The Best AI Incident Management Tools in 2026: Whose Model, Where Your Data Goes, and What It Costs

If you’ve spent any time shopping for AI incident management tools lately, then you’ve probably noticed something strange: every vendor claims to be “AI-powered,” but nobody seems to agree on what that actually means. You may even understand that moment when someone finally asks the question nobody wanted to say out loud: the Slack channel suddenly looks like a wall of half-thought updates, when someone pauses, looks at the AI-generated summary and asks “Wait, where is all this data going?”

The channel goes quiet, a few people stare blankly at the screen. Someone else scrolls through the vendor’s documentation like they’re searching for Poseidon’s trident, while another person mutters that they thought the AI was run locally. But wasn’t it the vendor’s model? Or were we all too busy trying to restart a failing service to pay attention?

That moment has been happening a lot lately and it’s not because teams became paranoid all of a sudden, but because AI quietly slipped through the back door into incident management without anyone stopping to ask what that meant. Before long, tools were generating summaries, suggesting next steps, calling themselves “AI-powered” and getting dangerously close to growing legs and walking around on two feet. Everyone nodded along until they realized they didn’t actually know whose model was running or where their incident data was being processed.

And once that realization hits, you can’t unsee it. Now you’re noticing how vague some vendors are about their AI, that pricing pages never mention it and how model choice and data flow are conveniently left out of comparison lists. And you start asking questions that seem obvious but somehow aren’t answered anywhere. Well, you’re not alone.

You’re here because you’ve hit the “stop assuming and start checking” wall. You want clarity instead of marketing, to know exactly whose model runs, where your incident data goes and whether AI is quietly adding zeroes onto your budget. (If you want the short version of where we stand, here’s our take on AI incident management).

Now that we’re on the same page, let’s get into it.

The Three Questions Everyone is Asking

On every RFP, Slack channel and security review, these same three questions keep repeating:

1. Which incident management tools let me bring my own AI model instead of using the vendor’s?

   This is the question that separates the category into two camps: the tools that let your model run the show, and the ones that insist on running their own. “Bring your own model” can mean two things: the first is plugging your own model into the vendor’s AI, which Rootly allows on Enterprise with your own OpenAI, Anthropic or Gemini key. The second is letting your own agent operate the platform through an API or an MCP server, which PagerDuty, Rootly and All Quiet all support. The difference is what else is running; every other tool in this list also runs its own AI features on your incident data, whereas All Quiet doesn’t run any, so your model isn’t an extra when it’s the only AI in the loop.

2. Which on-call tool doesn’t send incident data to an AI vendor?

   Security teams love this question, so much so that they’ve started asking it before they even look at the feature list. If your policy forbids sending incident data to external AI vendors, your options narrow fast. All Quiet is the only tool in this list that runs no AI on your incident data at all, so there’s no AI vendor in the loop unless your own agent brings one. The other seven offer AI features that process incident data when you use them, usually through a model provider on their sub-processor list, which can be a sticky situation if your security team has strict boundaries.

3. Which incident management tools charge extra for AI features?

   AI is the new “surprise fee.” Some tools bundle AI into the platform, some meter it, some charge per seat or per summary, some don’t publish pricing at all. PagerDuty and Datadog publish AI pricing and charge for it separately; incident.io, FireHydrant and ilert put AI on higher plans, while Rootly prices its AI SRE on request. All Quiet doesn’t charge an AI fee because it doesn’t run AI, as your agent works through its API as part of the Pro plan.

For every price side by side, see our incident management pricing comparison.

The r/devops Thread That Started The Conversation

If you missed the “Just realized our AI-powered incident tool is literally just calling ChatGPT API” thread, here’s the short version.

A user discovered that their incident management tool was quietly sending incident data to an external AI vendor. Not maliciously or sneakily, just as part of how the tool worked. But nobody had told them.

The thread blew up because it revealed something very important: buyers weren’t as confused about AI as they thought they were. They were confused about what vendors meant by “AI-powered.” A few thought the tool just had clever automation but others assumed it used their own model. Then there were the stragglers who thought it meant nothing at all.

The thread made it very clear that buyers want to know exactly where their incident data goes, whose model runs and whether AI is a feature or an added fee.

Which is why we’re here 😀

The Comparison Table

Some cheat codes before reading this table so you don’t get lost:

• Vendor runs its own AI tells you whether the tool processes incident data with a model it operates.
• Your model key in the vendor’s AI is the narrower kind of bring-your-own-model: your key, still their feature.
• Your agent can operate it is the other kind. Your agent works through an API or an MCP server.
• AI providers named as sub-processors is the question that determines whether your security team will even let you evaluate the tool.
• Vendor AI can be switched off is whether you can stop that processing. How AI is priced sits in the deep dive below, which is the part vendors love to hide behind vague phrases like “intelligent automation included.”

Checked 29 October 2026, from each vendor’s own pages. Sources are listed at the end.

Tool-by-Tool Deep Dive

Let’s take a look at each vendor in a bit more detail.

1. All Quiet

All Quiet runs no AI of its own on your incident data. Your agent, with your model, your prompts and your infrastructure, operates All Quiet through scoped, audited API tokens, with full agentic capability, no AI add-on fee, and your data never leaving your control.

What the AI/API does

All Quiet is the odd one out in this list because it doesn’t actually run its own AI. Instead, it hands the steering wheel to your agent, letting your model operate the platform. It’s a strikingly different mental model from the usual “vendor sprinkles AI on top of a feature and calls it extra cheese.”

Your agent can open incidents, update fields, coordinate responders, write timelines, run workflows and basically do anything the API allows. It’s not a summary generator or a chatbot, but a full operator with a captain’s hat and a fancy suit that has the same powers a human would have, except it doesn’t get tired or distracted or accidentally paste the wrong link into Slack.

Whose model

Always yours. You pick the model, the prompts, the infrastructure, the guardrails, the logging, the curtains and the rugs; everything. All Quiet doesn’t try to insert its opinions or influence your choice. It doesn’t even offer a recommended model. It simply says: bring whatever you want, as long as it can talk to the API. And that’s exactly why regulated teams like it so much; they already have a model approved by security and they don’t want to go through another approval cycle just because an incident tool insists on running its own AI.

Data

Your incident data stays with All Quiet and the sub-processors listed in our DPA. None of them are AI providers, so the only AI that ever sees your incidents is the agent you bring, working through the scoped token you give it, running wherever you decide. And that’s exactly how most security teams like it.

Pricing

Since there’s no AI add-on, there’s no AI add-on fee. Agent access through the API is part of Pro at $9.99 per user per month in the US region, or €11.99 for EU-hosted accounts, because the price follows the hosting region. You pay for the platform and your model does the work. If you want the full breakdown, the pricing page goes into the nitty gritty details.

2. PagerDuty

PagerDuty is perhaps the most established name in this list and has leaned into AI in a way that feels very PagerDuty (if you’re familiar with them, you’ll know what I mean). It’s structured, predictable, clearly documented and firmly in the vendor-run camp.

What the AI does

PagerDuty’s AI focuses on summaries, triage and workflow automation. It’s designed to make incidents easier to manage without requiring you to bring your own model. If you’ve ever been in a messy incident where five people are typing at once and nobody is sure what actually happened, PagerDuty’s AI tries to clean that up by generating summaries and suggesting next steps. It basically sprays responders with a fire extinguisher to help them stay aligned and keep the incident from spiralling into chaos.

Whose model

PagerDuty runs AI on models from providers on its sub-processor list. You don’t bring yours. PagerDuty chose this life deliberately so AI could feel like part of the platform rather than an external dependency. For many teams, especially those who don’t want to manage their own model, this makes a lot of sense. PagerDuty also runs a hosted MCP server, so your own agent can work with PagerDuty alongside its AI.

Data

Incident data is processed by PagerDuty’s AI, which is the vendor-run model. It’s no secret either; PagerDuty is very clear about it. Its sub-processor list names OpenAI, Microsoft, Google, AWS and IBM for AI, and generative AI is opt-in. Nothing is used until you switch it on, and you can switch it off any time. If your security policy allows vendor-run AI, this is fine. But if your system is locked behind glass like the Mona Lisa, PagerDuty might slip off your shortlist.

Pricing

Unlike other vendors who would prefer their AI pricing to remain as mystical as a prophecy whispered by a forest druid who speaks in nothing but riddles, PagerDuty simply publishes what it costs and gets on with life.

PagerDuty Advance, its generative AI, starts at $415 per month on annual plans. AIOps, its machine-learning noise reduction, starts at $699 per month and is billed by consumption, so metering does come into the equation. And “consumption” means events: once AIOps is switched on for a service, every event PagerDuty accepts counts toward the bill. That means every alert trigger, every repeat of the same alert, every resolve and even events that PagerDuty suppresses, drops or receives during a maintenance window.

PagerDuty doesn’t publish its overage rate, though. On the PD Reliability Platform, the same events come out of a yearly allowance ($40,000 a year on Essential), and anything above it is bought as credits whose prices aren’t published either. Without AIOps, events on Professional aren’t billed.

Compare All Quiet vs PagerDuty →

3. incident.io

incident.io has built a reputation for polished workflows and a very friendly user experience. Their AI follows the same philosophy, thankfully.

What the AI does

incident.io uses AI for summaries, updates and workflow assistance. It’s tightly integrated into the platform, so it has a natural feel to it. If you’ve ever used the platform, you know they care about making things feel smooth, and their AI is part of that. It tries to reduce the cognitive load on responders by keeping things tidy and up to date.

Whose model

incident.io runs the AI on models from OpenAI, Anthropic and Google, making it vendor-run. No bring-your-own-model option on its pricing page. They control the experience end to end. incident.io also runs a hosted MCP server on Team, Pro and Enterprise, so your own agent can work with your incidents too.

Data

Incident data is sent to incident.io’s AI, which is the vendor-run model. Those three providers are on its sub-processor list and you can ask incident.io’s support to switch all AI off, which stops data going to them. If your policy allows it, great. If not, ask them to switch it off or move on.

Pricing

AI pricing isn’t published but their pricing page shows that it’s included in the Pro tier and above. That doesn’t necessarily mean they don’t have an add-on for lower tiers but you’d have to speak to them to find out.

Compare All Quiet vs incident.io →

4. Rootly

Rootly is one of the earliest incident management platforms to add AI and it’s one of the few that let you bring your own model key (hence why it gets a closer look here).

What the AI does

Rootly’s AI focuses on summaries, timelines and workflow automation, like other tools. It tries to make incidents easier to follow by keeping the narrative clean and responders aligned. Similar enough to incident.io for everyday use, and its separate AI SRE product adds autonomous investigation agents on top.

Whose model

Vendor-run with a twist: on Enterprise, Rootly lets you plug in your own OpenAI, Anthropic or Gemini key, so the model is yours even though Rootly’s AI still does the work. It also runs its own MCP server for outside agents.

Data

Incident data is processed by Rootly’s AI, which is the vendor-run model. Its sub-processor list names OpenAI and Anthropic for AI. If your policy says no to external AI vendors getting your incident data, then you’ll want Rootly’s AI switched off for that team, which its settings allow per team and per feature.

Pricing

AI summaries, scribe and retrospectives come with Incident Response. AI SRE is a separate product, priced on request, and Enterprise includes it along with the bring-your-own-key option. So yes, AI add-ons are a thing.

Compare All Quiet vs Rootly →

5. FireHydrant

Always with a strong engineering audience, FireHydrant is practical, functional and focused on making incidents less painful.

What the AI does

Summaries, updates, incident coordination; all the good stuff. It tries to reduce the amount of manual typing during incidents, so your responders can at least have a few minutes of cognitive rest between tasks. It’s nothing fancy, it’s just helpful.

Whose model

Vendor-run on models from OpenAI and Anthropic.

Data

Sent to FireHydrant’s AI, which is, again, the vendor-run model. Both providers are on its sub-processor list. FireHydrant’s pages don’t say whether AI can be switched off, so ask before you sign.

Pricing

Included in the Enterprise plan but not entirely clear whether it can be added onto other plans. FireHydrant has been part of Freshworks since January 2026 and Freshworks is integrating it into its platform alongside Freshservice.

Compare All Quiet vs FireHydrant →

6. Datadog

Datadog is the biggest platform on this list and its AI reflects that.

What the AI does

Datadog’s AI spans observability, alerts, incidents and automation. It’s part of a huge system that tries to make sense of everything happening in your infrastructure, making it much more than just an incident tool. During incidents, it generates summaries, suggests next steps and helps responders understand what’s going on so they don’t dive head-first into their coffee machines with nothing but a mug and a dream.

Whose model

Datadog runs the AI on models from providers such as OpenAI and Anthropic, making it very vendor-run. Datadog also has a generally available MCP server, though it gives agents logs, metrics and traces rather than incidents.

Data

Processed by Datadog’s AI. Seven AI providers are on its sub-processor list, OpenAI and Anthropic among them, and admins can switch AI off for the whole organization per role, or cap its spend.

Pricing

Datadog sells AI credits at $500 per 500 credits a month billed annually or $1.30 per credit on demand. An autonomous investigation uses about 4 credits, so it’s nice and clear, but it’s metered and unused credits don’t roll over.

If Datadog is already your monitoring tool, here’s how incident management with Datadog works.

7. Grafana IRM

Grafana IRM is the incident management part of the Grafana ecosystem. Their AI is still evolving but the fundamentals are pretty clear.

What the AI does

Grafana IRM uses AI for summaries and workflow assistance to keep responders on the same page and lessen the manual load.

Whose model

Vendor-run: Grafana Assistant uses Anthropic’s Claude models through Anthropic, Google Vertex AI or Amazon Bedrock depending on your region, and you can’t choose the provider.

Data

Sent to Grafana’s AI. OpenAI, Anthropic, Google and AWS are on its sub-processor list for optional AI features. Nothing is sent until someone uses the Assistant, and an admin can switch it off.

Pricing

Grafana lists AI investigations and its Assistant without a separate price. Grafana also has an MCP server that covers incidents and OnCall, so your own agent can work with it too.

Compare All Quiet vs Grafana IRM →

8. ilert

ilert is the quiet guy in the room, standing alone at the work party snack table with its Christmas hat on and a mismatched black cloak. AI exists, and there’s a lot more of it than the quiet act suggests. They’re mysterious like that.

What the AI does

ilert uses AI for summaries and automation, like most other tools. It also has an AI SRE that runs investigations and an AI voice agent for incoming calls. It’s not trying to reinvent anything, just to be helpful and make incidents less painful.

Whose model

Vendor-run: Anthropic models via AWS Bedrock for EU customers, OpenAI and Anthropic elsewhere, according to ilert’s AI documentation.

Data

Processed by ilert’s AI. ilert’s sub-processor list names X.AI for AI features rather than the providers in its AI documentation, so it’s worth asking which applies to you. AI can be switched off globally in account settings and ilert runs a hosted MCP server for outside agents.

Pricing

AI is included in the Pro plan and above, with a credit system that varies between plans. That’s 250 credits a month on Pro, 1,000 on Scale and 5,000 on Enterprise, and ilert’s pricing page notes that AI credits are still in closed beta.

Compare All Quiet vs ilert →

Why Model Choice is Becoming the Real Differentiator

A few years ago, vendors competed on features. Who had the best timeline? Who had the smoothest Slack integration? Who had the prettiest incident page? But then AI descended from the sky like Aladdin on a magic carpet and now everyone has summaries and automation and some kind of “intelligent” workflow.

So the question shifted. It’s no longer about who has the best AI features but whose AI touches your data and whether you can turn it off. If your policy doesn’t allow incident data to be sent to external AI vendors, you need a tool where you can switch the vendor’s AI off completely (which most tools in this list allow) or one that runs no AI of its own on your data to switch off in the first place, which is All Quiet.

But if what you’re looking for is full agentic automation, you need a platform your agent can operate end to end. They might even let you bring beer too.

Security, Data Control and the New AI Boundary

Security teams used to care mostly about access control and audit logs but now it’s about where your incident data goes once AI enters the chat. They want to know if the AI vendor is inside your SOC2 boundary or if incident data leaves your region. They want to know whether summaries are generated locally or externally.

Vendor-run means your incident data is processed by the vendor’s AI on a model the vendor picks. Agent-operated means your own agent and model do the work and the AI step happens wherever you run your agent. Many tools now offer both; the boundary is whether the vendor’s AI is on your data at all.

Agentic operation wraps this conundrum up like a neat little birthday present. Your model does the work, your agent only gets the access you scope for it and your policies stay intact.

How to Choose the Right Model for Your Team

There’s a very simple way to decide this.

If your security policy says no to sending incident data to external AI vendors, you need a tool that runs no vendor AI on your data or lets you switch it off completely.

If you want convenience and don’t mind vendor-run AI, you can choose any of the vendor-run tools that fit your budget and needs.

If you want full agentic automation, go with a tool that lets your model operate the system.

If you want predictable pricing, avoid tools that meter AI usage.

And if you want to make the choice easier, cheaper and less painful, get in touch.

Here’s how All Quiet approaches AI incident management.

FAQs

Which incident management tools let me bring my own AI model instead of using the vendor’s?

It depends on what you mean. Rootly lets Enterprise customers plug their own OpenAI, Anthropic or Gemini key into Rootly’s AI. Every tool in this list lets your own agent operate it through an MCP server or an API. All Quiet is the only one that runs no AI of its own, so your model is the only AI touching your incident data. If your security team insists on controlling the model, the prompts, the infrastructure and the data boundary, that’s a very short shortlist. All Quiet’s approach is unusual because your agent operates the platform directly, which means the model you already trust becomes the one doing the work.

Which on-call tool doesn’t send incident data to an AI vendor?

All Quiet again. Your incident data stays with All Quiet and the sub-processors listed in its DPA and not one of them is an AI provider, so the only AI that ever sees your incidents is the one you bring. The other tools in this list offer AI features that process incident data when you use them, through AI providers named on their sub-processor lists, and most let you switch those features off. This isn’t inherently bad but it’s a clear dividing line. If your policy forbids external AI vendors, the list shrinks fast.

Which incident management tools charge extra for AI features?

PagerDuty sells PagerDuty Advance from $415 per month on annual plans and AIOps from $699 per month, billed per event; Datadog sells AI credits at $500 per 500 credits a month. incident.io, FireHydrant and ilert put AI on higher plans and Rootly prices its AI SRE on request. All Quiet doesn’t charge an AI fee because it doesn’t run AI. Published prices make PagerDuty and Datadog the easiest to budget for, but both meter usage, so a busy month costs more.

What’s the difference between vendor-run AI and bring-your-own-model AI?

Vendor-run AI means the platform uses its own model to generate summaries, triage incidents or automate workflows. Your incident data is sent to the vendor’s AI system because that’s how the model works. Bring-your-own-model means your model does the work, your agent operates the platform and no vendor AI touches your incident data. Vendor-run AI is convenient. Bring-your-own-model AI is controlled. The right choice depends on your security policy, not your taste in features.

Why does model choice matter so much in 2026?

Because AI is no longer a novelty; it’s a core part of incident response. Once AI starts touching incident data, security teams want to know exactly whose model is running, where the data goes and whether the model is inside or outside their compliance boundary. Features are converging across vendors but model choice is not. That’s why buyers are suddenly asking questions that comparison lists never used to cover.

Do any tools let me use both vendor-run AI and my own model?

Yes, most of them. Rootly lets you run its AI on your own model key on Enterprise and every vendor-run tool in this list also offers an API or MCP server, so your own agent can work alongside their AI. All Quiet takes the other route: it runs no AI of its own, so your agent is the only one.

Is vendor-run AI unsafe or risky?

Not necessarily. It depends entirely on your policy. Many teams are perfectly comfortable with vendor-run AI as long as the vendor is transparent about how data is processed. PagerDuty and Datadog are clear about their AI systems and pricing. The challenge is when vendors don’t publish details, which makes it harder for buyers to evaluate risk. The model itself isn’t the issue, just the clarity.

Why don’t some vendors publish their AI pricing?

Some bundle AI into the platform. Some treat it as an experimental feature. Some simply haven’t decided how to price it yet. And some, to be honest, seem to prefer their AI pricing to remain mystical and ethereal. Published pricing is easier for buyers but not every vendor has embraced that level of transparency.

Does bring-your-own-model AI mean I have to manage everything myself?

You manage the model, not the platform. All Quiet handles the incident management system. Your agent handles the automation. You choose the model, the prompts and the infrastructure. This is appealing to teams that already have an approved model and don’t want to add another one to their compliance list.

Is agentic incident management actually useful or is it just a buzzword?

It’s useful if you want automation that goes beyond summaries. An agent can open incidents, update fields, coordinate responders, run workflows and handle repetitive tasks without waiting for a human to click a button. It’s not a chatbot but an operator. Find out more on how it works in our glossary entry on agentic incident management.

How do I choose the right model for my team?

Start with your security policy. If your policy forbids sending incident data to external AI vendors, you need a tool with no vendor AI on your data, or one where you can switch it off completely. If your policy allows vendor-run AI and you prefer convenience, you can choose any of the vendor-run tools. If you want published AI pricing, look at PagerDuty and Datadog, but both meter usage. If you want full agentic automation, All Quiet is built for it.

Sources Checked

• All Quiet pricing, All Quiet, checked 29 October 2026
• Data Processing Agreement, Annex 3, All Quiet, checked 29 October 2026
• PagerDuty pricing, PagerDuty, checked 29 October 2026
• AIOps and PagerDuty Advance pricing, PagerDuty, checked 29 October 2026
• AIOps documentation, PagerDuty, checked 29 October 2026
• Billing, invoices and payments, PagerDuty, checked 29 October 2026
• Product-specific terms, PagerDuty, checked 29 October 2026
• PagerDuty subprocessors, PagerDuty, checked 29 October 2026
• Generative AI guidelines, PagerDuty, checked 29 October 2026
• Release notes (remote MCP server), PagerDuty, checked 29 October 2026
• incident.io pricing, incident.io, checked 29 October 2026
• AI data handling, incident.io, checked 29 October 2026
• Remote MCP server, incident.io, checked 29 October 2026
• Rootly pricing, Rootly, checked 29 October 2026
• Subprocessors, Rootly, checked 29 October 2026
• AI settings, Rootly, checked 29 October 2026
• Introducing the Rootly MCP Server, Rootly, checked 29 October 2026
• FireHydrant pricing, FireHydrant, checked 29 October 2026
• Subprocessors, FireHydrant, checked 29 October 2026
• Delivering the future of ServiceOps today, Freshworks, checked 29 October 2026
• Datadog pricing, Datadog, checked 29 October 2026
• Subprocessors, Datadog, checked 29 October 2026
• AI Credits, Datadog, checked 29 October 2026
• Datadog MCP Server documentation, checked 29 October 2026
• Grafana IRM, Grafana Labs, checked 29 October 2026
• List of subprocessors, Grafana Labs, checked 29 October 2026
• Grafana Assistant privacy and security, Grafana Labs, checked 29 October 2026
• Grafana MCP servers, Grafana Labs, checked 29 October 2026
• ilert pricing, ilert, checked 29 October 2026
• Sub-processor list, ilert, checked 29 October 2026
• AI and agents overview, ilert, checked 29 October 2026
• ilert MCP server (hosted), ilert, checked 29 October 2026
• Just realized our AI-powered incident tool is literally just calling ChatGPT API, r/devops, 29 October 2026