Reveal

06/11/2026

Bigger model ≠ better product. For embedded analytics, the SLM-vs-LLM decision drives latency, token cost, governance, and deployment flexibility.

Swipe through the trade-offs:
→ SLMs: fast, cheap, great for frequent, well-scoped analytics queries
→ LLMs: deeper reasoning for open-ended exploration
→ Hybrid: route by intent — most production teams end up here

Reveal lets you bring your own model and control cost per tenant, so the choice stays yours as you scale.

👉 Read the breakdown: https://www.revealbi.io/blog/slm-vs-llm?utm_source=linkedin&utm_medium=organic_social&utm_campaign=jun26_cio_cost&utm_content=slm_vs_llm

The SLM vs. LLM choice affects latency, token costs, governance, and deployment flexibility. See which one fits your embedded analytics needs

06/09/2026

Reveal 2.0 is here.

We rebuilt the Reveal Web SDK from the ground up — so your team can embed analytics into your product without fighting your own toolchain to do it.

🔹 No jQuery. TypeScript-first with full type definitions.
🔹 IIFE and ESM builds — install via CDN or npm
🔹 New connectors for Azure CosmosDB and ClickHouse
🔹 Improved accessibility across the SDK
🔹 Node and Java SDKs at API feature parity with ASP.NET (Preview)

If you evaluated Reveal before and the SDK felt behind your stack — that is no longer the case.

Built to fit your stack. Not the other way around.

👉 https://www.revealbi.io/blog/reveal-2-0-release

05/31/2026

May is almost over. Q2 is more than half done.

Question for the product leaders in my network:

What's the ONE analytics thing you wish you'd already shipped to your users by now?

More often than not, the answer we hear is some version of: "A way for users to answer their own questions without asking us."

That's the adoption gap. And it's fixable.

Drop your answer below — genuinely curious what's on your roadmap for Q3.

05/30/2026

The embedded analytics market is maturing. Here's what separates the platforms that will win from the ones that won't.

The checklist that matters in 2026:

- Conversational AI — not as a roadmap item, but in production
- True SDK — React, Angular, .NET, Java, not just a JS snippet
- Enterprise governance — row-level security, tenant isolation, audit logs
- Predictable pricing — per-seat models break at scale
- On-premise deployment — regulated industries won't go cloud-only
- Hybrid model AI architecture — SLM-first is the economically rational choice

SaaS teams choosing an analytics platform in 2026 need to ask: is this vendor 12 months ahead of us, or 12 months behind?

If you're evaluating embedded analytics this year — these are the questions worth asking.

👉 revealbi.io/embedded-analytics

05/29/2026

Three questions every SaaS CTO should ask before shipping an AI analytics feature.

If you can't answer all three confidently, you're not ready to ship.

1. Does your AI layer respect your row-level security? Not just at the dashboard level. At the query construction level. If a user asks a question that should surface restricted data, does your AI refuse — or does it answer?

2. Are your token costs bounded? Per tenant. Per user. Per query type. An unbounded AI feature is an unlimited liability on your cost of goods sold. Have you modeled what happens at 10x your current user volume?

3. Can you audit AI-generated queries? When a regulator asks what data your AI accessed on behalf of a specific user on a specific date — do you have a log? If not, you have a compliance gap.

These aren't gotcha questions. They're the conversations your enterprise customers' procurement teams will ask.

Reveal Embedded Analytics was designed with all three as foundational requirements.

👉 revealbi.io/on-prem-analytics

05/27/2026

The build vs buy calculator nobody uses — and the one question that actually matters.

Most analyses compare license cost vs developer hours and feature parity. They miss the compounding cost.

Here's what the spreadsheet usually leaves out:

Year 1: You build it. It works. Everyone's happy.
Year 2: A customer wants multi-tenant row-level security. That's a 2-month engineering sprint.
Year 3: A customer wants conversational AI. That's a 6-month project with an AI team you probably don't have.
Year 4: You're now maintaining two products. Your engineering team resents it. Your analytics is still 18 months behind what's commercially available.

The question that actually matters isn't "can we build this?"

It's: "Is analytics our core competency — or is it infrastructure we're paying engineering time to reinvent?"

For most SaaS companies, it's infrastructure.

👉 See the full build vs buy breakdown: revealbi.io/blog/should-you-buy-or-build-your-analytics-platform

05/22/2026

The embedded analytics pricing conversation you need to have before Q3 planning.

Most SaaS teams get surprised by analytics costs at two points:

1. When they scale past 1,000 active users and per-seat pricing becomes untenable
2. When they add AI features and discover token costs are unbounded

Both of these are architectural decisions made early that become financial problems late.

How Reveal Embedded Analytics handles pricing:
- Predictable, flat pricing — not per-seat, not per-query
- Per-tenant token limits for AI features — you control the ceiling
- Per-user query budgets — power users can't crater your margins
- Cloud, on-premise, or hybrid — same pricing model across deployment options

The analytics platform that surprises you with a bill at 10k users is not a partner. It's a cost center.

We're designed to be a growth enabler.

👉 revealbi.io/pricing/embedded-analytics

05/21/2026

SLM vs LLM for embedded analytics: which one actually wins?

Short answer: it depends on the query. Here's the longer answer.

For embedded analytics, most user questions are structured:
- Show me revenue by region last quarter
- What's the defect rate on Line 3 this week?
- Compare Q1 2025 vs Q1 2026 for APAC

These are pattern-matching queries on structured data. They don't require the reasoning depth of a large general model.

An SLM handles them faster, cheaper, and often more accurately — because it's been tuned specifically for structured data retrieval.

Where LLMs win:
- Complex multi-step reasoning across unstructured and structured data
- Narrative generation ("Write a summary of this quarter's performance")
- Edge cases where the SLM confidence score is low

The right architecture: SLM-first, with LLM fallback.

That's exactly how Reveal AI works:
- SLM handles the majority of queries (fast, cheap)
- LLM kicks in when query complexity demands it
- Token costs stay predictable because you're not routing every query through the most expensive model

👉 revealbi.io/blog/slm-vs-llm

The SLM vs. LLM choice affects latency, token costs, governance, and deployment flexibility. See which one fits your embedded analytics needs

05/20/2026

What's the real build vs buy tipping point for embedded analytics at your company?

At what point did you (or your team) decide that building analytics in-house wasn't the right call?

Was it:
A) The maintenance cost spiral
B) A customer asking for a feature you'd never have time to build
C) A security/governance requirement you couldn't meet
D) The realization that analytics wasn't your core product
E) Something else entirely

We're researching how SaaS teams actually make this decision — and the answer is almost never what we expect.

Drop your story below. Genuinely curious.

05/19/2026

React developers: here's what embedded analytics looks like when it's done right.

Not a component that sort-of-fits your app. Not an iFrame wrapped in a div.

A native SDK that integrates with your React project the same way any other component library does.

With Reveal Embedded Analytics you import RevealView from our React package, pass it your dashboard config, and your analytics inherits your design system, your authentication, your routing, and your state management patterns.

And when your customers want conversational AI, you add one more component. No backend AI infrastructure to build. No governance layer to architect. No token cost surprises.

Just analytics that works — in the stack your team already knows.

👉 revealbi.io/blog/analytics-sdk

Learn what an analytics SDK is and how to choose the right one for your SaaS product. See what to look for to scale without constraints.