Google Gemini Gemini 3.1 Pro Preview lifecycle transition: preview → GA. 11h Google Gemini Gemini 3 Flash Preview lifecycle transition: preview → GA. 11h OpenAI gpt-3.5-turbo-16k: context window 16,000 → 20,000 tokens. 13h OpenAI GPT-3.5 Turbo removed function calling support. 13h Google Gemini Gemini Robotics-ER 1.5 Preview lifecycle transition: preview →… 13h Google Gemini gemini-3-1-flash-lite-preview lifecycle transition: preview → G… 13h OpenAI GPT-3.5 Turbo added function calling support. 19h Google Gemini Gemini 3.1 Flash Live Preview pricing observed change: $0.75/1M… 20h Google Gemini Gemini 3.1 Flash Image 🍌 pricing observed change: $0.5/1M in,… 20h Google Gemini Gemini 3.1 Flash Image 🍌 pricing observed change: $0.5/1M in,… 20h Google Gemini Gemini 3.1 Flash Live Preview pricing observed change: $3/1M in… 1d Google Gemini gemini-3-1-flash-lite-preview lifecycle transition: active → pr… 3d Google Gemini Gemini 3.1 Pro Preview lifecycle transition: active → preview. 3d Google Gemini gemini-3-1-flash-lite-preview lifecycle transition: preview → G… 3d Google Gemini Gemini 3.1 Pro Preview lifecycle transition: preview → GA. 4d Google Gemini Gemini 3.1 Flash-Lite pricing observed change: $0.5/1M in, $1.5… 5d
For agencies & fractional CTOs

Make AI monitoring a service you bill for.

You already keep an eye on the AI models your clients depend on — the LLMs behind their chat and agents, plus the image, audio, and video models they call from OpenAI, Anthropic, Google, and others. AI Stack Watch turns that quiet, unbilled work into a monitored workspace, an approved baseline, and a white-label brief you can forward as your own deliverable.

AI agencies Fractional CTOs Fractional Chief AI Officers Automation consultants Boutique dev shops
First — what we actually watch

The AI models your product runs on — and the companies behind them.

Modern software runs on AI models served by outside companies: the large language models (LLMs) behind chat and agents, the embedding models behind RAG and search, and the image, video, audio, and speech models behind everything else. Enginerds tracks those models — and the providers that ship them, including OpenAI, Anthropic, Google, Meta, Mistral, AWS, and Azure — so a change on their side never blindsides you.

💬
Chat, agents & copilotsLLMs like GPT, Claude, Gemini, Llama
🔎
RAG & searchembedding & reranking models
🖼️
Image generatione.g. DALL·E, Imagen, Stable Diffusion, Flux
🎬
Video generatione.g. Sora, Veo, Runway
🗣️
Audio & speechtext-to-speech, transcription, voice
👁️
Vision & multimodalmodels that read images & documents

What it isn't: not uptime monitoring, server metrics, or analytics for your own app. It's intelligence on the model providers you build on — the part of your stack you don't control and can't freeze. When they change pricing, capabilities, or retire a model, your product can break or your bill can jump. We catch it first.

The operator's reality

Monitoring is real work — and it's invisible to the client.

If you run AI in production for other people, you're already tracking provider changelogs, pricing pages, and deprecation notices in the background. It's genuine, skilled work — but because nothing breaks when you do it well, the client never sees it, and it never makes it onto an invoice.

The first time it does become visible is usually the worst time: a model hits end-of-life, a bill jumps, or an API behavior shifts — and you're explaining it after the fact.

The shift

Turn the watch into a deliverable.

AI Stack Watch gives that hidden work a shape your client can see: a per-client workspace, an approved synthetic baseline, and a source-cited brief on your cadence. Monitoring stops being a fire drill and becomes a proactive, billable touchpoint — you bring clients the heads-up before the provider does.

How it fits your practice

Three steps, then it runs in the background.

01
Map each client's stack
Group the providers and models behind each client pipeline into its own workspace, and pick the changes that matter.
02
Approve a synthetic baseline
Lock a synthetic, no-client-data baseline once — the reference every future model change is checked against.
03
Forward the brief
A white-label monthly brief on your cadence, plus a decision brief whenever something material changes.
What it does for the relationship

Look ahead of the curve — on the record.

  • Defend the retainer. A recurring, professional brief makes ongoing value visible, even in a quiet month.
  • Stay proactive. You raise model and pricing changes before the client notices them — or asks why the bill moved.
  • Earn trust with sources. Every change links to the provider's own page and the exact wording that triggered it. Nothing paraphrased.
  • Send it as your own. White-label the brief under your brand; the "Powered by Enginerds" line is configurable off on the Agency tier.
Built around how you work

Per-client by design.

A workspace per client

Every alert tells you exactly whose stack it touches — scoped to what each pipeline actually depends on, not a firehose of the whole market.

Decision-support, not decisions

We tell you what changed and what to evaluate against the approved baseline. The call — and the client relationship — stays yours.

No proprietary client data required

Validation baselines are synthetic and editable. We don't need your client's real prompts, private records, files, or API keys — AI Stack Watch monitors the providers' own public pages and evaluates changes against the approved synthetic baseline for the workspace. The result is decision-support, not production certification. Cancel anytime, self-serve.

Give every client stack a watch worth billing for.

Start with one client workspace, approve a baseline, and let the briefs do the talking.

Questions about plans? Read the pricing FAQ.