Let us handle it
DevOps, SRE & Cloud Infrastructure
Indext Data Lab provides specialized engineering services in Cloud Infrastructure, Site Reliability Engineering (SRE), and DevOps automation. We design and manage scalable cloud platforms, containerized environments, CI/CD pipelines, and production-grade MLOps systems
100% Job Success
Expert-Vetted
Top-Rated Plus
100% Job Success
Expert-Vetted
Top-Rated Plus
100% Job Success
Expert-Vetted
Top-Rated Plus
100% Job Success
Expert-Vetted
Top-Rated Plus
Our priorities
1
Architectural Independence
We prioritize open-source, self-hosted architectures to eliminate proprietary vendor lock-in. Our goal is "Cloud Sovereignty"—ensuring you retain full ownership of your platform with true data portability. This allows your infrastructure to migrate between providers in days, ensuring your technology stack remains an asset, not a rental.
We prioritize open-source, self-hosted architectures to eliminate proprietary vendor lock-in. Our goal is "Cloud Sovereignty"—ensuring you retain full ownership of your platform with true data portability. This allows your infrastructure to migrate between providers in days, ensuring your technology stack remains an asset, not a rental.
2
Eliminating Zombie Infrastructure
Industry analysis shows 30% of enterprise cloud spend is wasted on "Zombie Resources"—development environments that were provisioned for testing and never decommissioned. We treat this idle infrastructure as a critical inefficiency that undermines budget predictability.
Industry analysis shows 30% of enterprise cloud spend is wasted on "Zombie Resources"—development environments that were provisioned for testing and never decommissioned. We treat this idle infrastructure as a critical inefficiency that undermines budget predictability.
2
Policy-as-Code Implementation
Indext Data Lab implements Time-to-Live (TTL) tagging on all non-production resources to automate lifecycle management. For example, if a developer provisions a high-cost GPU instance, our protocols ensure it automatically terminates after 8 hours unless explicitly renewed. This single automated governance script often recovers more budget than the cost of our monthly retainer.
Indext Data Lab implements Time-to-Live (TTL) tagging on all non-production resources to automate lifecycle management. For example, if a developer provisions a high-cost GPU instance, our protocols ensure it automatically terminates after 8 hours unless explicitly renewed. This single automated governance script often recovers more budget than the cost of our monthly retainer.
What we're offering
The Foundation: Infrastructure & Orchestration
With focus on: performance and intelligence
We engineer immutable environments. By treating your infrastructure as code, we eliminate "it works on my machine" bugs and prevent the silent creep of cloud costs
We engineer immutable environments. By treating your infrastructure as code, we eliminate "it works on my machine" bugs and prevent the silent creep of cloud costs
Terraform / OpenTofu
- Role: Infrastructure-as-Code (IaC) provisioning tool asynchronous backends for AI-agent integration
- Impact: We use it to turn your cloud setup into versioned code, ensuring environments are reproducible and free from manual errors
Kubernetes (K8s)
- Role: Container orchestration platform
- Impact: Ensures your applications automatically scale to meet traffic spikes and "self-heal" by restarting failed services instantly
- Role: Containerization standard
- Impact: Guarantees that your software runs identically on a developer's laptop and the production server, eliminating "it works on my machine" bugs
Prometheus & Grafana
- Used for: Real-time monitoring and visualization suite
- Impact: Provides 24/7 dashboards and alerts, allowing us to detect and fix performance bottlenecks before they affect your users
Observability & delivery (CI/CD)
With focus on: stability and cost-control
Traditional monitoring tells you when something broke. Our approach tells you why it’s about to break. We move beyond simple "uptime" to track the actual health of your business logic.
Traditional monitoring tells you when something broke. Our approach tells you why it’s about to break. We move beyond simple "uptime" to track the actual health of your business logic.
GitHub Actions/GitLab CI
- Used for: Automated CI/CD pipelines
- Impact: We build secure pipelines that test and deploy your code automatically, reducing release time from days to minutes
- Used for: Log aggregation and analysis
- Impact: Centralizes logs from all your services, making it easy to trace errors and conduct post-incident analysis
The Engine:
Cloud & AI Operations
Cloud & AI Operations
With focus on: security, compliance, and data protection
AI workloads are different. They require specialized handling of state, massive memory bandwidth, and expensive compute resources. We treat your cloud infrastructure as a precision instrument for growth.
AI workloads are different. They require specialized handling of state, massive memory bandwidth, and expensive compute resources. We treat your cloud infrastructure as a precision instrument for growth.
AWS/GCP (Cloud)
- Used for: Hyperscale cloud providers
- Impact: We optimize your cloud architecture specifically for cost efficiency (FinOps) and high-performance GPU availability
Python (Automation)
- Used for: Scripting language for Ops
- Impact: We write custom automation scripts to glue complex systems together and automate routine maintenance tasks
- Used for: Vector Database Infrastructure
- Impact: We manage and scale the specialized infrastructure required to run your RAG and AI-agent workloads in production
Building infrastructure in the AI era
In the modern technical landscape, the distinction between "Cloud Infrastructure" and "Artificial Intelligence" has dissolved. To build a sustainable system, we recognize that an AI model is only as effective as the environment that hosts it. We replace the 'black box' approach to AI with transparent, sovereign infrastructure. By enforcing deterministic reliability on probabilistic outputs, we ensure your AI transition is both stable and cost-optimized. This is how the disciplines of Cloud and AI converge:
LLMOps & The Evolution of DevOps
Traditional DevOps focuses on the Lifecycle of Code. In the AI era, this expands into LLMOps, which manages the tripartite lifecycle of Code, Data, and Model Weights.
We implement Hybrid Architectures that utilize Data Lineage tracking. This ensures that every model response can be traced back to its source data, solving the "black box" problem of reproducibility.
We implement Hybrid Architectures that utilize Data Lineage tracking. This ensures that every model response can be traced back to its source data, solving the "black box" problem of reproducibility.
Semantic Reliability & The New SRE
Standard Site Reliability Engineering (SRE) monitors system "uptime." AI-driven SRE monitors Semantic Health and Model Drift.
We deploy Deterministic Guardrails and Semantic Routers. By calculating the Cosine Similarity between model outputs and "ground truth" data, we catch hallucinations in real-time.
We deploy Deterministic Guardrails and Semantic Routers. By calculating the Cosine Similarity between model outputs and "ground truth" data, we catch hallucinations in real-time.
GPU Orchestration & Cloud Sovereignty
AI requires a fundamental shift in Cloud Resource Management. We move from CPU-heavy web hosting to high-performance GPU Orchestration and Vector Database management.
To prevent Vendor Lock-in, we prioritize Self-Hosted Architectures using tools like Kubernetes for GPU scaling. We mitigate the high cost of inference through Quantization (reducing model size without losing intelligence) and Inference Optimization.
To prevent Vendor Lock-in, we prioritize Self-Hosted Architectures using tools like Kubernetes for GPU scaling. We mitigate the high cost of inference through Quantization (reducing model size without losing intelligence) and Inference Optimization.
By combining these clusters, we offer a unified standard: Automated Governance. Our goal is build the sovereign, self-correcting infrastructure that allows AI to scale without spiraling costs or architectural fragility.
Selected case studies
Before you ask
Can you work with our existing engineering team?
Yes. We integrate into your Slack/Discord and Jira, acting as the "Special Forces" unit that handles the complex infrastructure plumbing so your product developers can focus on features.
How does Indext Data Lab handle incident response and 24/7 on-call for SRE clients?
We act as a Fractional SRE team. We integrate with your existing Slack, PagerDuty, or Opsgenie environments to establish a tiered escalation policy. When a Service Level Objective (SLO) is breached—such as a spike in 5xx errors or latency exceeding your 99th percentile—our engineers are notified instantly. Our goal is to reduce Mean Time to Recovery (MTTR) by performing immediate triage, followed by a detailed "blameless post-mortem" to ensure the same root cause never triggers an alert twice.
Can you integrate your LLMOps framework with our existing on-premise legacy data silos?
Absolutely. Most "AI-ready" firms are still sitting on decades of legacy data. We build secure Hybrid Cloud Connectorsthat act as a bridge between your on-premise SQL/Oracle databases and your cloud-native Vector Infrastructure (Milvus/Pinecone). We use Python-based ETL (Extract, Transform, Load) pipelines to sanitize, chunk, and embed this data, allowing your RAG (Retrieval-Augmented Generation) systems to "talk" to your legacy data without requiring a risky, full-scale migration to the cloud.
How do you ensure data lineage and GDPR compliance within an AI-driven CI/CD pipeline?
In the AI era, compliance is about "Data Provenance." We implement immutable data versioning within your CI/CD pipelines. This ensures every model output can be traced back to the specific version of the dataset used for training or fine-tuning. For GDPR compliance, we build automated workflows to identify and "scrub" PII (Personally Identifiable Information) before it enters your vector database, ensuring your AI agents don't inadvertently store or surface sensitive customer data.
Do your automated cost governance scripts work across multi-cloud environments or just AWS?
We leverage OpenTofu and Crossplane to apply standardized governance across AWS, GCP, and Azure. By providing a unified view of your cloud spend, we ensure that a stray high-performance cluster on GCP doesn't go unnoticed while you’re focused on your AWS dashboard.
Interested?