codetoday.ioOur DigitalTransformation Services
End-to-end engineering across the full modern data and AI stack.
How We Deliver Results
A battle-tested three-phase approach — from discovery to production — with zero hand-waving.
Assess & Architect
We start with a deep-dive into your stack, data flows, and pain points. No boilerplate proposals — every architecture is custom-designed for your scale and constraints.
Build & Integrate
Embedded engineers work alongside your team. We ship iteratively — every sprint delivers running code in your environment, not slide decks. CI/CD and observability from day one.
Operate & Optimise
We don't disappear post-launch. Continuous FinOps, automated alerts, runbooks, and optional 24×7 SRE support ensure your platform runs lean and reliable in production.
Trusted by engineering teams at
Who We Are
Engineers Who Live at the Intersection of Data, Cloud & AI
codetoday.io is a specialized engineering firm focused on the hardest problems in modern technology stacks. We don't do generalist IT — we go deep on DevOps, MLOps, Data Engineering, Big Data, and AI systems.
Our teams have shipped production workloads across AWS, Azure, and GCP — from real-time streaming pipelines processing billions of events, to multi-agent AI systems serving enterprise customers at scale.
- Deep expertise in cloud-native and AI-first architectures
- Proven track record across pharma, life sciences, and fintech
- Zero-to-production delivery with embedded engineering teams
- Continuous cost optimization and FinOps built into every engagement
Delivering Measurable Impact
Happy Clientsacross pharma, fintech & enterprise
Projects Deliveredend-to-end cloud & data solutions
AI Agents Deployedin production across use cases
Years Experiencebuilding technology that scales
Tools & Technologies We Master
We go deep — not broad. Every tool here has been used in production at scale.
We're Not a Generalist Shop
We specialize in the technologies that matter most for modern data and AI engineering. Here's what that means for you.
Production-First Mindset
Every architecture decision is made with production scale in mind. We don't build prototypes — we build systems that run at midnight on New Year's Eve without paging anyone.
Deep Vertical Expertise
DevOps, MLOps, Data Eng, BigData, and AI are not side practices for us — they're all we do. Our engineers live in these stacks daily and stay ahead of the curve.
Cost Engineering Built In
We instrument FinOps from day one. Tagging strategies, idle resource detection, right-sizing — cost reduction is part of delivery, not an afterthought.
Speed Without Shortcuts
We move fast because we have deep experience, not because we cut corners. Security, observability, and disaster recovery are non-negotiables in every engagement.
Transparent Pricing
No surprise invoices. Fixed-scope or retainer — you choose.
- Cloud FinOps or DevOps audit
- Architecture review + recommendations
- 2-week sprint with final report
- 30-day follow-up support
- Hands-on implementation
- Dedicated engineer
- End-to-end implementation (8–16 weeks)
- 2 dedicated senior engineers
- 2-week sprints with live demos
- Production-grade CI/CD + monitoring
- 90-day post-launch support
- Knowledge transfer sessions
- Dedicated pod (4–8 engineers)
- Multi-cloud, multi-region architectures
- 200+ AI agent deployments
- SLA-backed support (99.9%)
- Quarterly executive reviews
- Co-authoring on whitepapers
Real Results from Real Engagements
Numbers speak louder than testimonials. Here's what we've delivered.
Built a full MLOps platform on AWS SageMaker + MLflow for a global pharma MNC. Automated model retraining, versioning, and canary rollouts — eliminating 6-week manual deployment cycles entirely.
Rebuilt cloud infrastructure on AWS with FinOps-first architecture — right-sizing EC2 fleets, eliminating zombie resources, implementing intelligent tiering on S3. Achieved 99.99% uptime since go-live with zero unplanned outages.
Designed and deployed 200+ AI agents on AWS Bedrock AgentCore — covering clinical data extraction, regulatory compliance summarisation, and patient journey analytics. Full enterprise governance and audit trail included.
Migrated a batch-daily reporting pipeline to a real-time streaming lakehouse on Kafka + Redshift + dbt. Sub-second dashboards replaced overnight batch jobs, unlocking live inventory and pricing decisions.
Built a high-throughput IoT telemetry pipeline on AWS Kinesis + Timestream for real-time sensor monitoring across manufacturing plants. ML anomaly detection reduced unplanned downtime by 38%.
Deployed predictive maintenance ML models on edge compute — processing sensor data at the plant level and alerting engineers before failures occur. Integrated with existing MES systems with zero disruption to operations.
What Our Clients Say
Real outcomes from real engineering engagements.
Real Engagements, Real Results
Three case studies. Measurable outcomes. No stock photos, no made-up metrics.
E-Commerce DevOps Overhaul
Replaced a fragile Jenkins monolith with ArgoCD + GitHub Actions on EKS. 8 deploys a day, $175K/mo cloud savings, zero P1 incidents in 6 months.
Series B FinTech — From 6-Week Cycles to Daily Deploys
Full IDP build on AWS EKS + Backstage. Terraform-managed infra, automated DR, real-time cost dashboards. Deployment frequency up 10x, infra cost down 40%.
Pharma Clinical Data — 200+ AI Agents on Bedrock AgentCore
Multi-agent orchestration on AWS Bedrock AgentCore for clinical data extraction and regulatory document summarization. 87% analyst time saved.
Explore Our Services in Depth
Click a service to see tools, outcomes, and a real case study snapshot.
DevOps & Platform Engineering
We build internal developer platforms and CI/CD systems that reduce deployment friction to zero. Your teams ship code continuously with full observability, automated rollbacks, and GitOps workflows that scale from 5 engineers to 500.
MLOps & AI Platforms
We build the infrastructure that keeps ML models in production — not just the initial deploy. Feature stores, automated retraining pipelines, model registries, A/B testing frameworks, and the observability layer to catch data drift before it becomes a business problem.
Data Engineering
Reliable ELT/ETL pipelines, lakehouse architectures, and semantic data layers that your BI teams can trust. We replace fragile spaghetti pipelines with lineage-tracked, tested, observable data workflows that just work — even at 1B+ events per day.
Big Data & Analytics
Petabyte-scale data warehouses, real-time OLAP engines, and BI platforms that transform raw event streams into business intelligence. We architect for the volume you have today and the 10x you'll have in 18 months.
Generative AI & LLM Agents
Custom AI agents, RAG systems, and multi-agent orchestration workflows built for enterprise production — not just demos. We add the guardrails, audit trails, cost controls, and governance layers that make AI deployable in regulated industries.
Cloud Infrastructure & FinOps
Multi-cloud architecture, infrastructure-as-code, security hardening, and cloud cost optimization. We don't just reduce your bill — we instrument FinOps observability so your teams can see cost attribution in real time and catch waste before it accumulates.
Built on the Tools That Actually Scale
We work with the platforms and frameworks that power the most demanding production environments.
What Could We Save You?
Adjust the sliders to see a conservative estimate of ROI from a typical engagement.
Estimates based on industry benchmarks (McKinsey, Gartner, DORA Report 2024). Actual results vary — we'll give you a detailed estimate in a free consultation.
Book a free 30-min architecture review →
Us vs In-House vs Big 4 Consulting
An honest comparison. We win on speed, depth, and accountability. Not on slide decks.
| Criterion | codetoday.io | In-House Team | Big 4 / SI |
|---|---|---|---|
| Time to first output | 1–2 weeks | 3–6 months (hiring) | 4–8 weeks (scoping) |
| Seniority of engineers | Senior only, no juniors on your work | Mixed, depends on market | Seniors sell, juniors deliver |
| Cost structure | Fixed scope or lean T&M | High fixed cost + benefits + equity | $300–600/hr blended rate |
| Domain depth | MLOps · DevOps · Data · GenAI — specialists | Narrow, depends on hires | Generalist with thin vertical layers |
| Accountability | Named engineers, direct Slack access | Full (internal) | Account manager layer, slow escalation |
| Knowledge transfer | Built-in: runbooks, workshops, pair programming | Native — knowledge stays | Often poor — creates dependency |
| Scale up/down | 1 week notice, elastic team size | Months to hire or PIP | Contractually rigid |
| Delivery track record | 3 public case studies, verifiable results | Depends on team | References guarded by NDAs |
Engagement Models
Flexible structures that fit your stage — from a targeted sprint to a long-term embedded team.
A time-boxed deep dive on one concrete problem: CI/CD overhaul, data pipeline rebuild, cost audit, or MLOps quickstart. Defined deliverables, defined timeline.
- Fixed scope & price
- 2–3 senior engineers embedded
- Architecture doc + runbooks
- 30-day post-delivery support
A dedicated pod of 3–6 engineers that works alongside your team on continuous platform evolution. Best for greenfield builds and complex multi-quarter programmes.
- Dedicated 3–6 person pod
- Daily standups with your team
- Full knowledge transfer
- Quarterly business reviews
On-demand SRE coverage for production platforms we've built or that need ongoing optimization. Incident response, FinOps reviews, and proactive reliability engineering.
- 24×7 on-call coverage
- Monthly FinOps reviews
- Proactive SLO management
- Incident post-mortems
Technology Radar
Inspired by Thoughtworks. Where we stand on every tool in our stack — updated quarterly.
Meet the Engineering Leads
Senior engineers who've shipped at scale — not consultants who write reports.
From the Engineering Blog
Technical deep-dives, architecture patterns, and lessons from production.
Deploying 200+ AI Agents on AWS Bedrock AgentCore: Architecture & Lessons Learned
How we designed a multi-agent orchestration system with enterprise guardrails, cost controls, and audit trails for a pharma client.
The AWS Cost Spiral: How SageMaker Zombie Endpoints Quietly Burn $700K/yr
A practical guide to detecting, attributing, and eliminating idle ML endpoints before they crater your cloud budget.
Real-Time Lakehouse on AWS: Kafka → Glue → Redshift Serverless in Under 4 Hours
Step-by-step walkthrough of standing up a production-grade streaming lakehouse with sub-second latency and zero-ops maintenance.
Kubernetes Cost Optimisation Checklist 2025: 14 Levers We Pull on Every Cluster
From namespace resource quotas to Karpenter spot-instance pools — the exact playbook we apply to every client cluster.
AWS Bedrock vs Azure OpenAI for Enterprise: A Practitioner's Honest Comparison
After running both in production we have strong opinions. Here's where each wins, where each loses, and how to choose.
MLflow vs SageMaker: Which MLOps Platform for 2025?
We've used both at scale. The answer depends on your team's cloud maturity, not the marketing slides.
We're Hiring Senior Engineers
We work on the hardest infrastructure problems in data and AI. If you've shipped production ML platforms, real-time data pipelines, or multi-agent AI systems — we'd like to talk.
See all openings →Why Engineers Love Working Here
Real production work
Zero toy projects. All engagements are production-scale challenges.
Remote-first globally
Work from anywhere. Clients across US, EU, India, Southeast Asia.
Learning budget
$2K/yr for certs, conferences, and courses. We invest in your growth.
Equity upside
Senior engineers get ESOP participation from day one.
5+ years with Kubernetes, Terraform, and CI/CD at scale. Experience with IDP (Backstage / Port) is a plus.
Apply →Production MLOps experience: SageMaker, MLflow, feature stores, model monitoring. Bedrock / LLM agent experience a strong plus.
Apply →Deep Spark, Airflow, and dbt expertise. Experience with streaming (Kafka, Flink) and lakehouse formats (Iceberg, Delta) required.
Apply →Frequently Asked Questions
Straight answers to what engineering leaders ask us most.
Engagement size varies by scope. Typical projects run $25K–$150K. We offer a free 2-hour discovery call to size the work accurately before quoting. Most clients see 3–5x ROI within 6 months through reduced incidents, faster deployments, and cloud cost savings.
MLOps is the discipline of running ML models reliably in production — versioning, monitoring, retraining, and CI/CD for models. If your data science team struggles to get models out of notebooks and into production, or models silently degrade over time, you need MLOps. We've shipped production ML pipelines for pharma, fintech, and retail clients.
Quick wins (CI/CD pipeline, K8s hardening, cost audit) ship in 4–6 weeks. Full platform builds (IDP, data lakehouse, multi-agent AI) take 3–6 months. We work in 2-week sprints so you see value every fortnight, not at the end of a long contract.
Both. We have a startup track (Series A–C) focused on building the right foundation fast, and an enterprise track for teams at scale dealing with complexity, compliance, and multi-cloud sprawl. Pricing and deliverables differ — book a call and we'll tell you honestly which track fits.
AWS is our primary speciality — 12 AWS certifications across the team, deep Bedrock/SageMaker/Glue/Redshift expertise. We also work on GCP (Vertex AI, BigQuery) and Azure (ADF, Synapse, Azure ML). Multi-cloud architectures are a core competency.
We're a senior-only team — no juniors billed at senior rates. The engineer who scopes your project ships it. We specialise in cloud-native + AI infrastructure exclusively, so we're deeper than a generalist firm. And we're 30–50% cheaper than Big 4 for equivalent outcomes.
Yes — embedded collaboration is our default model. We pair with your team, document everything, and leave you self-sufficient. Knowledge transfer is a deliverable, not an afterthought. We explicitly avoid dependency-building.
10% of the first engagement value with no cap and no expiry on your referral window (12 months from intro). A $100K project = $10K to you. Payments go out within 30 days of client invoice settlement. Full details on the Partner page.
What Our Clients Say About Us
Real results from real companies. No cherry-picked quotes — these are the outcomes we're most proud of.
"CodeToday migrated our entire ML pipeline to AWS Bedrock in 6 weeks. Our model deployment time dropped from 3 days to 2 hours. The team's depth in both ML and cloud is exceptional — they flagged cost issues before they became problems."
"We engaged CodeToday for a cloud cost audit and ended up saving $22K/month. What impressed us most was that they didn't just fix the immediate issue — they left us with dashboards and runbooks so our team can self-manage going forward."
"The DevOps transformation they led cut our release cycle from fortnightly to daily deploys. Zero production incidents in 4 months post-launch. The knowledge transfer was genuine — our team now owns and understands everything they built."
Senior Engineers, Not Slide Decks
The people who scope your project are the people who build it. No account managers, no bait-and-switch.
Active · India
ML Specialist & more
Remote-first delivery
Ready to Build Something Great?
Tell us about your engineering challenge. We'll get back to you within 24 hours.
codetoday.io
DevOps · MLOps · Data Engineering · BigData · AI Platforms
Bangalore, Karnataka 560066
India
accounts@codetoday.io
Mon–Fri, 9AM–6PM IST
Response within 24h
Book a 30-Minute Architecture Review
No sales pitch. Bring your hardest infrastructure problem — we'll discuss options, patterns, and whether we're a fit. Zero obligation.