Gemini Spark Review: Google's 24/7 AI Agent
Gemini Spark is Google's new agentic AI assistant announced at I/O 2026. Learn what it does, when it launches for Google AI Ultra subscribers, and how it compares to Claude MCP and ChatGPT.
I was up at 3am in Sydney watching the Google I/O 2026 livestream, coffee in hand, half-expecting another round of incremental Gemini updates. Then Sundar Pichai introduced Gemini Spark, and I found myself actually pausing the stream to take notes.
This is a post about what we know, what we don't know, and whether it's worth paying for Google AI Ultra to get early access.
TL;DR
- What is it? Gemini Spark is Google's new 24/7 agentic AI assistant that runs on dedicated cloud VMs — no laptop required
- When can you use it? Google AI Ultra subscribers get access "next week" from the May 19 announcement; broad availability TBD
- What does it compete with? Claude with MCP, ChatGPT Custom GPTs, OpenHands — but the persistent VM execution model is genuinely different
- The honest catch: It's not publicly available yet, pricing for Google AI Ultra is around $249/month, and we have zero real-world performance data
Who I Am and Why This Matters to Me
I run a network of AI tools sites from Sydney, including openaitoolshub.org. I've spent the last 18 months testing agentic AI setups — Claude with MCP running local tools, ChatGPT Custom GPTs, AutoGen multi-agent pipelines, OpenHands for code tasks.
I watched the I/O 2026 livestream from a café in Newtown at 3am local time. My partner thought I'd lost the plot. Maybe I had. But when you're a one-person operation trying to hit revenue targets with AI as your main productivity multiplier, the delta between "AI that needs a human in the loop" and "AI that works while you sleep" is the difference between 40-hour weeks and actually having a weekend.
That framing matters for how I'm thinking about Spark. I'm not evaluating it as a consumer product. I'm evaluating it as a solo founder who needs it to justify the cost.
⚖️ The real question isn't "Is Spark impressive?" — it's "Does Spark save me enough time to offset ~$249/month?"
What Gemini Spark Actually Is
Based on Google's I/O 2026 announcement, Gemini Spark is described as a "24/7 agentic assistant" built into the Gemini app.
Here's what Google confirmed:
- Persistent execution: Spark runs on dedicated VMs on Google Cloud. You queue a task and close your laptop — Spark keeps working
- Cross-app reasoning: It can pull from Gmail, Docs, Sheets, and Slides to complete tasks. The demo showed Spark drafting a status email to a manager by reading project emails and pulling data from linked spreadsheets — without any human prompting mid-task
- MCP-compatible: Spark integrates with external services via Model Context Protocol, which means it can reach beyond Google's suite into third-party tools
- Long-horizon tasks: Sundar Pichai called it "the next evolution of smart digital assistants... agentic AI taking on long-horizon tasks with minimal oversight"
The key distinction from previous Gemini features: earlier versions required you to stay present. Spark is designed to run asynchronously and check back in when done.
📊 For context: the agentic AI market is growing fast. According to Gartner's 2026 predictions, over 33% of enterprise software applications will include agentic AI by 2028. Spark is Google's answer to that trend, but for individuals first.
⚠️ What We Don't Know Yet
This is where I think 95% of the posts you'll see about Gemini Spark are failing you. They're presenting the announcement as if it's a product review. It isn't.
Here's what's genuinely unclear:
Pricing specifics: Google confirmed Spark launches for Google AI Ultra subscribers. But Ultra currently costs around $249/month — and that's before they've added Spark. Will that price change? Will Spark be an add-on?
MCP server compatibility: "MCP-compatible" sounds great, but which servers? Claude's MCP community has hundreds of third-party servers. Does Spark work with existing MCP servers, or does Google mean it will use MCP as a protocol internally?
Rate limits and queue depth: If Spark runs on dedicated VMs, what happens if you kick off five concurrent long-running tasks? Is there a queue? A timeout?
Privacy handling: Spark accesses your emails, docs, and sheets. What data leaves Google's infrastructure? What's the retention policy? For solo founders handling client data, this isn't a footnote.
Reliability at task boundaries: Demo videos show clean handoffs. Real agentic systems fail at the edges — malformed API responses, ambiguous instructions, multi-step reasoning errors. We have zero data on Spark's error rate in production conditions.
I haven't been able to test Spark yet — Ultra subscribers get it next week from the announcement date, and I'm not currently on Ultra. But when I do get access, these are the first five things I'll verify.
🧭 Spark vs Other Agentic Assistants I've Actually Used
I can't compare Spark's real performance to anything because it isn't available yet. What I can do is lay out the comparison axis that I'll use when it is.
| Dimension | Gemini Spark | Claude + MCP | ChatGPT Custom GPTs | OpenHands | AutoGen |
|---|---|---|---|---|---|
| Persistent execution (no laptop needed) | ✅ Cloud VMs | ❌ Local MCP servers, session-bound | ❌ Requires active session | ⚠️ Server-hosted option | ⚠️ Requires running infra |
| Native Google Workspace access | ✅ Gmail/Docs/Sheets/Slides | 🔧 Via MCP plugins | 🔧 Via plugins, inconsistent | ❌ Not native | ❌ Not native |
| MCP community servers | ⚠️ TBD compatibility | ✅ Hundreds of community servers | ❌ Own plugin system | ✅ Supports MCP | 🔧 Custom tool integrations |
| My experience with it | ❌ Not yet available | ✅ 12+ months daily use | ✅ 8+ months testing | ✅ 4+ months for code tasks | ✅ 3+ months for batch work |
| Solo founder ROI signal | Unknown — waiting | High for file/code tasks | Medium — GPTs are inconsistent | High for dev work | Medium — setup overhead |
My actual working stack right now: Claude with MCP handles most long-document tasks and code reviews. ChatGPT handles things where the GPT store has a purpose-built plugin. OpenHands does autonomous code work overnight.
None of them give me "queue a task, close laptop, check results tomorrow." That's what Spark is promising. If it delivers, it genuinely displaces parts of this stack.
Should You Wait for Spark, or Use What's Available Now?
Depends on your situation.
If you're already a Google AI Ultra subscriber: You'll get access within the next week or two. Worth testing immediately. You're already paying for it.
If you're a solo founder considering switching to Ultra for Spark: I wouldn't make the jump yet. $249/month is $3,000 a year. Wait for 30 days of public reports. If the async execution holds up in real workloads, the ROI math becomes viable. Right now it's vaporware with a very credible announcement behind it.
If you're on Claude Pro or ChatGPT Plus and your workflows are mostly document and code tasks: Your current setup is probably fine for the next few months. Claude's MCP server support is mature and battle-tested. Spark's MCP compatibility is unproven.
The one user who should seriously consider getting on the Ultra waitlist immediately: anyone whose bottleneck is "I can't start the next AI task until the current one finishes." If that's you, Spark's persistent VM model addresses your exact problem.
How I'm Planning to Test Spark When It Launches
I'll be upfront: I don't have access yet, and I'm not going to pretend otherwise. Here's my test plan for when I do.
Week 1 — Basic async tasks:
- Queue a "summarize all emails from [client] this week and draft a status update" task overnight
- Check whether Spark accurately pulls from Sheets and Docs without hallucinating numbers
- Test: does it actually run while my laptop is closed, or does it stall?
Week 2 — MCP integration stress test:
- Connect Spark to 3 MCP servers I already use with Claude (Obsidian vault, GitHub, a Postgres database)
- Run the same task in Spark and Claude+MCP side by side
- Measure: task completion rate, hallucination rate, time saved
Week 3 — Real workload:
- Give Spark a 3-hour solo-founder task: research 5 competitor AI tools, pull their pricing from their websites, and draft a comparison document
- This is a task I currently spend 90 minutes doing manually each time
- If Spark does it in the background while I focus elsewhere, the $249/month becomes easier to justify
I'll publish results here at openaitoolshub.org when I have them.
Is Gemini Spark Free?
No. Based on the I/O 2026 announcement, Gemini Spark is available first to Google AI Ultra subscribers. Google AI Ultra is a paid tier — pricing hasn't been officially locked in for the Spark era, but current Ultra plans run around $249/month. There's no confirmation of a free tier or trial period for Spark specifically.
When Can I Use Gemini Spark?
Google said Ultra subscribers will get access "next week" from the May 19, 2026 announcement — so expect access around late May 2026. Broader availability hasn't been announced. If you want early access, signing up for Google AI Ultra is currently the only path.
Gemini Spark vs ChatGPT Agent — What's the Actual Difference?
The biggest structural difference is execution persistence. ChatGPT's agent features (including Operator) require an active session — close your browser and the task stops. Gemini Spark runs on Google Cloud VMs, meaning it continues working after you log off. Additionally, Spark has native access to Gmail, Docs, Sheets, and Slides without requiring external plugin connections, whereas ChatGPT relies on its plugin and tools store for equivalent access. Whether this matters in practice depends entirely on whether Spark's task completion is reliable — which we won't know until real users test it at scale.
I'll keep this post updated as Spark access opens up and I can run actual tests.
If you want to see how I evaluate other AI agents and tools — including ones I've spent months using daily — the AI agent architecture guide is a good starting point for understanding the underlying models, and I have a full breakdown of Microsoft's agent framework and Mastra AI if you want current alternatives while waiting for Spark.
About the author: Jim Liu is a Sydney-based solo founder who runs a network of AI tools review sites, including openaitoolshub.org. He has been testing AI productivity tools daily since 2023, with a focus on agentic AI systems and how they actually perform for solo founders and small operators. More about Jim →