THE BUYER’S FIELD GUIDEAt 2 a.m., the database alarm sounds. By breakfast, finance has three versions of yesterday’s revenue. At noon, someone demonstrates an AI assistant that cannot touch the systems it is supposed to improve. Each problem needs expertise; buying the same expertise for all three would be an expensive comedy.
Pythian is easy to remember as the database company. Its current offer reaches further: cloud infrastructure, analytical platforms, business intelligence, machine learning and AI embedded in everyday work. This guide groups that scope into eight buying paths. Start with the failure you can describe, then decide whether you need a bounded project, continuing operational cover, or both. A service name is a door, not a diagnosis.
What brought you here?
Database consulting
Define a repair project; add DBA cover if the operational gap continues.
Explore this serviceA reading map of the services covered in this guide.
01 / Database consulting: stop the recurring incident
Queries slow down at the same hour. Locks pile up. A recovery plan exists, but nobody has proved it works. Database consulting is the project route for assessment, root-cause work, performance tuning, architecture changes and database migration expertise. Pythian publicly supports Oracle, Microsoft SQL Server, PostgreSQL and MySQL, among other engines.
Ask for the bottleneck, the proposed fix and evidence that the fix survives your actual workload. Harvard Business Publishing’s multi-database story illustrates the breadth of expertise available; its engagement was managed support, so use it as evidence of operational depth rather than a like-for-like consulting project.
02 / Managed DBA: make the night shift somebody’s job
When the immediate fix is finished but the on-call rota remains impossible, consider managed DBA services. This is ongoing database administration: monitoring, maintenance, patching, recovery planning and incident response. Buying a consulting project does not itself establish continuing coverage.
Harvard Business Publishing used Pythian’s remote DBAs across several database technologies. Pythian reports $700,000 in annual operational savings and 24/7 coverage. For your own brief, spell out supported engines, escalation responsibilities, recovery objectives and response commitments. The elegant contract is the one that still makes sense during an outage.
Pythian is a great remote DBA partner. They’re a part of our team and they make us successful at Harvard Business Publishing.
Stefano McGhee · Senior Manager of DevOps
Harvard Business Publishing · Customer story
03 / Cloud migration: move without losing the business
A hosting deadline approaches. Peak demand exposes fixed capacity. An acquisition leaves several clouds and no coherent operating plan. Cloud migration consulting covers assessment, target architecture, migration planning and execution across Google Cloud, AWS, Microsoft Azure and Oracle Cloud Infrastructure.
AllSaints moved a complex estate to Google Cloud, with Pythian reporting 75% operational cost savings and 35% faster page loads. Its work included automated infrastructure and deployment pipelines. A buyer should specify dependency mapping, rehearsals, rollback and acceptance criteria before cutover. Arrange DevOps or infrastructure ownership while the migration team still knows where the awkward dependencies live.
04 / Data warehouse consulting: repair the foundations
When departments keep their data in separate kingdoms, analysis becomes diplomacy. Data warehouse consulting addresses the shared platform underneath: architecture, integration, migration, query performance and governance. Pythian lists BigQuery, Snowflake, Databricks and Microsoft Fabric among its modern platform options.
Openforce provides a useful buying pattern. Separate SQL Server databases fed a centralized Snowflake warehouse through AWS Glue pipelines. The design retained AWS and connected to Power BI. That sequence matters: establish dependable data and agreed metrics before polishing the charts. Set a freshness target, test reconciliation and identify who will repair a broken overnight load.
05 / BI consulting: give the meeting one set of numbers
A warehouse can be healthy while its reports remain bewildering. Choose BI consulting when the obstacle is metric definitions, dashboards, reporting workflows or adoption. Pythian’s published scope includes assessment, automated pipelines, visualization and training, with tools including Power BI, Tableau and Looker.
Openforce is also relevant here: warehouse engineering and Power BI reporting belonged to the same buying journey. Pythian says data requests moved from weeks to minutes. Define the decisions each dashboard must support, who owns each metric and which users need training. Then establish DataOps coverage for the pipelines and reporting platform. A beautiful chart with stale inputs is merely well-dressed misinformation.
06 / Production AI: get the pilot into the working day
The demo wins applause. Deployment wins a list of unanswered questions: access rights, live data, integration, cost, evaluation and human review. Production AI consulting tackles that passage from an isolated prototype to an operational workflow, including data readiness, system integration, deployment and ongoing performance management.
Day & Ross used Google Cloud and Gemini to extract information from bills of lading and connect it to its transportation management system. Pythian reports 99% data accuracy and 120 documents processed per minute. For your use case, agree acceptable errors, review thresholds and a business measure before launch. A model’s cleverness matters less than whether the next shipment can move.
Operational cost savings
AllSaints ↗
Annual operational savings
Harvard Business Publishing ↗
Document data accuracy
Day & Ross ↗
Figures reported by Pythian for these customer engagements.
07 / Machine learning: investigate the prediction that disappoints
If a live model predicts poorly, swapping the cloud logo will not repair its judgment. Machine learning and AI development is the more precise buying path for data preparation, model architecture, training, evaluation and deployment. Begin with an agreed baseline and separate model quality from serving latency.
In its Busbud case study, Pythian describes improving a predictor for price and seat-availability changes under supplier API constraints, using TensorFlow expertise and revised evaluation methods. It reports improved accuracy without a percentage uplift. After model acceptance, MLOps should monitor quality and drift, manage versions and define retraining triggers. The real world has a habit of changing the examination paper.
08 / Google Workspace: turn Gemini access into use
Licenses are purchased. Employees keep doing the same tasks the same way. Google Workspace consulting is the buying path for collaboration migration, security, change management and Gemini enablement. Role-specific workflows, prompt practice and internal champions address a people problem that provisioning alone cannot solve.
Pythian’s Gemini QuickStart customer study describes employee surveys, practical workshops and follow-up measurement; it reports savings of up to four hours per month or more on routine tasks. Start with a department and a repeatable task, then measure use and time saved. Ongoing Workspace support and an adoption program can carry the rollout forward.
Choose the workload, then the platform
Pythian publicly works across Google Cloud, Oracle, AWS and Microsoft; its analytical platform pages also cover BigQuery, Snowflake, Databricks and Microsoft Fabric. These names occupy different layers of a system. Openforce’s combination of AWS, Snowflake and Power BI is a useful reminder that a buying decision can cross ecosystems.
Compare existing investments, data location, security requirements, SQL reporting demand, engineering workloads, model needs and operating costs. Ask the team to explain the choice against those constraints. The Databricks and Microsoft Fabric service pages establish public capability; they do not make either platform the automatic answer.
Baseline, risks, priorities
Implement, validate, accept
DBA · DataOps · DevOps
MLOps · AI support
Write the handoff into the opening brief
An assessment should end with prioritized work. A build or migration should end with a tested service and a named operator. Database ownership maps to DBA support; data pipelines and analytical platforms to DataOps; deployment automation and infrastructure workflows to DevOps; model monitoring and retraining to MLOps. Live AI may also need AI managed support for integrations, safeguards and running costs.
Before signing, request deliverables, success measures, access arrangements, runbooks, escalation rules and the operational boundary with your internal team. Ask what happens on the first ordinary Tuesday after go-live. That is when the buyer’s guide becomes a business decision.
Before you write the brief
Is Pythian only a database services company?
No. Its public services span databases, cloud migration, data platforms, BI, machine learning, production AI and Google Workspace.
Should I choose database consulting or managed DBA?
Choose consulting for a defined assessment, repair or architecture project. Choose managed DBA when you need continuing administration, monitoring and operational coverage.
How do data warehouse consulting and BI consulting differ?
Warehouse work focuses on the shared data platform and its performance. BI work focuses on metrics, reporting, dashboards and how people use them. One project can require both.
Which platforms does Pythian publicly support?
Its services cover Google Cloud, AWS, Microsoft Azure and Oracle, plus analytical platforms including BigQuery, Snowflake, Databricks and Microsoft Fabric. Confirm the precise scope for your workload.
What support should follow an AI launch?
Define ownership for data pipelines, model monitoring, drift and retraining, infrastructure, AI safeguards and cost. Depending on scope, this can involve DataOps, DevOps, MLOps and AI managed support.
Service scope and customer results come from Pythian’s published pages and case studies, linked throughout. Research checked October 2, 2026.
About Pythian ↗