Cloud, DevOps and platform engineering
Secure cloud foundations, infrastructure as code, containers and deployment you can repeat with confidence.
Capability
Keep your systems healthy and improving after launch
Subject to the agreed scope and operational arrangements, we can provide ongoing technical support after implementation.
In short
A technology partner should not disappear when a project reaches its final milestone. Where the engagement calls for it, Nexstella monitors, maintains and improves your systems after launch, under a scope, support hours and escalation arrangements agreed before support begins.
Last updated
01 When it matters
Do you need engineering support after launch?
Technology decisions made today should support the business tomorrow. Where the engagement calls for ongoing support, we can continue helping you monitor, maintain, improve and extend your systems.
We can also help assess new opportunities, prioritize improvements, troubleshoot technical challenges and plan the next stage of development.
A system has gone live, and the business wants it monitored, maintained and improved as its needs change, within arrangements that are clear to everyone.
We would agree the service boundaries, access permissions, support hours and escalation procedures up front, then monitor the system, investigate incidents and plan improvements.
02 Services
What does this include?
- Data pipeline maintenance and DataOps
- Application maintenance and enhancement
- Cloud infrastructure support
- Deployment and release assistance
- MLOps and LLMOps support
- Monitoring and incident investigation
- Performance and cost optimization
- Capacity and reliability reviews
- Security patching coordination
- Documentation and knowledge transfer
- Continuous improvement planning
03 Applications
Where does this usually apply?
- DataOps for data pipelines and platforms
- Platform support for cloud infrastructure and the releases that run on it
- Application maintenance and enhancement after launch
- AI operations, including MLOps and LLMOps support
04 Technology
How do we choose the technology?
Support builds on the technology already in place. Where changes are proposed, they weigh maintainability, supportability, your team's operational capacity and total cost of ownership.
05 Deliverables
What do you get?
- Agreed service boundaries, access permissions, support hours and escalation procedures
- Operational reports
- Optimization recommendations
- Updated performance baselines
- An improvement backlog
06 Needs
Which needs does this serve?
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Growing technology
For growing businesses whose infrastructure, data and releases need to keep pace with more users and complexity.
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Specialist engineering
For companies with their own engineers who need specialist skills, architecture advice or extra capacity.
07 Questions
What do people usually ask?
Depending on the agreed scope, it can cover data pipeline maintenance, application maintenance and enhancement, cloud infrastructure support, deployment assistance, MLOps and LLMOps support, monitoring, and incident investigation.
Service boundaries, access permissions, support hours, escalation procedures, responsibilities and applicable service-level objectives are agreed before support begins.
Where the engagement includes it, yes. Support can include performance and cost optimization, capacity and reliability reviews, and continuous improvement planning alongside monitoring and incident investigation.
Solutions
Which solutions do we build for this?
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Cloud foundation
We set up the accounts, networks, environments and controls that applications, data and AI workloads run on, so infrastructure is repeatable rather than improvised.
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DevOps automation
We automate how code and infrastructure are built, tested, scanned and deployed, with approvals and rollback, so releases rely less on manual steps.
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Cloud cost optimization
We make cloud costs visible by workload, set baselines, and then optimize resources and queries without weakening performance, security or reliability.
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MLOps and LLMOps
We build the engineering lifecycle around machine learning models and LLM applications: reproducible training, versioned prompts, evaluation, release controls and monitoring.
If this sounds like you: Production that more than one person can run
If this sounds like you: Production engineers, without hiring a platform team
If this sounds like you: Keep your live product running after your developer moves on