Adps AI
AI-native SRE platform whose specialised agents detect, diagnose, and remediate cloud, Kubernetes, and CI/CD incidents — including IT operations alerts by
If your on-call rotation is burning out and you run AWS and Kubernetes at scale, Adps AI is worth a serious look — its Kubernetes and CI/CD specialisation (Git Change Intelligence agents, HPA/VPA autoscaling, service-mesh routing) is narrower and deeper than general-purpose AIOps tools like Datadog or New Relic, which observe but don't act on your behalf. The problem is the opacity: the site's /pricing page sells a website-speed product (Starter free, Pro $24/month billed annually, Agency $169/month billed annually) and says nothing about SRE pricing. The MTTR-reduced-by-up-to-99% claim is vendor-supplied, not benchmarked. Bring a real past incident to the demo and make them replay it
Verified 3d ago · liveness 70/100 · cite: rightaichoice.com/tools/adps-ai
- SRE teams looking to cut on-call burden with autonomous incident response
- DevOps engineers managing large-scale Kubernetes clusters
- Platform engineering teams automating cloud-native operations
- Organisations on AWS and Kubernetes shifting from reactive firefighting to self-healing ops
- Teams without cloud or Kubernetes environments
- Non-technical users seeking no-code automation tools
- Buyers who need a documented Slack or PagerDuty integration before committing
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Skip Adps AI if your incidents need a documented Slack, PagerDuty, or Datadog integration on day one, since only AWS and Kubernetes are listed, or if you want a fixed published price for the SRE product before starting a commercial conversation.
Autonomous remediation is the headline capability, but the site says nothing about whether guardrails, dry-run, and approval modes are add-ons or enterprise-only features — get that in writing.
Without a published SRE price, sizing is guesswork: the only visible numbers are the unrelated website-optimisation tiers (Starter free, Pro $24/month billed annually, Agency $169/month billed annually). Comparable autonomous-remediation specialists — Datadog AIOps, New Relic — price per host and per seat and are expensive at scale, so Adps AI likely sits in the same enterprise band. Only signed demo-and-quoted pricing tells you where you land.
In short
Adps AI — AI-native SRE platform whose specialised agents detect, diagnose, and remediate cloud, Kubernetes, and CI/CD incidents — including IT operations alerts by. Best for SRE teams looking to cut on-call burden with autonomous incident response, DevOps engineers managing large-scale Kubernetes clusters, Platform engineering teams automating cloud-native operations. Free to start; paid plans from $24/mo.
What's new in Adps AI
Checked 6 days agoAcross the latest 5 updates: 5 feature updates.
June Jetstream Update
One-click optimize 30% faster; real-time alerts to email/mobile; faster DNS pre-fetching.
May Surge Update
Unified Analytics & Monitoring hub; PageSpeed integration; Performance Trends graph; fixes.
March Momentum Update
JS/CSS minification 25% more efficient; new Code Health report; HTML optimization fix.
February Flash Update
Image compression 15% faster; AVIF support; pre-optimization preview tool.
Launch Blitz Update
One-click optimize 20% faster; interactive onboarding; custom welcome dashboard.
What people actually say about Adps AI — is it worth it?
We scanned public community sources for Adps AI on Aug 21, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Our own analysis of that scan says the posts were off-subject. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
Viability Score
How well maintained and how widely used is Adps AI? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Real-time incident detection across cloud and Kubernetes
- Automated root cause analysis across logs, metrics, traces, and changes
- Automatic rollback, restart, and scaling decisions
- Proactive anomaly detection and healing
- Instant remediation with safe, policy-driven actions
- Continuous learning to prevent repeat incidents
- Automatic pod and node autoscaling (HPA/VPA)
- Smart traffic routing with service mesh
- Real-time cluster performance tuning
- Self-healing deployments and rollouts
- Real-time metrics, logs, and traces
- Kubernetes event and pod health tracking
- Service-level insights (SLIs/SLOs)
- Anomaly detection with alert intelligence
- Git Change Intelligence and CI/CD agents
About Adps AI
Adps AI is an AI-native SRE platform aimed at DevOps, SRE, and platform engineering teams running cloud-native infrastructure on AWS and Kubernetes. Rather than paging an on-call engineer, it runs a network of specialised AI-SRE agents — Git Change Intelligence and CI/CD agents, AIOps Detection and Anomaly Detection agents, Incident-SRE and Incident-Reliability agents, Signal and Telemetry Intelligence agents, Monitoring and Observability agents, and an AIOps Orchestrator — that detect incidents, correlate logs, metrics, traces, and changes, then execute remediation such as rollback, restart, and scaling themselves. Kubernetes gets dedicated treatment: HPA/VPA pod and node autoscaling, service-mesh traffic routing, cluster performance tuning, and deployments and rollouts that heal themselves, tracked against SLIs and SLOs. Onboarding is presented as three steps — connect your cloud, repos, and pipelines, then operations run on their own. The vendor claims MTTR reduced by up to 99%; that figure is a vendor number, not a benchmarked one. One thing to know before you look: the public /pricing page currently shows a website-optimisation plan (Starter free, Pro $24/month billed annually, Agency $169/month billed annually) that has nothing to do with the SRE product, and no SRE pricing is published anywhere on the site — access requires requesting a demo.
Behind the Verdict
Adps AI's pitch is narrow and specific: replace the human on-call response loop with a roster of specialised agents that see an incident, work out what changed, and act. The architecture is what sets it apart from AIOps incumbents. Where Datadog (and the AIOps tier of New Relic) stop at alerting and dashboarding, Adps AI positions agents that do rollback, restart, and scale on their own. Its Git Change Intelligence and CI/CD agents tie root cause to the commit or pipeline run that actually broke prod — a genuinely useful correlation that most observability tools leave to a human mashing tabs. If you live on Kubernetes all day, the agent list reads like a wishlist: HPA/VPA autoscaling, service-mesh traffic routing, self-healing rollouts, SLI/SLO tracking with error-budget breach detection. The failure modes are as important as the features. Autonomous remediation is a high-trust capability, and the site says nothing about remediation guardrails: dry-run modes, approval gates, blast-radius limits, or what happens when the AI's rollback choice is the wrong one. It documents AWS and Kubernetes as integrations and nothing else — no Slack, no PagerDuty, though those may simply be undocumented rather than missing. Sizing the thing is the biggest single blocker: the /pricing page is for a website-optimisation product (Starter free, Pro $24/month billed annually, Agency $169/month billed annually) and the SRE plan is not listed, so you cannot evaluate cost without a sales conversation. The MTTR-reduced-by-up-to-99% claim is a marketing figure, not a benchmarked result, and no third-party validation is offered. Treat Adps AI as a promising specialist that earns a demo and a proof-of-concept, not a signed contract — and use the POC to force transparency on guardrails and outage rollback behaviour.
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Real-world workflow fit
Concrete scenarios for the personas Adps AI actually fits — and what changes day-one when you adopt it.
Connect the AWS account, the Git repo, and the CI/CD pipeline in the three-step onboarding; leave agents running in observe-and-recommend mode for the first two weeks.
Outcome: On-call pages drop as the Git Change Intelligence agent ties failed rollouts back to the specific commit that caused them, and you get a baseline of what the agents would have done before letting them act.
Adps AI's incident detection catches the crash loop, the Incident-SRE agent correlates recent deploys and node health, and the remediation agent restarts or scales the workload under policy.
Outcome: The crash loop is resolved before the engineer opens a laptop, and the incident report shows exactly which change caused it.
Adps AI watches error budgets and service-mesh traffic, and reroutes traffic away from a degraded deployment when the SLO breach threshold is hit.
Outcome: Customer-facing availability holds during a bad rollout, and the post-incident review has a clean timeline of what the agents changed.
Use Cases
- Automatically detect and resolve Kubernetes pod failures without manual intervention
- Reduce MTTR for cloud production incidents by automating detection through remediation
- Perform root cause analysis by correlating logs, metrics, traces, and changes
- Self-heal AWS infrastructure by automatically rolling back faulty deployments
- Monitor and remediate CI/CD pipeline failures using AI agents
- Autoscale Kubernetes workloads with HPA/VPA
- Automatically reroute traffic via service mesh when a deployment degrades
- Maintain SLIs/SLOs with automated rollback when error budgets are breached
Limitations
- Pricing for the SRE platform is not published on the site, and the public /pricing page currently describes an unrelated website-optimisation service (Starter free, Pro $24/month billed annually, Agency $169/month billed annually), which makes cost evaluation impossible without a sales conversation.
- Access requires requesting a demo.
- Documented integrations are limited to AWS and Kubernetes.
- The MTTR-reduced-by-up-to-99% figure is a vendor claim, not a benchmarked result, and the site says nothing about remediation guardrails, dry-run or approval modes, or blast-radius limits for autonomous actions.
as of 2026-10-09
Verification history
We have re-verified Adps AI 10 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-checked, vendor evidence unchanged
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 10 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Adps AI tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Starter
Free forever
Ideal for
Solo owner of one personal website trying the website-optimisation product — not the SRE platform Adps AI markets on its homepage.
What this tier adds
Free entry point for the website-optimisation service: one site, basic image and code optimisation, up to 10,000 monthly pageviews.
Pro
$24/month, billed annually
Ideal for
Small site owner running up to ten websites who wants the optimisation product's analytics and priority support.
What this tier adds
Adds up to 10 websites, advanced optimisation, enhanced analytics and monitoring, 100,000 monthly pageviews, and priority support over Starter.
Agency
$169/month, billed annually
Ideal for
Marketing or web agency managing many client sites that needs white-label reporting and a named account contact.
What this tier adds
Adds unlimited websites, team collaboration, white-label reporting, unlimited pageviews, and a dedicated account manager over Pro.
Where the pricing makes sense
The company stage and team size where Adps AI's pricing actually pencils out — and where peers do it cheaper.
Without a published SRE price, sizing is guesswork: the only visible numbers are the unrelated website-optimisation tiers (Starter free, Pro $24/month billed annually, Agency $169/month billed annually). Comparable autonomous-remediation specialists — Datadog AIOps, New Relic — price per host and per seat and are expensive at scale, so Adps AI likely sits in the same enterprise band. Only signed demo-and-quoted pricing tells you where you land.
Setup time & first value
How long it actually takes to get something useful out of Adps AI — broken out by persona, not the marketing-page minute.
The site presents onboarding as three steps — connect cloud, repos, and pipelines — and claims 'instant agent activation' in seconds, which likely means the agent fleet reads your environment quickly. Real time to first trusted remediation is longer: plan a two-week observe-and-tune window before letting agents act unsupervised in production. Sizing the deal is separate: without a published SRE
Switching to or from Adps AI
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From PagerDuty + Datadog: point Adps AI at the same AWS and Kubernetes sources you already send to Datadog, and let its agents handle detection and remediation paging used to route to humans.
- →From manual runbooks: encode the rollback and restart steps your on-call engineers run by hand as Adps AI remediation policies, so the agents execute the same actions unattended.
- →From New Relic AIOps: run Adps AI alongside New Relic during the observe-only period, compare the two incident feeds, then shift the remediation actions to Adps AI once the false-positive curve is tolerable.
- ↗To Datadog: revert to Datadog alerting for detection and reintroduce human on-call runbooks for remediation — you keep the observability layer, you lose the autonomous fix.
- ↗To New Relic: fall back to alerting plus dashboards, and if you used Adps AI's Git Change Intelligence for commit-level root cause, add a separate change-tracking tool to cover that gap.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Adps AI”, and we withheld 6: 6 could not be judged, because “Adps AI” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Adps AI.
Official links
Tools that pair well with Adps AI
Common stack mates teams adopt alongside Adps AI, with the specific reason each pairing earns its keep.
Resolve AI
AI SRE platform whose autonomous agents go on-call, triage alerts, investigate incidents and run production tasks alongside your engineers.
Sazabi
Sazabi is AI-native observability: conversational debugging, autonomous alerts, and coding agents that open fix PRs.
Dash0
Dash0 is OpenTelemetry-native observability where Agent0 investigates incidents, finds root cause, and opens fix PRs.
Featured Head-to-Head Comparisons
Adps Ai vs Spider Cloud
Adps AI and Spider Cloud serve completely different needs: Adps AI is an autonomous SRE platform for cloud-native incident management (pricing requires sales contact), while Spider Cloud is a cost-effective web scraping API built for AI agents and RAG pipelines (freemium, ~$0.03/1k pages). Choose Adps AI if your team manages Kubernetes at scale and wants to reduce on-call burden; choose Spider Cloud if you need fast, structured web data for LLMs or AI workflows.
Adps Ai vs Presto Voice
These tools serve completely different domains: Presto Voice is for QSR chains seeking to automate drive-thru ordering and boost revenue through upselling, while Adps AI is for SRE/DevOps teams aiming to autonomously detect and fix incidents in cloud-native environments. Your choice depends on whether you run a restaurant franchise or manage Kubernetes clusters — there is no overlap.
Adps Ai vs Temporal Ai
Choose Temporal AI if you need to build resilient, long-running workflows with state recovery and retries—especially for AI agents or microservices. Choose Adps AI if you want an autonomous SRE platform that automatically detects and fixes production incidents in Kubernetes/cloud environments. These tools solve fundamentally different problems.
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Resolve AI
AI SRE platform whose autonomous agents go on-call, triage alerts, investigate incidents and run production tasks alongside your engineers.
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