breaking.

If you ask ten people how to get AI agents to query your data directly, you’ll get ten different answers involving caches, indexes, and a warehouse that struggles to perform under concurrency spikes. Most data infrastructure was built for analyst teams running dashboards, not agent swarms firing off hundreds of queries a minute, at all hours, with no pattern to predict.

We built e6data so you can handle 1,000+ queries per second with under one-second latency, bring streaming data into queryable Iceberg tables in about 15 seconds, and run it all in your existing cloud or on-prem environment, so your data never leaves your systems.

Can’t make it? Feel free to schedule a time with us earlier here: https://t.co/bzQaYccCxO

[e6data @e6data]

Congrats to Ramp! This will make the token layer boring (in the best possible way). Once everyone stops bleeding token cost, the next storm is going to hit: agent query volume. We’ve been quietly building for it at e6data.

Router solves tokens, we're solving the compute engine.

[Ramp @tryramp]

We bought https://t.co/4WFUJAf1Cc.

Now we're building the best one.

Our customers buy quadrillions of tokens every month through Ramp, and we've run our own AI on this router for 3 years to keep costs down.

Today it's yours at https://t.co/4WFUJAf1Cc

[e6data @e6data]

Nobody files a ticket for a slow dashboard.

They just stop opening it and start asking you in Slack. That's how you lose a data product without a single alert firing.

Read more here: https://t.co/RgdiKuJ7fT

[e6data @e6data]

Your compaction job fails nightly with a commit conflict. Losing the race to the streaming writer isn't the first fix; reconciling delete files at scan time is. Rewrite those first, short job, commits before the stream can invalidate it, split compute and then fix the race.

[e6data @e6data]

The SQL warehouse is stuck starting, and the first query just waits.

Your dashboard isn't slow. It hasn't started yet.

Read more here: https://t.co/tLocWrBUnW

[e6data @e6data]

We’re thrilled to introduce e6data’s native MCP.

Connect MCP-compatible agents like Claude Code, Codex, Cursor, and custom AI workflows directly to your lakehouse, without extra infra or custom integrations.

Huge shoutout to Arnav and Tanay for bringing this to life.

[e6data @e6data]

There’s more hiding inside open lakehouse architectures than most teams realize.

Catch @vishnuv9248's talk on June 18 at 11:30 AM PDT, then drop by booth 548 to meet the e6data team.

Cost, governance, portability, and the freedom to run anywhere.

[e6data @e6data]

Next stop: Databricks Data + AI Summit.

We’ll be there talking about how teams can scale lakehouse analytics without moving data, reworking their stack, or letting costs climb as usage increases.

Zero migration. 60% cost savings. Run anywhere.

[e6data @e6data]

That’s a wrap on Snowflake Summit.

Big thanks to everyone who stopped by the e6data booth, signed up for a demo, and joined the conversations.

Loved the chats on Iceberg v3, adoption, and where open table formats go next.

See you at the next one.

[e6data @e6data]

2/ At Booth #1605, the conversations made this theme very real.

Cost keeps coming up, especially as agentic workloads add new compute pressure on top of already growing analytics demand.

We’ve booked 40+ demos with teams thinking through this. If you’re at Moscone, drop in.

[e6data @e6data]

1/ Day 1 at Snowflake Summit 26 had a clear theme: enterprise AI is moving out of the pilot phase.

Sridhar’s keynote put it well: the winners in AI won’t be the companies with the most models. They’ll be the ones with the best governed, connected, and accessible data.

[e6data @e6data]

Heading to Snowflake Summit 2026? Meet e6data at Booth #1605.

Bring the workloads you’re trying to scale: high concurrency, rising compute costs, customer-facing analytics, or new AI use cases.

Book a demo and enter for a chance to win 1 of 3 Meta Glasses.

#SnowflakeSummit

[e6data @e6data]

What’s putting pressure on your data team right now?

More concurrency? Customer-facing analytics? Cloud costs?

If you’re at the Moscone Center, come meet e6data at booth 1605.

Let’s talk bottlenecks and a scalable path forward.