Build with Gemini 2.0 Flash, 2.5 Pro, and Gemma using the Gemini API and Google AI Studio.
Users generally praise Google AI for its robust and versatile capabilities, particularly highlighting the intelligent and rapid processing power of models like Gemini 3.1 Flash. The main strengths lie in innovation and integration with popular tools like Firebase, improving workflow and productivity. However, some users express concerns over the pricing structure, especially for top-tier subscriptions like Google AI Ultra, which costs $249.99. Overall, the reputation of Google AI remains strong, noted for cutting-edge technology and comprehensive support for developers and businesses.
Mentions (30d)
70
2 this week
Avg Rating
4.2
20 reviews
Platforms
7
Sentiment
10%
23 positive
Users generally praise Google AI for its robust and versatile capabilities, particularly highlighting the intelligent and rapid processing power of models like Gemini 3.1 Flash. The main strengths lie in innovation and integration with popular tools like Firebase, improving workflow and productivity. However, some users express concerns over the pricing structure, especially for top-tier subscriptions like Google AI Ultra, which costs $249.99. Overall, the reputation of Google AI remains strong, noted for cutting-edge technology and comprehensive support for developers and businesses.
Features
Use Cases
Industry
information technology & services
We’re launching a brand new, full-stack vibe coding experience in @GoogleAIStudio, made possible by integrations with the @Antigravity coding agent and @Firebase backends. This unlocks: — Full-stack
We’re launching a brand new, full-stack vibe coding experience in @GoogleAIStudio, made possible by integrations with the @Antigravity coding agent and @Firebase backends. This unlocks: — Full-stack multiplayer experiences: Create complex, multiplayer apps with fully-featured UIs and backends directly within AI Studio — Connection to real-world services: Build applications that connect to live data sources, databases, or payment processors and the Antigravity agent will securely store your API credentials for you — A smarter agent that works even when you don't: By maintaining a deeper understanding of your project structure and chat history, the agent can execute multi-step code edits from simpler prompts. It also remembers where you left off and completes your tasks while you’re away, so you can seamlessly resume your builds from anywhere — Configuration of database connections and authentication flows: Add Firebase integration to provision Cloud Firestore for databases and Firebase authentication for secure sign-in This demo displays what can be built in the new vibe coding experience in AI Studio. Geoseeker is a full-stack application that manages real-time multiplayer states, compass-based logic, and an external API integration with @GoogleMaps 🕹️
View original| Model | Input / 1M tokens | Output / 1M tokens |
|---|---|---|
| gemini-2.5-pro | $1.25 | $10.00 |
| gemini-2.0-flash | $0.10 | $0.40 |
| gemini-2.0-pro | $1.25 | $5.00 |
| gemini-1.5-pro | $1.25 | $5.00 |
| gemini-1.5-flash | $0.07 | $0.30 |
Light
1M tokens/mo
$0.16 – $5
gemini-1.5-flash → gemini-2.5-pro
Growth
50M tokens/mo
$8 – $238
gemini-1.5-flash → gemini-2.5-pro
Scale
500M tokens/mo
$83 – $2,375
gemini-1.5-flash → gemini-2.5-pro
Estimates assume 60/40 input/output ratio. Actual costs vary by usage pattern.
g2
What do you like best about Vertex AI?I use Vertex AI for content creation, improving workflows, and RAG purposes. It significantly cuts down the time spent on research and allows me to tailor output and formatting, which saves even more time. In terms of workflows, it helps produce copy at a faster rate and capacity while maintaining good quality, allowing us to scale. I love that Vertex AI is an enterprise solution with safety and compliance features. It's a great all-in-one tool for enterprises, capable of RAG, generative text/video/images, building agents, etc. It's just a nice playground to have access to for creating tools, and it's enabled my team and me to do things that were previously not possible. The access to generative AI with Google Search grounding and System Instructions customization is super advantageous, allowing my team to scale production of marketing copy effectively. Review collected by and hosted on G2.com.What do you dislike about Vertex AI?The UI is quite bloated. There are features that could be advertised better (or those that are in preview) like the AI Agent Builder. Depending on the user role, it could be better to adjust the UI to be more accessible and simple, perhaps by renaming some categories and features, including some documentation on the pages themselves. Review collected by and hosted on G2.com.
What do you like best about Vertex AI?I find using Vertex AI to be fun, which is an unexpected perk. The pricing is kind of affordable, making it a much more reliable option for me. I also think the reasoning behind its pricing is really good. Setting it up is quite easy, so that’s another strong point. Review collected by and hosted on G2.com.What do you dislike about Vertex AI?I think the vulnerability in experiments could be improved. It's something that really needs attention. Also, the SSS vulnerability needs improvement. Review collected by and hosted on G2.com.
What do you like best about Vertex AI?I use Vertex AI to build and run machine learning models, and I find it very helpful because it lets me work with data, train models, and make predictions all in one place without needing to set up everything myself. I love that I can try different models and compare results easily, which helps me understand what works best without a lot of manual effort. The AutoML feature is great too, guiding me through the steps, making the process easier even though I'm not a machine learning expert. I also appreciate how well Vertex AI integrates with other Google Cloud services, allowing me to use my data directly without moving it around, which saves me effort and keeps my work simple. This all makes my workflow faster, simpler, and more organized. Review collected by and hosted on G2.com.What do you dislike about Vertex AI?One thing that could be better is how easy it is to learn at the beginning. It can feel confusing if you are new and some steps are not very clear. Another issue is that it can be hard to understand the pricing. Costs can increase quickly if you are not careful and it is not always easy to track spending. Sometimes, when something goes wrong, it is also difficult to find the exact problem. Better error messages or guidance would help a lot. Review collected by and hosted on G2.com.
What do you like best about Vertex AI?it functions as a "powerful command center" for testing models and exposing endpoints, which helps streamline production grade software deployment. Review collected by and hosted on G2.com.What do you dislike about Vertex AI?Vertex AI for its steep learning curve and overwhelming complexity, particularly around setup, permissions, and resource management and unexpected high costs due to opaque pay-as-you-go billing and lack of clear warnings during free trials Review collected by and hosted on G2.com.
What do you like best about Vertex AI?I appreciate that Vertex AI helped us extract relevant points faster from documents, turning unstructured information into something we could easily present and share with stakeholders. I love the documentation and how it enabled us to quickly test different approaches from design to practical implementation, building the whole machine learning stack ourselves. Trying different models was also a plus due to its speed. The initial setup was very easy and straightforward, which made it convenient to start using quickly. Review collected by and hosted on G2.com.What do you dislike about Vertex AI?I guess the cost transparency while experimenting with different models and workflows. To be honest, understanding the cost part and where to put limits was a bit tiresome because we were afraid of doing something wrong and no hard stop on spending amount. Review collected by and hosted on G2.com.
What do you like best about Vertex AI?I like that Vertex AI automates a lot of the setup, making it easier to experiment with different models and turn them into APIs quickly. I appreciate how it orchestrates the models and deploys them as services, allowing easy integration into our app. It handles processing and analyzing large amounts of product data without needing to build ML infrastructure from scratch. Additionally, the integration with OCR tools for automatically flagging risky additives is a huge plus. It integrates easily with the rest of the Google Cloud ecosystem, making it simple to connect data, models, and scaffold real projects quickly. The initial setup was quite easy, which was beneficial. Review collected by and hosted on G2.com.What do you dislike about Vertex AI?I think Vertex AI could improve by providing better cost transparency and implementing safeguards to prevent overspending. I had to spend extra time reviewing the cost structure to ensure it stayed within safe limits. It would be helpful to have hard stops when the budget is hit or options for pre-paid budgets. Review collected by and hosted on G2.com.
What do you like best about Vertex AI?the usage of multimodality and agentic coding Review collected by and hosted on G2.com.What do you dislike about Vertex AI?I dislike the high costs, a steep learning curve, and complex, non-intuitive workflows Review collected by and hosted on G2.com.
What do you like best about Vertex AI?I like that Vertex AI brings the whole ML workflow into one platform and integrates well with Google Cloud services. It also saves time by handling infrastructures and scaling automatically. I also like how easy it is to deploy models and manage them through APIs. The platform is flexible and works well for both experimentation and production workloads. Review collected by and hosted on G2.com.What do you dislike about Vertex AI?One area that could be improved is the learning curve for new users, especially when configuring services in Google Cloud. Pricing and documentation could also be clearer for beginners. Review collected by and hosted on G2.com.
What do you like best about Vertex AI?The reliability that is offered by Vertex Ai is amazing Review collected by and hosted on G2.com.What do you dislike about Vertex AI?Well, to be frank, there’s really nothing to dislike. Review collected by and hosted on G2.com.
What do you like best about Vertex AI?Vertex AI Studio is easy to use, and the code output is downloaded for further development. Review collected by and hosted on G2.com.What do you dislike about Vertex AI?The complexity is high. I can access the product, but there’s no clear way to understand it because there isn’t an explanation of the code behind it. A README file would really help, and some visualization of how things work or how the different parts fit together is needed. Review collected by and hosted on G2.com.
Former Google CEO Eric Schmidt, Big Machine Records CEO Scott Borchetta & Tavistock VP Gloria Caulfield were all booed at commencement speeches, as AI backlash is now hitting campus stages🇺🇸
submitted by /u/Democrat_maui [link] [comments]
View originalServerless alternatives to OpenAI's end-of-life'd fine tuning
Does anyone have a decent alternative to OpenAI's fine tuning service they would recommend? I am looking for something that works in a serverless model and doesn't require dedicated hardware. The only real alternative I've found is Google's Gemini, but it only works with their older models. submitted by /u/dandiephouse [link] [comments]
View originalOpenAI bidding on the search term 'claude down' on Google Search
submitted by /u/jedsk [link] [comments]
View originalRace to create ASI
submitted by /u/KeanuRave100 [link] [comments]
View originalDay 5 of an open experiment: can a vibe-coded app find users with zero ads? Following along live
Starting an open experiment with this community. Want people to follow it live, not see a polished case study. The setup 100% AI-generated — no human code, no setup Zero ads, zero budget, zero growth tricks Posting real Google Search Console screenshots from day one Today is day 5 What it is A free CRM for freelancers and Ukrainian sole proprietors — time tracker, income/expense ledger, PDF acts of completed work, tax deadline reminders. Free to use, no card, no time limits. How Claude built it Claude wrote the entire stack — frontend, backend, DB schema, PDF generator, email flows, deploy config. I described the user, the pain, the constraints. Claude proposed schema, generated code, fixed its own bugs through iteration. I never opened the editor to write code by hand. Why public Most "built with Claude" stories show up after the product wins. I want to share the boring middle — impressions, clicks, what queries Google actually sends, what dies, what surprises. Screenshots GSC https://preview.redd.it/wvotfm2st92h1.png?width=3022&format=png&auto=webp&s=3f85d20f32f368cdef02ec860543fe6a6fd995aa What I'm tracking week over week Impressions, clicks, indexed pages, signups. I'll come back with the same four screenshots every week so the line is honest — up, flat, or down. The product: https://minteo.app/ If you want to follow the experiment, save the post — I'll reply here with weekly updates. submitted by /u/GroundOk3521 [link] [comments]
View originalGoogle I/O 2026 confirms AI companies are creating their own bubble narrative
People do not believe AI is a bubble because they are too dumb to understand the technology. They believe it because AI companies keep selling it like a bubble. That is the problem. AI companies talk like they are building the next layer of civilization, but behave like they are shipping unstable SaaS experiments: products that get renamed, nerfed, rate-limited, deprecated, or replaced before users can trust them. Google I/O 2026 felt like the latest example. Google should be one of the dominant AI players. It has the talent, infrastructure, data, research history, and money. But Google has a product trust problem. Same cycle over and over: launch something flashy, ship it incomplete, fail to support it properly, let it rot, then replace it with a new name or new app that does something similar. A rebrand is not maintenance. A revamped name is not reliability. A new AntiGravity installer is not a commitment. And this is not just Google. It is the whole AI industry. Companies keep pushing demos, gamed benchmarks, branding, rate-limit games, vague tiers, and quiet model changes. Users notice when quality drops, latency changes, limits tighten, or a product suddenly behaves differently. In serious business or engineering contexts, suppliers are expected to provide stability: clear terms, reliable service, predictable limits, maintained products, transparent pricing, and long-term availability. A small slip in that sense, and you start losing clients and your reputation sinks you. Trust does not come from another theatrical demo. It comes from commitment. Give people a product, a model, stable limits, a clear price, and a promise that it will keep working. Support it. Maintain it. Document changes. Stop silently swapping the engine and pretending nothing happened. I am not anti-AI. I think the technology is real and useful. That is why this is so frustrating. The industry is creating its own bubble narrative: overpromise, underdeliver, rename, repackage, change terms, and expect everyone to keep believing. People are not being irrational, and AI labs deserve this. Maybe they think AI is a bubble because AI companies keep acting like it is one. AI does not need more magic tricks. It needs reliability, transparency, support, and product discipline. submitted by /u/hatekhyr [link] [comments]
View originalGoogle wants Gemini AI on your face so it can sell you more ads later
submitted by /u/Electrical-Title3978 [link] [comments]
View originalI built a Chrome extension that gives your AI coding tools a memory layer - took 3 months, Claude helped me ship it.
I built Herb • - a productivity layer that sits on top of your AI coding tools. Honestly, probably 60% of the actual coding happened in Claude. I'd describe the feature, Claude would write the logic, I'd test it, break it, come back and fix it. That loop for 3 months. It's a weird kind of collaboration but it works. You know how every time you open a new Claude or ChatGPT chat, it has no idea who you are? You have to explain yourself every single time. "I'm using Next.js, TypeScript, Tailwind, here's what I'm building, here's how I like my code structured..." - same thing, every session, every tool. Herb • fixes that. You write it once. Every new chat remembers it. That's the core. What Herb does: Context Injection - set up a profile once (stack, preferences, current goals). Inject it into any AI chat in one click. No retyping your setup every session. Rules Library - save your .cursorrules and prompting patterns. Tag, search, copy in one click. Session History - save AI conversations with a button that appears on Claude and ChatGPT. Reference them later. Projects - group rules and sessions by project across tools. Prompt Templates - reusable templates with variables like {{language}} or {{error_message}}. Fill and fire. Community Rules - shared library of production rules anyone can import. Next.js, FastAPI, React TypeScript, Tailwind, Node/Express. You can contribute yours too. It's free. And I would genuinely love honest feedback after using the tool. Herb • Chrome Extension submitted by /u/Opening-Fun-7280 [link] [comments]
View originalAnthropic Announced vs current compute capacity (Sources Below)
source list: Google Cloud TPU deal — up to 1M TPUs, “well over 1 GW” expected online in 2026 https://www.anthropic.com/news/expanding-our-use-of-google-cloud-tpus-and-services https://www.googlecloudpresscorner.com/2025-10-23-Anthropic-to-Expand-Use-of-Google-Cloud-TPUs-and-Services (Anthropic) Fluidstack / Anthropic $50B U.S. AI infrastructure — Texas + New York, sites coming online through 2026 https://www.anthropic.com/news/anthropic-invests-50-billion-in-american-ai-infrastructure https://www.fluidstack.io/about-us/blog/fluidstack-selected-by-anthropic-to-deliver-custom-data-centers-in-the-us (Anthropic) Microsoft + NVIDIA deal — $30B Azure compute commitment + up to 1 GW additional capacity https://blogs.microsoft.com/blog/2025/11/18/microsoft-nvidia-and-anthropic-announce-strategic-partnerships/ https://blogs.nvidia.com/blog/microsoft-nvidia-anthropic-announce-partnership/ (The Official Microsoft Blog) Google + Broadcom next-gen TPU deal — multiple GW starting 2027; Broadcom SEC filing says ~3.5 GW https://www.anthropic.com/news/google-broadcom-partnership-compute https://investors.broadcom.com/static-files/c906d370-921b-4bc2-bb7b-57877dfcf1ae (Anthropic) Amazon / AWS deal — up to 5 GW, nearly 1 GW by end-2026 https://www.anthropic.com/news/anthropic-amazon-compute (Anthropic) AWS Project Rainier — operational now, nearly half a million Trainium2 chips; Claude expected on 1M+ Trainium2 chips https://www.aboutamazon.com/news/aws/aws-project-rainier-ai-trainium-chips-compute-cluster (Amazon News) SpaceX / Colossus 1 — all Colossus 1 compute, >300 MW, 220k+ NVIDIA GPUs within the month https://www.anthropic.com/news/higher-limits-spacex https://x.ai/news/anthropic-compute-partnership (Anthropic) Independent reporting for SpaceX deal https://www.reuters.com/business/retail-consumer/anthropic-unveils-dreaming-feature-help-its-ai-agents-self-improve-2026-05-06/ (Reuters) submitted by /u/Business_Garden_7771 [link] [comments]
View originalI built and shipped my Android app with Claude as my coding partner
Hi all I wanted to share a small win. I recently built and published my Android app, Nearfolks, and Claude was a big part of the development process. Nearfolks is a private relationship notebook for remembering people better. It helps users save notes about people, organize them into circles, set reminders, and remember small personal details before meeting someone again. The product idea was simple: not every relationship tool needs to be a sales CRM. Some people just want a private place to remember friends, family, community members, clients, and people they care about. The app is privacy-first: - no account - no cloud - no tracking - offline-first - data stays on the user’s device The app has a free version, and the upgrade is a one-time optional purchase for unlimited people, extra themes, and backups. No subscription. Claude helped me a lot with the build process: planning features, improving Flutter structure, debugging issues, writing cleaner code, thinking through edge cases, and getting unstuck during Play Console release problems. One release issue I faced was that closed testing worked fine, but production was blocked because of an older SQLCipher native dependency related to Android 16 KB memory page size support. Updating the dependency and rebuilding fixed it. What I found most useful about Claude was not just “write this code,” but using it like a patient technical partner: explaining errors, comparing approaches, and helping me move forward step by step. For people here who are building apps with Claude: - How do you structure your prompts for bigger projects? - Do you use Claude mainly for code generation, debugging, architecture, or product thinking? - Any tips for keeping an AI-assisted codebase clean as the project grows? Google Play: https://play.google.com/store/apps/details?id=com.nearfolks.notebook submitted by /u/shahzaib_sultan [link] [comments]
View originalThe American Rebellion Against AI Is Gaining Steam
The only thing growing faster than the artificial-intelligence industry may be Americans’ negative feelings about it, as former Google Chief Executive Eric Schmidt saw on Friday. Delivering a commencement address at the University of Arizona, Schmidt told students the “technological transformation” wrought by artificial intelligence will be “larger, faster, and more consequential than what came before.” Like some other graduation speakers mentioning AI, Schmidt was met with a chorus of boos. Ex-Google CEO Gets Booed While Discussing AI in Commencement Speech submitted by /u/chota-kaka [link] [comments]
View originalshipped my first chrome extension this week, came out of pure frustration tbh
been using AI tools nonstop for work and kept noticing my sessions would just... degrade. like the answers would get worse over time in the same chat and i had no idea why. turns out context windows are a thing and after a while the AI literally starts forgetting what you told it at the start so i spent a few weeks building something dumb and simple. it's just a little pill that floats on claude, chatgpt, gemini and perplexity and shows you a live quality score. fresh, warning, degraded. that's it. no backend, no login, nothing stored. just reads what's happening and tells you called it slate. it's free. https://chromewebstore.google.com/detail/dgkgpdchcpofkfhcfapmlljfigchfjjk?utm_source=item-share-cb https://preview.redd.it/nxkh6hanv32h1.png?width=1280&format=png&auto=webp&s=5a1588cb7283a8375c570a4633547b102850b5c5 submitted by /u/-HydrogeN [link] [comments]
View originalAnthropic just bought the company that generates most production MCP servers
Anthropic acquired Stainless on Monday for a reported $300M+. Most coverage is framing this as a developer tools acquisition. Stainless is best known for generating the official Python and Node SDKs that ship with OpenAI, Google, Meta, Cloudflare, and Anthropic. The SDK story is real. The MCP side is the part that matters here. Stainless was one of the first vendors to extend their compiler to produce MCP servers from the same OpenAPI specs that produce their SDKs. MCP hit ~97M monthly SDK downloads by December 2025 and around 10,000 production servers by early 2026. A lot of that production code was Stainless-generated. Anthropic now owns the dominant MCP server generator. What actually changed hands on Monday: The engineering team. Roughly 40-50 people including founder Alex Rattray, who previously built Stripe's patented SDK generation system. Now reporting to Katelyn Lesse in Anthropic's Platform Engineering org. The technology. The generator, the templates, the language-specific runtimes, the OpenAPI extensions Stainless invented for SDK-specific edge cases. The hosted product is winding down. New signups stopped Monday. New SDK and MCP server generations stopped Monday. Existing customers keep what they've already generated but the pipeline is closed. My read: this is closer to what Google did with Kubernetes than to a normal acquisition. Anthropic created MCP. Anthropic donated MCP to the Linux Foundation last December. Anthropic now owns the dominant implementation toolchain. The protocol is vendor-neutral on paper. The implementation toolchain isn't. Six months of Anthropic M&A starts looking less coincidental: December 2025: Bun, the JS runtime, pulled into Claude Code February 2026: Vercept, computer-use AI April 2026: Coefficient Bio, ~$400M healthcare AI May 2026: Stainless, SDK and MCP plumbing They're not buying training infrastructure or GPU clusters. They're buying the integration layers around the model. The bet seems to be that frontier models are converging faster than anyone expected, so the moat is everywhere except the model. If you're building on MCP today, tooling quality probably improves. Stainless's generator was already the cleanest in the space and the team that built it is now at Anthropic. Patterns will standardize faster as Stainless-derived templates become the de facto reference. The flip side is concentration risk. Cloudflare's MCP server framework, Pulse MCP, and the open-source generators Stainless released during the transition all become strategically important if you want any diversity in your stack. Sources: Anthropic announcement Why Anthropic actually did this, and migration math Curious whether Stainless ending up inside Anthropic reads as good news (better tooling) or concentration risk (one company owns the standard and the reference implementation) from your seat. submitted by /u/Ok-Constant6488 [link] [comments]
View originalClaude + Strava + Runna + Peloton
Built a personal running dashboard on top of Strava that's grown into something I actually use every day. Here's what it does: **Overview** — pulls all your Strava activities and gives you weekly/monthly mileage, pace trends, heart rate zones, and an AI coaching insight that reads your recent training and gives you an actual observation, not a generic tip. **Performance** — deeper analytics, PR tracking, long-term trend charts. **Fuel** — this is the part I'm most proud of. It syncs with my Runna training calendar via Google Calendar, classifies each day (rest / easy / moderate / hard / long run), and generates personalized daily macro targets that periodize automatically around your training load. Carbs scale hard with intensity — rest days are low, long run days are aggressive. It looks 14 days ahead so you can plan meals around what's coming. On rest days it integrates Peloton cross-training recommendations and adjusts your nutrition targets to reflect the actual workout load — a pull day gets different macros than a pure rest day. **Train** — a 7-day weekly view that lays out your Runna runs alongside recommended Peloton workouts for non-run days. The recommendations are periodization-aware: it won't put a leg day the day before your long run, favors upper body pull days after hard efforts, and always stacks a core add-on. On mobile, tapping the Peloton class opens it directly in the Peloton app. Built with Flask + Python on the backend, vanilla JS on the frontend, running locally on my home network. No cloud, no subscriptions — just a local server I hit from any device on my LAN. Happy to share any part of the code if anyone's interested. submitted by /u/albus_fulger [link] [comments]
View originalI thought AI articles could be generated with 2-3 prompts. I ended up building an 11-step workflow.
When I started this project, I honestly thought article generation would be simple: Give Claude a topic Ask for an outline Generate the article Done In reality... the output usually felt generic, repetitive, or structurally weak. So over time the workflow became much more complex. Right now the pipeline I built with Claude Code uses ~11 separate prompts/steps: topic planning search intent analysis outline generation competitor structure analysis section-by-section generation intro/conclusion generation content enhancement internal linking SEO cleanup image generation final formatting/export One thing that improved quality a lot: I stopped treating article generation as a single prompt. Generating sections independently with focused context produced MUCH better results than asking for a full article at once. Another big improvement: I started enriching prompts with external SEO/search data. Now the workflow also analyzes: Google search result structures competitor headings/topics related keyword data search intent patterns I use SEO APIs to feed that data into the prompts before generation. The result feels way less “AI fluffy” compared to my earlier versions. I’ve been testing it on my own websites (one blog for dog owners and website about tattoo) I publish content on two blogs and use this workflow regularly there. I’m actually pretty happy with the results I’m getting from it. OutscoreAgent Still experimenting a lot with workflows/agents, so I’d genuinely love feedback from people here using Claude for similar tasks. There’s a free tier (5 free articles + 14-day trial) if anyone wants to test it. submitted by /u/PlentyButterfly4462 [link] [comments]
View originalGoogle AI uses a tiered pricing model. Visit their website for current pricing details.
Google AI has an average rating of 4.2 out of 5 stars based on 20 reviews from G2, Capterra, and TrustRadius.
Key features include: Build with Gemini, Customize Gemma open models, Run on-device, Build responsibly, Integrate Google AI models with an API key, Integrate models into apps, Explore AI models, Own your AI with Gemma open models.
Google AI is commonly used for: Build with Gemini.
Google AI integrates with: Google Cloud Platform, Firebase, TensorFlow, Kubernetes, Chrome, Android, Web APIs, Google AI Studio, Gemini API, Gemma models.
Lenny Rachitsky
Founder at Lenny's Newsletter
3 mentions
Based on user reviews and social mentions, the most common pain points are: down, token usage, API costs, LLM costs.
Based on 237 social mentions analyzed, 10% of sentiment is positive, 87% neutral, and 3% negative.
