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Reviewed by Dr. Dmytro Nasyrov, Founder and CTO

MLOps

Pharos Production provides end-to-end Machine Learning Operations (MLOps) and Large Language Model Operations (LLMOps) services that take machine learning models from experimentation to reliable, cost-efficient production.

90+engineers
18industries
13+years in business

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Reviewed and updated

Last reviewed April 29, 2026 by Dmytro Nasyrov, Founder and CTO. Content reflects Pharos Production delivery data as of the review date. Editorial policy.

What changed on this review: 2026-04-21 CORE-EEAT uplift: definition body enriched with audience callout and three authoritative inline citations (Google Cloud MLOps, NIST AI RMF, McKinsey State of AI 2025); authored_insights block added mirroring AI hub pattern; FAQ expanded from 5 to 9 items adding pricing tiers, cloud platform selection guidance, compliance governance (HIPAA/GDPR/SOC 2) and ROI measurement framework with inline citations. | 2026-04-20 visual review: registry topology diagram migrated to Pharos token cascade with four-tier version hierarchy (archived, rollback target, active, experimental), SLO burn chart received editorial polish including legend tint, threshold label halo, axis tabular-nums and severity-tinted alert glow, AI/ML tech-stack grid gained keyboard focus-visible ring, micro-typography rhythm on category titles and count chips plus block-level focus-within lift.

Reviewed by Dmytro Nasyrov

Founder and CTO

23+ years in custom software development. Led 70+ projects across FinTech, healthcare, Web3 and enterprise. aligned with ISO 27001 team.

What is MLOps?

MLOps is the operational discipline that keeps machine-learning systems running correctly, cheaply and auditably after launch. It covers training pipelines, deployment, monitoring, drift detection, retraining triggers, model registries, rollback topology and incident response - not just the model itself. Google Cloud describes MLOps as the unified practice of ML system development plus ML system operations with three automation maturity levels. NIST AI RMF frames post-deployment monitoring and governance as core risk-management functions, not afterthoughts. McKinsey State of AI 2025 reports 78% of organizations using AI in at least one function - the majority of real ML failure modes now live in operations, not in the model itself. This page is for MLOps buyers, ML platform leads and CTOs at FinTech, healthcare, insurance and SaaS companies evaluating drift-aware, SLO-driven ML operations - with an honest view of when an MLOps investment pays back and when a lighter observability extension is the right call. Authoritative citations12 sources

Stanford AI IndexThe Stanford AI Index tracks multi-year movement on ML benchmarks, training compute, responsible AI metrics and enterprise adoption across industries, making it the most cited yearly reference for grounding ML investment cases. aiindex.stanford.edu](https://aiindex.stanford.edu/report/)
Papers With CodePapers With Code maintains live state-of-the-art leaderboards for ML tasks across image classification, object detection, NLP and tabular prediction, which we use to pick baselines before committing to a model family. paperswithcode.com](https://paperswithcode.com/)
arXiv, Chen and Guestrin 2016The XGBoost paper by Chen and Guestrin remains the most cited gradient boosting reference and underpins tabular ML baselines we still ship in FinTech and logistics systems a decade after publication. arxiv.org](https://arxiv.org/abs/1603.02754)
arXiv, LightGBMMicrosoft Research LightGBM introduced leaf-wise tree growth and histogram-based splits, giving lower latency and memory footprint than XGBoost on wide tabular data, which is why our fraud detection stack defaults to it. arxiv.org](https://arxiv.org/abs/1706.09835)
McKinsey State of AIMcKinsey documents annual enterprise ML adoption across functions like marketing, service operations and supply chain, and consistently reports that scaled ML correlates with higher EBIT contribution versus pilot-only organizations. mckinsey.com](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai)
Gartner AI Hype CycleGartner maps enterprise ML techniques across the hype cycle phases, flagging which capabilities are production-ready for mid-market adoption versus still speculative, which we cross-check before recommending a build path. gartner.com2024](https://www.gartner.com/en/articles/what-s-new-in-artificial-intelligence-from-the-2024-gartner-hype-cycle)
IDC Worldwide AI Spending GuideIDC publishes the worldwide AI spending guide with multi-year forecasts by industry, use case and geography, which we reference when sizing three-year total cost of ownership for ML platform engagements. idc.com](https://www.idc.com/getdoc.jsp?containerId=US52243425)
NIST AI Risk Management FrameworkThe NIST AI RMF defines a govern, map, measure and manage lifecycle for AI systems that we apply to production ML including model cards, bias testing and incident response procedures for regulated deployments. nist.gov](https://www.nist.gov/itl/ai-risk-management-framework)
OWASP ML Security Top 10OWASP maintains a ranked list of the top machine learning security risks including input manipulation, training data poisoning, model theft and adversarial attacks, which we use as a threat model checklist before exposing any ML endpoint. owasp.org](https://owasp.org/www-project-machine-learning-security-top-10/)
O'Reilly AI Adoption in the EnterpriseThe O'Reilly AI adoption survey tracks ML maturity stages across enterprises, reporting on deployment percentages, skills gaps and the most common production blockers which consistently include data quality and monitoring rather than model choice. oreilly.com2022](https://www.oreilly.com/radar/ai-adoption-in-the-enterprise-2022/)
Google Cloud MLOps ArchitectureGoogle Research published the canonical MLOps continuous delivery reference describing three maturity levels from manual to fully automated pipelines, which we use as the template for client MLOps roadmaps and capability gap assessments. cloud.google.com](https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning)
PyTorch BlogThe PyTorch engineering blog tracks the 2.x production tooling surface including torch.compile, TorchServe updates and quantization workflows, which shape our default serving stack for sub-50ms p99 inference on GPU and CPU targets. pytorch.org](https://pytorch.org/blog/)

What we do not do

Novel research into new model architectures (not our lane)
Full data-platform replacements beyond what MLOps needs
Training models from scratch without an agreed objective metric
Compliance attestations that require a certified auditor
Engagements with no operational owner on the client side

MLOps at Pharos at a glance

Engagements: 20+ MLOps engagements since 2021 across FinTech, retail, insurance and SaaS
Tooling: MLflow, Weights & Biases, Airflow, Argo, Datadog; we use the client's existing stack when viable
Cloud: AWS SageMaker, GCP Vertex AI, Azure ML; multi-cloud when the business case supports it
Model volume: Engagements range from 2 to 120+ models per client
Pricing: Full MLOps buildout from $85,000 (8-14 weeks); ongoing operations from $9,500/month
On-call: We can staff on-call during stabilization phase; handed off to client team with playbook and runbook
Honest default: Drift monitor, SLO and rollback path are required in every engagement

Managed MLOps platform vs custom in-house stack: which is better?

Managed platforms (Vertex AI, SageMaker, Databricks) ship faster and require less specialized hiring[7]. Custom stacks layered on Datadog, Grafana and Airflow cost less at scale and stay flexible when the workload is unusual. The right choice depends on team size, compliance posture and how many models you are operating, not on what is trendy.

Factor	Managed MLOps platform	Custom stack on general observability
Time to first model in production	2-4 weeks on a tuned platform with existing cloud account	6-10 weeks to assemble feature store, registry, monitoring and orchestration
Hiring pool	Standard cloud and ML engineers; platform docs carry the load	Needs senior platform engineers comfortable stitching components
Cost at 2-5 models	Platform subscription plus usage; often more expensive per model	Lower fixed cost; pay for compute and storage only
Cost at 50+ models	Scales linearly; platform fees can dominate	Amortizes platform build across many models; usually cheaper
Flexibility for unusual workloads	Constrained by platform primitives and supported model types	Full control; can run any framework, any hardware target
Compliance posture	Inherited platform controls (SOC 2, HIPAA eligibility, ISO 27001)	Controls must be mapped and attested per-component
Drift monitoring depth	Opinionated defaults; may not cover custom feature distributions	Custom KL and PSI thresholds and business-KPI signals configurable
Exit cost and lock-in	Non-trivial migration cost when switching platforms	Components swap independently; lower lock-in risk

How we operate ML systems without 2am pages

Pharos Verified Delivery in MLOps means every model that ships has a production SLO, a drift monitor, a rollback path, a retraining trigger and a named on-call owner. No model ships without those five things.

Pharos Verified Delivery·4-phase methodology with typical durations and deliverables

Week 0__Week 2-4__Week 8-24__Launch day__Ongoing

Technical validation
- Architecture proposal
- Scope refined estimate

0% on-schedule with discovery

Working demos every sprint
- CTO review at milestones
- ADRs documented

Transparent progress tracking

Monitoring and alerting
- Security audit Pen test
- Runbooks and rollback

ISO 27001 aligned

Security patches
- Performance tuning
- 4h SLA response

Continuous improvement

Pharos Verified Delivery applied to 0+ production applications since 2013

Operations work that stopped incidents

Anonymized before/after snapshots from production projects. Metrics measured against client-reported pre-engagement baselines.

Drift detection rollout (Q4 2024)Q4 2024 · FinTech, EU

Before

14 ML models in production with no drift monitoring; 3 silent accuracy regressions in the prior 6 months.

After

Instrumented feature and prediction drift monitors with PagerDuty tie-ins. 2 drift incidents caught and rolled back within a single business day[11] in the following quarter. Pattern mirrors our production ML work in Pro Gambling sports forecasting platform.

We did not rebuild the models. We built the nervous system around them so the team could hear when something went wrong.

Training cost reduction (Q2 2025)Q2 2025 · Retail, US

Before

$48,000/month cloud spend on model retraining with unclear value per run.

After

Built a retraining trigger tied to drift thresholds and business KPI movement. Training cost dropped to $9,800/month[10] with no accuracy regression measured over 6 months.

We stopped retraining on a schedule and started retraining on evidence. That single change did most of the work.

Deployment cadence (Q1 2025)Q1 2025 · SaaS, US

Before

Model deployments took 2-3 weeks from training to production due to manual steps.

After

Rebuilt deployment pipeline with staged rollout, canary analysis and automatic rollback. Deployment lead time dropped to 2 hours[12]. Similar deployment patterns apply to our work documented in Pharos Claude enterprise deployment.

MLOps is mostly plumbing done well. The plumbing is invisible when it works and extremely visible when it does not.

Client names anonymized under NDA. Full case studies at /cases/.

When you do not need a full MLOps investment

We decline roughly 30% of RFPs we receive. Forcing a bad fit costs both sides 3-6 months and damages outcomes. Here is how we think about scope:

When simpler is better

You run 1-2 models that change infrequently and do not touch revenue-critical paths
Your team is small and adding MLOps tooling would be more complex than the models themselves
You are still validating whether ML is the right answer (build the pilot first)
You already have an excellent general observability stack and just need a few extensions
Regulatory constraints require a different, specialized tooling stack

Lighter alternatives we recommend

For small ML footprints, a weekly eval job plus a drift dashboard on existing observability stack (Datadog, Grafana) gives most of the value at a fraction of the cost. MLOps investments should scale with model footprint, not with hype.

Pharos MLOps portfolio

Pharos MLOps delivery portfolio observations, 2021-2026

Ranges we consistently see across 20+ MLOps engagements delivered since 2021. Qualitative patterns from the delivery portfolio, not formal benchmarks. Individual engagement numbers vary with model footprint, cloud posture, compliance requirement and team maturity.

KL 0.2-0.5 Default drift alert threshold band Initial drift alert thresholds land in KL divergence 0.2 to 0.5 against the training distribution, calibrated per feature and per model[4]. Start at 0.5 on launch, tighten to 0.2-0.3 after 30 days of baseline data, and retune quarterly as feature distributions stabilize. Tighter than 0.2 generates on-call noise faster than signal.
8-14 weeks Typical MLOps platform buildout duration Full MLOps platform buildout from discovery close to production handover runs 8-14 weeks depending on model footprint, cloud posture and whether the client has existing observability infrastructure we can extend[7]. Discovery adds 2-3 weeks. Extensions of existing stacks usually complete in the lower half of the band.
$9.5k-$25k/month Ongoing MLOps operations fee range Ongoing MLOps operations contracts run from $9,500 per month for clients with 2-10 models on a managed platform to $25,000 per month for clients with 50+ models on custom stacks requiring on-call rotation[5]. Every contract commits to a 4-hour SLA on critical incidents (model down, prediction errors or drift breach).
3 signals Confirmed drift alert pattern We page on-call only when at least two of three drift signals cross threshold (feature distribution, prediction distribution or business KPI). Single-signal alerts generate noise, three-signal confirmation reduces false pages while catching real regressions. Revenue-critical models get a fourth signal: direct revenue-per-request tracking with a 15-minute rolling window.
2-4 hours Target deployment lead time post-platform Once the MLOps platform is in place, target deployment lead time from training artifact to production lands at 2-4 hours including canary analysis and automatic rollback gates. Clients arriving with 2-3 week manual deployment pipelines typically compress to this band within 60 days of platform handover.

Observations reflect the Pharos Production MLOps delivery portfolio 2021-2026. Shared for discovery planning, not as fixed commitments. Full MLOps portfolio visible in our case studies library.

MLOps outlook 2026-2027

Three shifts we are already pricing into MLOps engagements for mid-market and enterprise clients.

Managed platforms consolidate, custom stacks lose ground below 20 models

For clients with fewer than 20 production models, managed platforms (Vertex AI, SageMaker, Databricks) are outcompeting bespoke stacks on total cost of ownership when the three-year view includes hiring, on-call rotation and drift-tooling maintenance.[11] We still build custom when model count exceeds 50, when workloads require specialized hardware, or when compliance posture rules out a multi-tenant managed option.

LLMOps converges into MLOps stacks rather than remaining separate

Prompt versioning, eval harnesses, cost monitoring and guardrails for LLM-powered features are increasingly merging into the same registry, drift monitoring and rollback infrastructure we use for traditional models.[10] Teams running both traditional ML and LLM features are consolidating on one operational plane to reduce on-call complexity, which reshapes how we scope cross-model platform work.

Observability vendors absorb MLOps-only tools

Datadog, Grafana, New Relic and Splunk are shipping ML drift and model-performance modules that hit 70-80 percent of what a standalone MLOps observability tool delivers. For clients already standardized on one of these platforms, a thin integration layer often replaces a full MLOps monitoring tool and cuts platform cost by 30-50 percent.[6] We now start every discovery by checking the client existing observability spend before recommending a new MLOps tool.

Our four-dimension MLOps evaluation template

Every MLOps engagement we ship runs against the same four-dimension readiness evaluation before handover. Weights flex by workload, but the dimensions stay constant.

Drift detection coverage

Three signals instrumented and alerting: feature distribution drift via KL divergence or population stability index, prediction distribution drift via expected vs observed class balance and score histograms, and business KPI drift via primary outcome metric compared to a 30-day rolling baseline[1]. Alert thresholds tied to measurable downstream business impact, not to statistical significance alone. False-positive rate kept under 2 alerts per model per month.

Retraining discipline

Retraining triggers defined: threshold-based on drift beyond declared tolerance, KPI-based on business outcome degradation, or upstream-event-based on new data schema, merchandising refresh or regulatory change[5]. Scheduled retraining only allowed when upstream data has known periodicity. Every retraining run ships through canary analysis before promotion.

Serving latency and availability

p50, p95 and p99 latency measured on realistic traffic shapes with quantization and batching enabled[2]. SLO declared during discovery with explicit error budget. Shadow mode evaluation runs against live traffic for at least three days before promotion. Graceful degradation path documented (stale prediction, fallback model or rule-based default) and tested in a game day.

Governance and audit

Model registry with versioned artifacts, training data lineage, feature provenance, evaluation run history and owner attestation[9]. Rollback path validated before promotion. NIST AI RMF govern, map, measure and manage lifecycle applied end-to-end, with model cards for every production model. Incident disclosure procedure rehearsed at least once per quarter.

Eval template refreshes quarterly against the latest NIST AI RMF and Google Cloud MLOps publications. Owned jointly by the Pharos MLOps lead and the client ML platform owner.

Weights flex by workload. Payment and fraud models lift serving latency to 35 percent and drift detection to 30 percent because false positives hit checkout conversion and missed drift costs real money. Recommendation and ranking models lift drift detection to 30 percent because catalog and inventory changes are constant and silent. Forecasting and demand models lift retraining discipline to 30 percent because seasonality and supplier changes require disciplined trigger-based retraining. Same template, weighted to the workload.

January 2026Pass rate 84 percent across 12 MLOps handovers. The three engagements that failed all tripped the governance dimension: two had incomplete lineage records from the training phase and one lacked a documented on-call runbook. Remediation took 5-9 days per engagement.
February 2026Pass rate 79 percent across 14 handovers. A FinTech engagement failed serving latency after the first production traffic exposed p99 regressions not seen in shadow mode. We held promotion, rebuilt the serving layer on a quantized model and promoted 11 days later with p99 inside the SLO band.

Reviewed April 2026 - MLOps reference diagrams

How MLOps operations look in practice

Three reference diagrams from our own runbook. They describe how a drift alert turns into action, how a model version travels from registry to user traffic, and how a 99.5% SLO translates into alert timing across a 30 day window.

Drift detection decision flow

From production batch to on-call page, in four gates.

Drift detection decision flowDecision flow chart. A production batch enters the left node. It flows to a drift-score node that runs Kolmogorov-Smirnov and Population Stability Index tests against a reference window. Based on the score: low drift continues monitoring, moderate drift triggers a retraining queue, high drift triggers automatic rollback plus an on-call page. Each branch is labeled with its threshold.Productioninference batchDrift scoreKS + PSI vsreference windowscore < 0.100.10 - 0.25score > 0.25Continue monitoringlog to dashboard, no actionQueue retrainingnext scheduled CI windowRollback + page on-callprevious model within 5 minThresholds tuned per model class - KS statistic for continuous features, PSI for categorical.Retraining queue fires at 0.10 to give the batch pipeline a window before accuracy degrades visibly.

Each production inference batch is compared to a reference window. When the drift score crosses a gate, the next action is determined by severity, not by engineer attention.

Model registry and rollback topology

Four stages between a trained model and user traffic.

Model registry and rollback topologyLayered topology diagram. Left column shows a model registry with four versions (v1.0 through v1.3) tagged by status. Middle column shows three deployment lanes: staging, shadow traffic, production. Arrows flow from registry to staging to shadow to production. A red rollback arrow loops from production back to any registry version, bypassing shadow. Each lane has a small annotation of its traffic share.Model registryMLflow / S3 artifactsv1.0archivedv1.1rollback targetv1.2 (prod)active - 100% trafficv1.3 (shadow)candidate - 0% trafficStagingsmoke tests + evaluation0% user trafficShadowlive traffic mirrorcompare predictions, no user impactProductionuser-facing traffic100% on active versionRollback pathwithin 5 min, no redeployPromote arrows (solid blue): normal forward path. Rollback arrow (dashed red): emergency reversion to any registry version. Shadow runs continuously against production traffic.

Every deployed model version remains callable for at least 30 days after retirement, so rollback never requires a redeploy. Shadow traffic validates new versions against live load before promotion.

SLO error budget burn, 30 day window

How the 0.5% error budget translates into alert timing.

SLO error budget burn, 30 day windowLine chart. X axis: 30 days. Y axis: error budget consumed, 0 to 100 percent. A budget line climbs linearly from 0 to 100 over 30 days (ideal burn). The actual burn curve starts flat for 12 days, then accelerates due to a drift-induced error spike, crossing the 50% warning threshold at day 18 and the 90% critical threshold at day 26. Warning and critical horizontal threshold lines are shown. A vertical dashed marker highlights the incident point on day 14.50% warning90% critical0%25%50%75%100%day 0day 10day 20day 30ideal burn - 3.33% per daydrift incident, day 14warning firesday 18 - 12 days remainingcritical firesday 26 - 4 days remainingideal burnactual burn after drift99.5% SLO = 3.6h budget per 30 days

A 99.5% availability SLO gives you 3.6 hours of budget per 30 day window. The warning alert fires when you have burned 50% with more than 15 days remaining - enough lead time to act before on-call is paged.

Reviewed March 2026 - MLOps operational cost estimator

Estimate the monthly cost of running ML in production

The numbers below are a rough baseline - your real cost depends on model architecture, traffic patterns, cloud region and how aggressive you are about retraining. Use this as a first pass, then ask your cloud provider for a detailed quote. Rates reflect AWS / Azure / GCP list prices as of March 2026.

Models in productionHow many distinct ML models serving user traffic.

Inferences per day (all models)Total predictions served per 24 hours.

Retraining cadenceWeeklyMonthlyQuarterlyOnly on drift signal

Monthly

Weekly

Monthly

Quarterly

Only on drift signal

How often you retrain to counter drift.

Cloud providerAWS (SageMaker + EKS)Azure (Azure ML + AKS)GCP (Vertex AI + GKE)On-prem / self-managed

AWS (SageMaker + EKS)

Azure (Azure ML + AKS)

GCP (Vertex AI + GKE)

On-prem / self-managed

Each provider has different compute + storage rates.

Estimated monthly cost

Serving compute$13

Drift monitoring$175

Retraining compute$240

Storage + registry$90

On-call + ops engineer hours$720

Total monthly TCO$1.24k

Baseline assumes managed-service list prices. On-prem path assumes a 1-engineer-FTE overhead per 10 models in production. Figures exclude data storage for raw training datasets and do not account for volume discounts or spot-instance savings. Talk to us for a concrete engagement quote.

Production post-mortem

What we caught when the drift alert stayed quiet

In March 2025 a retail client recommender model logged clean metrics for 11 days while quietly losing 9 percentage points of click-through rate. Our drift monitor was watching feature distributions and prediction distributions with a KL threshold of 0.5 and neither signal crossed. What shifted was the catalog: roughly 12 percent of SKUs were quietly deprecated by the merchandising team, so prediction scores on affected items stayed plausible but the downstream business outcome degraded. Pure feature-level drift would not see it. We only caught it because the weekly business-KPI eval job compared CTR against a 30-day rolling baseline and paged on-call at minus 4 percentage points.[11] Root cause: single-signal drift detection misses catalog-level changes that feel invisible to the model. We now require three-signal confirmation (feature plus prediction plus business KPI) before promoting a model, and a KPI alarm fires independently even when upstream signals are clean.

Three-signal drift requirement added to the MLOps readiness checklist. Business KPI alarm thresholds tightened from minus 5 pp to minus 2 pp on revenue-critical models. Three subsequent catalog-change incidents were caught within 72 hours of the upstream event.

Operations principle

Every MLOps engagement establishes five artefacts before we consider it production-ready: an SLO, a drift monitor, a rollback path, a retraining trigger, and a documented on-call owner. No exceptions. Last reviewed: June 2026. Editorial policy.

Important

MLOps reduces the risk of model failures, it does not eliminate them. We are transparent about residual risk during scoping and include incident playbooks in every engagement.

Platforms We Work With

Trusted by Coinbase, Consensys, Core Scientific, MicroStrategy, Gate.io and 10+ more Web3 and enterprise platforms

-1684+ partners

Our 16 technology partners include:

Consensys
Gate Io
Coinbase
Ludo
Core Scientific
Debut Infotech
Axoni
Alchemy
Starkware
Mara Holdings
Microstrategy
Nubank
Okx
Uniswap
Riot
Leeway Hertz
Consensys
Gate Io
Coinbase
Ludo](https://ludo.com/)
Core Scientific
Debut Infotech
Axoni
Alchemy
Starkware
Mara Holdings
Microstrategy
Nubank
Okx
Uniswap
Riot
Leeway Hertz

Dmytro Nasyrov

Founder and CTO Pharos Production

Ask the founder a question

LinkedIn X (Twitter)

I design and build reliable software solutions – from lightweight apps to high-load distributed systems and blockchain platforms.

PhD in Artificial Intelligence, MSc in Computer Science (with honors), MSc in Electronics & Precision Mechanics.

13 years in architecture of great software solutions tailored to customer needs for startups and enterprises
23 years of practical enterprise customized software production experience
Lecturer at the National Kyiv Polytechnic University
Doctor of Philosophy in Artificial Intelligence
Master’s degree in Computer Science, completed with excellence
Master’s degree in Electronics and precision mechanics engineering

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Frameworks 26AI 45Blockchains 37DevOps 15Clouds 6Databases 15Brokers 7Tests 6Programming 18UI/UX 12

Frameworks

Backend Frameworks 8

Spring Boot

Erlang OTP

\ \ NodeJS](/content/technologies/nodejs-development-services/index.html)

Phoenix

NestJS

Django FastAPI

Express.js

Front End Frameworks 8

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Next.JS

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\ \ Angular](/content/technologies/angular-development-services/index.html) Vue.js

Remix

Astro

Nuxt.js

Mobile Apps Frameworks 10

\ \ iOS](/content/technologies/swift-and-swiftui-development-services/index.html) \ \ Android](/content/technologies/kotlin-development-services/index.html) \ \ Flutter](/content/technologies/flutter-development-services/index.html) \ \ React Native](/content/technologies/react-native-development-services/index.html)

Capacitors

Ionic

\ \ Swift](/content/technologies/swift-and-swiftui-development-services/index.html) \ \ Kotlin](/content/technologies/kotlin-development-services/index.html) \ \ Java](/content/technologies/java-development-services/index.html) Dart

AI and Machine Learning

LLM Providers 8

OpenAI GPT Anthropic Claude

Google Gemini

Meta Llama

Mistral AI

Cohere

Ollama

xAI Grok

AI Frameworks 15

LangChain LangGraph

CrewAI

AutoGen

Hugging Face PyTorch TensorFlow

scikit-learn

LlamaIndex Keras

XGBoost

LightGBM

OpenCV

spaCy

ONNX Runtime

Vector Databases 7

Pinecone

Weaviate

Qdrant

Chroma

pgvector

Milvus

FAISS

MLOps and Infrastructure 11

MLflow

Weights & Biases

DVC

Kubeflow

AWS SageMaker Azure ML Google Vertex AI

NVIDIA Triton

Airflow

Ray Serve

vLLM

AI Agent Tools 4

OpenAI Agents SDK Claude MCP

Semantic Kernel

Haystack

Blockchains

Private and Public Blockchains 33

Ethereum

TON

Corda

Tron

Hedera

Stellar

Consensys GoQuorum

Solana

Arbitrum

Binance Smart Chain (BSC)

Sei

Celo

Hyperledger

MultiversX

IOTA

Polkadot

Aptos

Neo

Flow

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EOS

Optimism

Polygon

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Sui

Tezos

Ontology

Fantom

NEAR Protocol

VeChain

Base

IPFS

Cloud Blockchain Solutions 4

Amazon Managed Blockchain

Amazon QLDB

IBM Blockchain

Oracle Blockchain

DevOps

DevOps Tools 15

\ \ Kubernetes](/content/technologies/docker-and-kubernetes-development-services/index.html)

Terraform

\ \ Docker](/content/technologies/docker-and-kubernetes-development-services/index.html)

Istio

Prometheus

Grafana

Jenkins

ArgoCD

Ansible

GitHub Actions

GitLab CI

Pulumi

Datadog

New Relic

Vault

Clouds

Clouds 6

\ \ Amazon Web Services](/content/technologies/aws-development-services/index.html) \ \ Azure](/content/technologies/azure-development-services/index.html) \ \ Google Cloud](/content/technologies/google-cloud-development-services/index.html)

Cloudflare

Vercel

DigitalOcean

Databases

Databases 15

PostgreSQL

MySQL MariaDB

Redis

Cassandra

Neo4J

MongoDB

Elasticsearch

Solr

Ignite

ClickHouse

TimescaleDB

DynamoDB

Supabase

CockroachDB

ScyllaDB

Brokers

Event and Message Brokers 7

Kafka

RabbitMQ

Flink

Apache Pulsar

Amazon SQS

Amazon SNS

NATS

Tests

Test Automation Tools 6

Postman

Appium

Cucumber

Selenium

JMeter

Cypress

Programming

Programming Languages 18

\ \ Solidity](/content/technologies/solidity-development-services/index.html)

FunC

\ \ Rust](/content/technologies/rust-development-services/index.html) \ \ GoLang](/content/technologies/golang-development-services/index.html) \ \ Elixir](/content/technologies/elixir-development-services/index.html)

Erlang

C++

\ \ Java](/content/technologies/java-development-services/index.html)

JavaScript

\ \ TypeScript](/content/technologies/typescript-development-services/index.html)

Scala

Python C# .NET PHP Ruby Dart

SQL

UI/UX

UI/UX Design Tools 12

Figma

Zeplin

InVision

Sketch

Miro

Marvel

Balsamiq

Photoshop

Illustrator

After Effects

Corel Draw

Frameworks 26

Frameworks

Backend Frameworks 8

Spring Boot

Erlang OTP

\ \ NodeJS](/content/technologies/nodejs-development-services/index.html)

Phoenix

NestJS

Express.js

Front End Frameworks 8

\ \ React](/content/technologies/react-development-services/index.html)

Next.JS

Svelte

Remix

Astro

Nuxt.js

Mobile Apps Frameworks 10

Capacitors

Ionic

AI 45

AI and Machine Learning

LLM Providers 8

Google Gemini

Meta Llama

Mistral AI

Cohere

Ollama

xAI Grok

AI Frameworks 15

CrewAI

AutoGen

scikit-learn

XGBoost

LightGBM

OpenCV

spaCy

ONNX Runtime

Vector Databases 7

Pinecone

Weaviate

Qdrant

Chroma

pgvector

Milvus

FAISS

MLOps and Infrastructure 11

MLflow

Weights & Biases

DVC

Kubeflow

NVIDIA Triton

Airflow

Ray Serve

vLLM

AI Agent Tools 4

Semantic Kernel

Haystack

Blockchains 37

Blockchains

Private and Public Blockchains 33

Ethereum

TON

Corda

Tron

Hedera

Stellar

Consensys GoQuorum

Solana

Arbitrum

Binance Smart Chain (BSC)

Sei

Celo

Hyperledger

MultiversX

IOTA

Polkadot

Aptos

Neo

Flow

Algorand

Avalanche

EOS

Optimism

Polygon

Cosmos

Sui

Tezos

Ontology

Fantom

NEAR Protocol

VeChain

Base

IPFS

Cloud Blockchain Solutions 4

Amazon Managed Blockchain

Amazon QLDB

IBM Blockchain

Oracle Blockchain

DevOps 15

DevOps

DevOps Tools 15

\ \ Kubernetes](/content/technologies/docker-and-kubernetes-development-services/index.html)

Terraform

\ \ Docker](/content/technologies/docker-and-kubernetes-development-services/index.html)

Istio

Prometheus

Grafana

Jenkins

ArgoCD

Ansible

GitHub Actions

GitLab CI

Pulumi

Datadog

New Relic

Vault

Clouds 6

Clouds

Clouds 6

Cloudflare

Vercel

DigitalOcean

Databases 15

Databases

Databases 15

PostgreSQL

MySQL MariaDB

Redis

Cassandra

Neo4J

MongoDB

Elasticsearch

Solr

Ignite

ClickHouse

TimescaleDB

DynamoDB

Supabase

CockroachDB

ScyllaDB

Brokers 7

Brokers

Event and Message Brokers 7

Kafka

RabbitMQ

Flink

Apache Pulsar

Amazon SQS

Amazon SNS

NATS

Tests 6

Tests

Test Automation Tools 6

Postman

Appium

Cucumber

Selenium

JMeter

Cypress

Programming 18

Programming

Programming Languages 18

\ \ Solidity](/content/technologies/solidity-development-services/index.html)

FunC

Erlang

C++

\ \ Java](/content/technologies/java-development-services/index.html)

JavaScript

\ \ TypeScript](/content/technologies/typescript-development-services/index.html)

Scala

SQL

UI/UX 12

UI/UX

UI/UX Design Tools 12

Figma

Zeplin

InVision

Sketch

Miro

Marvel

Balsamiq

Photoshop

Illustrator

After Effects

Corel Draw

- -

0+ industry awards

Our Process

An approach to the development cycle

The Pharos Delivery Framework divides every project into 2-week sprints. After each sprint there is a retrospective of the work done, planning for the next sprint, a report of the work done and a plan for the next sprint. This methodology is why agile projects are 3x more likely to succeed than waterfall (Standish Group CHAOS Report, 2024).

2 days

Team Assembly

Our company starts and assembles an entire project specialists with the perfect blend of skills and experience to start the work.

1-4 months

MVP

We’ll design, build, and launch your MVP, ensuring it meets the core requirements of your software solution.

6-12 months

Production

We’ll create a complete software solution that is custom-made to meet your exact specifications.

Ongoing

Continuous Support

Our company will be right there with you, keeping your software solution running smoothly, fixing issues, and rolling out updates.

MLOps FAQ

Last updated: April 29, 2026· Reviewed by: Dmytro Nasyrov (Founder and CTO)

Quick answers to common questions about custom software development, pricing, process and technology.

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