Adps AI
Adps AI is an AI-native SRE platform for DevOps and on-call teams. Specialized agents detect, diagnose and resolve production incidents across cloud, Kubernetes and CI/CD, claiming up to 99% lower MTTR. It supports AWS only.
What is Adps AI?
Adps AI presents itself as an AI-native DevOps and SRE platform: a network of specialized agents that detect, diagnose and resolve production incidents across cloud, Kubernetes and CI/CD without waiting for a human to pick up the pager. The homepage states the claim bluntly, promising autonomous incident resolution and an MTTR reduced by up to 99%.
The work is split across named agents rather than handled by a single model. The site describes agents for Git change intelligence and CI/CD, AIOps detection and anomaly detection, incident and reliability response, remediation orchestration, signal and telemetry intelligence, and monitoring and observability; the terms of service add GitOps, IaC, Docker, Kubernetes, security and PromptOps agents to that roster. Above them sit three operating modes you choose from: Observe-only, Human-in-the-loop and Autonomous. Nothing runs outside an explicit authorization policy.
Scope is deliberately narrow. The platform is optimized exclusively for AWS, with deep hooks into EKS, EC2, ECR, S3, Lambda, CloudWatch and IAM, and it works strictly inside the IAM permissions you grant, which you can revoke from the AWS console at any time. On Kubernetes it handles pod and node autoscaling through HPA and VPA, service-mesh traffic routing, real-time tuning and self-healing rollouts. Observability covers metrics, logs and traces, event and pod health tracking, SLI/SLO monitoring and anomaly detection. Infrastructure-as-Code is generated and applied in Terraform or CDK.
Engineers interact in plain language, through CLI commands, GitOps workflows or agentic prompts. The FAQ insists that no complex dashboards or heavy YAML are required, and that most teams reach autonomous workflows within 30 to 60 minutes. The homepage narrates the same path in three steps: connect your environment, let the AI take over with auto-build, auto-heal and auto-scale, then run continuous autonomous operations.
Maturity is the caveat. The company is named Adps AI, calls itself remote-first, and is led by founder and CEO Dhruvit Talati. The domain was registered in August 2025, the first Wayback capture dates from December 2025, and a waitlist page still announces that Adps AI is launching soon.
What it does
- Detect production incidents in real time across cloud and Kubernetes
- Run root cause analysis automatically on logs, metrics, traces and recent changes
- Execute remediation through policy-governed actions
- Trigger rollbacks, restarts and scaling decisions without human input
- Generate and apply Infrastructure-as-Code in Terraform or AWS CDK
- Correct configuration drift before it turns into an outage
- Trigger and optimize CI/CD workflows
When to use Adps AI / When not to
A quick filter to help you decide if Adps AI is the right fit.
When to use Adps AI
- SRE and DevOps teams carrying the on-call pager for production workloads running on AWS
- Platform teams operating Amazon EKS and Kubernetes clusters at scale
- Startups running production infrastructure without a dedicated platform team
- Engineering organizations trying to cut the human cost and fatigue of on-call rotations
- Teams looking to consolidate a sprawling DevOps toolchain behind a single agentic layer
When not to use Adps AI
- Organizations running on Azure, Google Cloud or on-premise: the terms of service state that Adps AI supports AWS only at this time
- Non-technical users and teams that do not own cloud, Kubernetes or CI/CD infrastructure
- Teams that need immediate general availability, since the site still runs a waitlist and announces a launch to come
- Buyers whose process requires public pricing before a first conversation, as no product tariff is published
- Organizations that require a completed SOC 2 certification: the vendor describes SOC2-ready controls, not an obtained certificate
How to use Adps AI
A typical end-to-end flow, from setup to results.
- Request a demo through the vendor's cal.com link or join the waitlist, as there is no self-service signup today
- Connect your AWS account and your Git provider, whether GitHub, GitLab or Bitbucket
- Grant Adps AI an IAM role that scopes what it may read and do: metadata, logs and metrics, IaC generation, deployments, CI/CD triggers and self-healing actions
- Choose your automation level between Observe-only, Human-in-the-loop and Autonomous
- Let the platform map your environment, from cloud resources, pipelines and repositories to cluster states, metrics, logs and configurations, into a real-time operational model
- Hand day-to-day execution over to the agents for auto-build, auto-heal and auto-scale
- Interact in natural language, through CLI commands, GitOps workflows or agentic prompts
- Let detection and remediation run continuously, with first autonomous workflows expected within 30 to 60 minutes according to the vendor
- Revoke the AWS permissions from your own AWS console whenever you want to stop
Pros & Cons
Pros
- Closes the loop that AIOps leaves open, executing the remediation instead of stopping at the alert
- Three graduated automation modes, including an observe-only mode that takes no action at all
- A clear contractual commitment not to train models on customer data
- Ownership of the outputs stays with the customer, and the terms say so explicitly
- IAM permissions are scoped and revocable from your own AWS console at any moment
- A long list of named native integrations: AWS services, GitHub, GitLab, Bitbucket, Terraform, ArgoCD, Slack, Jira, Prometheus, Grafana, Snyk and Trivy
- TLS 1.2+ in transit and AES-256 at rest, with secrets never stored in plain text
Cons
- AWS only: neither Azure, nor Google Cloud, nor on-premise environments are supported
- No product pricing is published; the /pricing page still carries the demonstration tariff of the website template the site was built from, describing an unrelated product
- A waitlist page still announces that the product is launching soon, so general availability is not established
- No API documentation and no dedicated product page: the features live on the homepage
- SOC2 is presented as internal SOC2-ready controls and alignment, never as an obtained certification
- No published DPA, no subprocessor list and no Article 27 EU representative
- No postal address, no full legal entity name and no support email: contact runs through a form or a booking link
Pricing & Plans
No pricing for the Adps AI platform is published. The site does expose a /pricing page, but its content is the demonstration tariff of the website template the site was built from and describes an unrelated product, so none of those figures can be attributed to Adps AI. No free plan and no free trial are announced for the platform. The terms of service confirm that paid subscriptions exist, that they are billed monthly or annually, that payments are non-refundable except where the law requires otherwise, that taxes may apply and that prices may change with prior notice. AWS infrastructure costs are billed by AWS directly and remain separate from the Adps AI subscription fees. Access today runs through a demo request or the waitlist, so any figure must be obtained from the vendor.
Data, GDPR & hosting
A consolidated view of how Adps AI handles your data.
GDPR overview
Implementation is partial and largely declaratory. The privacy policy says that, depending on your location, you may have rights under GDPR, CCPA or similar laws, and lists them: access, rectification, deletion, export, objection to certain processing, revocation of AWS and third-party access, and account closure. Requests go by email to privacy@adps.ai, handled by what the policy calls a Privacy & Security Office. International transfers to the United States are acknowledged, with protections said to be consistent with applicable data protection laws but never specified. Everything else is absent: no explicit claim of GDPR compliance anywhere on the site, no Article 27 EU representative, no named DPO, no stated legal basis for processing, no mention of Standard Contractual Clauses, no published DPA, and no subprocessor list, the policy naming only categories of recipients.
Who owns the data?
The terms of service split ownership cleanly. Adps AI keeps its software, its agents and models, its brand, its documentation, its training data and its interface. You keep your AWS resources, your code, your configurations and data, and the prompts and outputs generated from your own source materials; what you receive is a limited, non-exclusive, revocable license to use the platform. The vendor states that it does not store sensitive application data or AWS credentials beyond what operation requires, and that it does not collect the application data, customer records or personal data living inside your AWS environment. Human access is restricted to authorized staff, logged, and confined to troubleshooting, support tickets or security incidents.
Reuse rights
Outputs generated from your own source materials belong to you, and nothing in the terms claims a revenue share or restricts commercial reuse of what the agents produce. Two conditions sit alongside that freedom. First, you remain responsible for reviewing and validating the code and the actions the AI generates: the terms place the risk carried by generated code, Infrastructure-as-Code and automations on you, not on the vendor. Second, the acceptable-use clause forbids using Adps AI to build a competing AI DevOps platform. In short, reuse is free, verification is your job, and turning the service against itself is not permitted.
Data retention & training
Hosting summary
Jurisdiction is American: the terms of service place the contract under the law and the courts of the State of Delaware. The privacy policy states that if you access Adps AI from outside the United States, your data may be transferred to and processed in the U.S. No AWS region and no more precise hosting country are named. The announced security measures are TLS 1.2+ in transit, AES-256 at rest, secure AWS IAM integration, role-based access control, internal controls described as SOC2-ready, secrets never stored in plain text and regular security audits; AWS credentials are encrypted, revocable and used only for authorized operations. What is left unsaid matters as much: no DPA, no subprocessor list, no Standard Contractual Clauses and no detail on the safeguards covering transfers. One technical caveat: the site's IP address resolves to an Amazon anycast node observed from Amsterdam, which is a CDN edge, not a place where customer data is stored.
Things to keep in mind
Risks and trade-offs to weigh before adopting Adps AI.
- In Autonomous mode the AI modifies production infrastructure with no human validation, once your team enables it
- The terms state that AI outputs may contain errors and that reviewing them remains your responsibility; the vendor disclaims liability for changes executed under the automation rules you configured
- Financial exposure: automatic scaling actions generate AWS costs billed to you directly, while the contractual liability cap is limited to what you paid over the previous twelve months, with AWS costs and infrastructure damage excluded
- Privilege surface: the platform operates with broad IAM permissions over your production environment
- Skill erosion: delegating on-call duty and root cause analysis steadily drains a team's own operational knowledge of its systems
- Dependency on a young, pre-launch vendor with no public corporate footprint, no address and no known funding
- The terms give no guarantee that logs, history or generated artifacts will be preserved, and the testimonials shown on the site are unverifiable and should not be read as customer references
Setup & Integrations
Technical difficulty
Moderate, and technical by nature. You need an AWS account and a Git provider, and the central act is granting Adps AI an IAM role, which means deciding how much authority an automated agent gets over production. The vendor says most teams reach autonomous workflows in 30 to 60 minutes, that nothing in your existing infrastructure has to be replaced, and that engineers need no new tool, no complex dashboards and no heavy YAML. The real audience is DevOps and SRE profiles, not general users. Access is currently gated behind a demo or the waitlist rather than self-service.
Deployment
Integrations
Behind Adps AI
Resources
All the official URLs gathered for verification and reference.
Frequently asked questions
What does Adps AI actually automate?
Does it replace my existing DevOps tools or work alongside them?
Can the AI change my infrastructure without human approval?
Which clouds are supported?
How secure are the actions the agents take?
What does onboarding look like, and how long does it take?
Is my data used to train AI models?
How long is my data kept?
What does Adps AI cost?
Who pays for the AWS resources the agents create?
Should you pick Adps AI?
Adps AI has a clear and genuinely differentiating proposition: close the loop that AIOps leaves open. Where most tooling stops at a well-correlated alert, this platform claims to execute the remediation itself, through rollbacks, restarts, scaling, drift correction and generated Infrastructure-as-Code, inside the IAM permissions you grant it. The technical vocabulary is credible: HPA and VPA, service-mesh routing, SLI/SLO tracking, Terraform and CDK, and three graduated automation modes that let a team start in observe-only. The confidentiality commitments are unusually clean for a young vendor: no training on customer data, output ownership left with the customer, AWS credentials encrypted and revocable at will.
What is missing is proof. The domain was registered in August 2025 and the first Wayback capture dates from December 2025. A waitlist page still announces a launch to come. The pricing page was never customized and still displays the demonstration tariff of the website template, describing a product that has nothing to do with SRE. The footer social links point at the networks' generic homepages. The testimonials cannot be verified and were not treated as facts here. There is no postal address, no full legal entity name, no known funding round and no obtained certification.
The practical consequence is simple. Adps AI is worth a conversation if you run production on AWS, carry a real on-call burden and are comfortable evaluating a pre-launch vendor through a demo rather than a self-service trial. Start in observe-only, scope the IAM role tightly, and budget for the AWS costs that autonomous scaling will put on your own bill. If you need Azure, Google Cloud, public pricing or a signed compliance artifact today, this is not yet your tool.
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