Step-by-Step Guide to Fine-Tune an AI Model

Estimated reading time: ~ 8 minutes.

Key Takeaways

• Fine-tuning enhances the performance of pre-trained AI models for specific tasks.
• Both TensorFlow and PyTorch are robust frameworks that facilitate the fine-tuning process.
• Proper data preparation is crucial for effective model training and evaluation.
• Hyperparameter tuning and careful monitoring can prevent overfitting during training.
• Numerous real-world applications demonstrate the effectiveness of fine-tuned models.

Table of Contents

• Introduction
• Prerequisites
• Selecting a Pre-Trained Model
• Preparing Your Data
• Fine-Tuning Setup
• Fine-Tuning Process
• Evaluating the Model
• Saving and Exporting the Model
• Real-World Application Examples
• Conclusion
• Additional Resources

How can machine learning models create accurate predictions even with minimal data? The answer lies in fine-tuning pre-trained models, a technique that leverages existing knowledge for new tasks. By adjusting these models, practitioners can achieve impressive results, often faster and with less data than building a model from scratch. In this guide, we’ll explore the fine-tuning process using two popular frameworks: TensorFlow and PyTorch, and provide actionable steps to help you successfully embark on this journey.

Introduction to Fine-Tuning

Fine-tuning involves taking a pre-trained AI model—one that has already learned patterns from a vast dataset—and adapting it for a specific application. This process allows users to benefit from the model's existing knowledge while making adjustments for unique requirements. The use of fine-tuned models significantly reduces the time and data needed for training, providing better performance in targeted tasks, such as image recognition or natural language processing.

Prerequisites

Before diving into the fine-tuning process, it's important to have a fundamental understanding of machine learning concepts. Ensure you have the following:

• Software: Python, TensorFlow, and PyTorch installed.
• Environment setup: Jupyter Notebooks or an Integrated Development Environment (IDE) for coding.

Selecting a Pre-Trained Model

Choosing the right pre-trained model is essential for achieving optimal results. Various models serve different tasks:

• TensorFlow Hub and PyTorch Hub offer a plethora of pre-trained options suitable for diverse applications.
• Research the models available, selecting one that aligns with your project goals.

Preparing Your Data

Data quality and quantity are paramount when fine-tuning a model. Follow these critical steps:

1. Data collection: Gather relevant datasets for your task.
2. Preprocessing: Implement cleaning and augmentation techniques to enhance dataset quality.
3. Data splitting: Divide your dataset into training, validation, and test sets for effective evaluation.

Fine-Tuning Setup

In this phase, you set the stage for training:

• Load the pre-trained model: Utilize either TensorFlow or PyTorch to access your chosen model.
• Modify the architecture: Tailor the model for your particular application by adding layers or changing existing ones.
• Loss functions and optimizers: Set these up according to your task needs.

Fine-Tuning Process

Now, it's time to fine-tune your model:

• Adjust hyperparameters: Modify settings like learning rate and number of epochs.
• Training: Utilize your prepared dataset to train the model.
• Monitoring: Use callbacks to implement early stopping and track performance metrics.

Evaluating the Model

After training, evaluating the model's performance is critical:

• Use various metrics such as accuracy, F1 score, and confusion matrices to assess effectiveness.
• Watch for overfitting by comparing training and validation results.
• Visualize metrics to grasp performance across epochs effectively.

Saving and Exporting the Model

Once satisfied with the model’s performance, saving it for future use is important:

• Save your fine-tuned model using formats offered by TensorFlow (e.g., SavedModel) or PyTorch (e.g., TorchScript).
• Consider deployment requirements when exporting to different environments.

Real-World Application Examples

Fine-tuned models have gained traction in many sectors. Examples include:

• Image classification: Identifying objects within images using convolutional neural networks.
• Sentiment analysis: Analyzing text data to determine reader sentiment, which can be valuable in market research.

Conclusion

Fine-tuning an AI model using TensorFlow or PyTorch can exponentially enhance your machine learning capabilities. By leveraging pre-trained models and adapting them for specific tasks, you can achieve remarkable outcomes with fewer resources. As you experiment with different datasets and fine-tuning approaches, you will discover the true potential of AI.

Find Out more: Book a 15-minute consult with Massimo Bensi

Watch a video about Fine-Tuning AI Models here.

https://massimobensi.com

Frequently Asked Questions (FAQ)

Q: Why is fine-tuning important in AI?

A: Fine-tuning allows models to leverage existing knowledge for better performance on specific tasks while reducing training time and data requirements.

Q: What framework is better for fine-tuning, TensorFlow or PyTorch?

A: Both frameworks are popular and have their strengths; the choice depends on your project needs and familiarity with the tools.

Q: How do I prepare my data for fine-tuning?

A: Ensuring data quality through cleaning and augmentation, and dividing it into training, validation, and test sets are key steps.

Q: What should I do if my model is overfitting?

A: Monitor training and validation metrics, adjust hyperparameters, and utilize techniques like dropout and early stopping.

Q: How can I evaluate my fine-tuned model?

A: Common metrics like accuracy, F1 score, and visual examination of confusion matrices provide insights into performance.

Q: Can I fine-tune models for non-image tasks?

A: Absolutely! Fine-tuning is applicable to various domains, including NLP and tabular data analysis.

Q: How do I save a fine-tuned model?

A: Use the save functionalities provided by TensorFlow or PyTorch to export your model in the desired format for future use.

Q: Are there pre-trained models for my specific task?

A: Many pre-trained models are available on TensorFlow Hub and PyTorch Hub catering to a variety of tasks and applications.

Q: How do hyperparameters affect model performance?

A: Hyperparameters, like learning rate and epochs, significantly influence training speed and final accuracy. Tuning these is crucial for optimal performance.

Q: How often should I monitor training metrics?

A: Regular monitoring during training will help you identify issues like overfitting early and allow for timely adjustments to your approach.

Should You Self-Host Your Large Language Model?

Estimated Reading time: ~ 4 minutes

Key Takeaways:

• Data security and compliance are major drivers for self-hosting: if you handle sensitive or regulated data (PII, PHI, financial, etc.), using a public API creates risk of data exposure, reuse, or non-compliance.

• Regulatory constraints such as HIPAA, GDPR, PCI, GLBA often make self-hosting the only viable path for enterprises in heavy-regulation sectors.

• Customization and performance: Self-hosting allows control over latency, uptime, tuning, model fine-tuning on your proprietary data—leading to differentiation.

• Cost model shift: While public-API LLMs are cheaper initially, with scale (large token volumes) the per-token costs can balloon; self-hosting has higher upfront investment but becomes more predictable and cost-efficient long-term.

• Strategic decision criteria: Self-hosting is not for every organization – but for those with regulated workloads, high volume usage, desire for customization, and need for control (cost/vendor/lock-in) it becomes a strategic imperative. 

Table of Contents / Structure:

• Data Security and Compliance: The Non-Negotiable

• Regulatory Requirements: From Risk Exposure to Risk Elimination

  • HIPAA

  • GDPR

• Customization & Performance: Turning AI Into a Competitive Asset

  • Full Control Over Performance 

  • Fine-Tuning for Your Business 

• Long-Term Cost Strategy: Controlling the Economics of AI

  • The API Cost Curve

  • The Self-Hosted Curve

• Executive Summary: When Self-Hosting Makes Strategic Sense

• Frequently Asked Questions (FAQ)

As AI adoption accelerates, organizations face a pivotal decision: run Large Language Models (LLMs) in-house or rely on public APIs. Both approaches can unlock efficiency and innovation, but the choice ultimately determines how much control—versus complexity—you’re willing to own.

For business leaders, three drivers usually shape the decision: data protection, model customization, and long-term cost strategy.

 

Data Security and Compliance: The Non-Negotiable

For most enterprises—particularly those handling sensitive, proprietary, or regulated information—data protection is the biggest reason to self-host.

When your teams use a public LLM API, every prompt and response is processed on external infrastructure. That carries unavoidable risks:

• Exposure risk: Any third-party provider can experience a breach, potentially exposing data such as customer records, financial information, or intellectual property.

• Data-use uncertainty: Some providers reserve the right to use customer inputs to improve their models. Even if disabled, executives must trust a vendor’s internal controls.

• Regulatory landmines: Strict frameworks like HIPAA, GDPR, PCI, and GLBA impose non-negotiable requirements on how data—especially PII or PHI—is stored, processed, and transferred. Simply sending sensitive data to a non-certified external system can violate compliance rules.

Self-hosting eliminates these variables. All processing occurs within your secured perimeter—your data center or private cloud—allowing complete control over how sensitive information moves through your systems.

For industries like banking, insurance, healthcare, defense, and global retail, this control isn’t optional. It’s the only acceptable way to work with customer data, internal analytics, or proprietary documents.

Regulatory Requirements: From Risk Exposure to Risk Elimination

Executives in regulated sectors face increasing scrutiny around how AI systems handle personal or confidential information. Two regulations illustrate why many organizations simply cannot use public APIs:

HIPAA

Hospitals or health systems cannot send Protected Health Information to any service that is not a certified Business Associate. Many public LLM providers won’t sign the required agreements or cannot meet the full compliance obligations.

Self-hosting circumvents the issue: PHI never leaves the organization’s controlled infrastructure.

GDPR

GDPR introduces several challenges for public AI APIs:

• Data cannot be transferred outside approved regions.

• Users have the “right to be forgotten.”

• Personal data cannot be repurposed (e.g., for model training) without explicit consent.

A self-hosted LLM can be deployed entirely within EU servers, enforce data-retention rules, and ensure compliant deletion—capabilities public APIs can’t guarantee.

For leadership teams, self-hosting moves AI compliance from a legal gray zone to a clear, controllable strategy.

Customization & Performance: Turning AI Into a Competitive Asset

Public APIs offer convenience, but they are inherently generic. For organizations aiming to differentiate through AI, self-hosting unlocks deeper strategic advantages:

Full Control Over Performance

Public API performance depends on provider load, network conditions, and rate limits. For mission-critical applications—customer support automation, internal coding assistants, or real-time decision tools—latency or downtime becomes a business risk.

Self-hosting lets you optimize the hardware, tune performance, and guarantee uptime.

Fine-Tuning for Your Business

Open-source models like Llama or Mistral, or even the latest OpenAI GPT-OSS, can be fine-tuned on your proprietary data:

• legal contracts

• service agreements

• product documentation

• internal knowledge bases

• customer conversations

The result is a model that doesn’t just understand your domain—it understands your company.

This creates something no competitor can buy: an AI asset that reflects your vocabulary, risk tolerance, workflows, and institutional knowledge.

For executives looking to create lasting advantage, this is often the most compelling case for self-hosting.

 

Long-Term Cost Strategy: Controlling the Economics of AI

At small scale, public APIs are the cheaper, faster way to get started. But as usage grows, the economics shift.

The API Cost Curve

Public LLMs charge per token. As internal usage increases—engineering assistants, customer support bots, analytics tools—token consumption grows linearly. Organizations generating billions of tokens per month can face unpredictable and rapidly escalating monthly costs.

The Self-Hosted Curve

Self-hosting requires a substantial initial investment in GPUs, infrastructure, and ML operations talent. But once deployed:

• per-inference costs drop dramatically

• monthly costs become predictable

• hardware becomes a long-term asset, not a recurring fee

For high-volume enterprises, the breakeven point arrives quickly. Over a multi-year period, self-hosting often delivers superior total cost of ownership.

Just as importantly, it removes dependency on external vendors whose pricing or model behavior can change at any time.

 

Executive Summary: When Self-Hosting Makes Strategic Sense

Self-hosting isn’t for every organization. But for enterprises that meet any of the following criteria, it often becomes the only viable choice:

• You operate in a heavily regulated environment.

• You handle large volumes of sensitive or proprietary data.

• You want material competitive advantage from AI customization.

• Your long-term AI usage will be high or mission-critical.

• You want control over cost predictability—and freedom from vendor lock-in.

In these cases, self-hosting moves from being a technical option to a strategic imperative.

Book a call to find out more about LLM usage via Public API vs Self-Hosted.

Watch a video on self-hosting LLM.

massimobensi.com

Frequently Asked Questions (FAQ)

Q: What does it mean to self-host a Large Language Model?

A: Self-hosting means running an LLM on your own infrastructure—either on-premises or in a private cloud—rather than sending data to an external AI provider. This gives organizations full control over data flows, model behavior, and system performance.

Q: Why do companies choose to self-host LLMs instead of using public APIs?

A: Organizations self-host LLMs primarily to protect sensitive data, meet regulatory requirements, customize the model for proprietary workflows, and gain long-term cost control. It’s a strategic move for enterprises that cannot rely on external vendors for confidentiality, compliance, or predictable pricing.

Q: Is self-hosting an LLM more secure than using a public API?

A: For most enterprises, yes. Self-hosting keeps all processing within your secured environment, preventing sensitive information—such as PII, PHI, financial data, or IP—from leaving your network. This dramatically reduces exposure to breaches, data misuse, or non-compliance.

Q: Does self-hosting help with GDPR or HIPAA compliance?

A: Absolutely. Self-hosting allows organizations to enforce strict data residency, retention, and deletion policies. It also eliminates the need to transfer regulated data to third-party vendors, which is often the biggest compliance barrier under HIPAA, GDPR, PCI, and similar regulations.

Q: Is self-hosting an LLM cheaper in the long run?

A: It can be. Public APIs offer low startup costs but become expensive as usage scales because pricing is tied to tokens. Self-hosted LLMs require significant upfront investment in GPUs and infrastructure, but the per-inference cost drops sharply at high volume. Enterprises generating millions or billions of tokens per month often see strong long-term ROI.

Q: What types of businesses benefit most from self-hosting?

A: Industries that handle sensitive or regulated data—such as banking, insurance, healthcare, government, and large enterprises—see the greatest benefit. High-volume users such as global support centers, engineering organizations, and analytics teams also gain cost and performance advantages.

Q: Can self-hosted LLMs be customized?

A: Yes, and this is one of the biggest advantages. Enterprises can fine-tune open-source models on their own data, creating highly specialized tools that reflect internal terminology, policies, and workflows. This level of customization is not possible with most public APIs.

Q: How difficult is it to operate a self-hosted LLM?

A: Self-hosting requires infrastructure, ML engineering expertise, and ongoing maintenance. For organizations without internal AI operations teams, the complexity can be significant. Many choose managed private-cloud hosting or hybrid approaches to reduce the operational burden.

Q: What are the performance benefits of self-hosting?

A: Self-hosted systems offer lower and more predictable latency, higher uptime guarantees, and better scalability for mission-critical applications. You’re not subject to rate limits, network congestion, or shared-tenant slowdowns from public vendors.

Q: Can self-hosted LLMs prevent vendor lock-in?

A: Yes. Self-hosting allows businesses to switch between open-source models, adjust architectures, or scale hardware independently. This creates long-term strategic flexibility and shields the organization from sudden vendor pricing changes or API policy shifts.

Beyond Spreadsheets: Comparing Four Database-as-a-Service Platforms

Estimated reading time: ~11 minutes

Key takeaways

• Four contenders: Baserow, Airtable, SeaTable, and ClickUp serve different needs, budgets, and compliance profiles. 🎯

• Self-hosting matters: Baserow and SeaTable enable data sovereignty and unlimited scaling on your own infra; Airtable and ClickUp are cloud-only.

• Pricing diverges: Airtable’s polished ecosystem comes at a premium; SeaTable offers EU-friendly value; Baserow shines for cost-conscious technical teams; ClickUp is the all-in-one work hub.

• Feature depth vs. breadth: Airtable leads in integrations/automation; Baserow emphasizes open-source extensibility; SeaTable pairs scale with GDPR; ClickUp prioritizes project ops.

• Pick by use case: Choose based on compliance, team skills, data size, and total cost of ownership—not hype.

Table of contents

• Introduction

• Pricing

• Hosting Options

• Main Features

• Flexibility (Integration Options)

• Conclusion

• Summary Table

• FAQ

Introduction

Excel and Google Sheets have dominated data manipulation for decades. 

But a new wave of Database-as-a-Service (DBaaS) tools blends spreadsheet familiarity with true database power—relational structure, automation, and no-code app building. 

Systems as CRM, PIM, DAM, can be easily and quickly built on using these platforms (read my post about building an online product catalog in 48 hours using AirTable).

This post compares four notable platforms: Baserow (Netherlands), Airtable (United States), SeaTable (Germany/China JV), and ClickUp (United States).

Pricing

Baserow offers cloud and self-hosted options. The Free tier supports unlimited databases with 3,000 rows and 2GB per workspace. Paid tiers start at $10/user/month (Premium) and $20/user/month (Advanced), with self-hosted tiers removing row/storage/history limits.

Airtable raised prices in recent years. Free: 1,000 records/base, 1GB storage, up to 5 editors. Team: $20/user/month (annual), 50,000 records & 20GB/base. Business: $45/user/month, 125,000 records & 100GB/base. Enterprise Scale: custom pricing and 500,000+ records; AI credits included on paid plans.

SeaTable targets EU value: Free with 10,000 rows and 2GB. Plus: €7/user/month with 50,000 rows & 50GB. Enterprise: €14/user/month with 100GB and automations; self-hosted Enterprise with custom pricing and unlimited usage; strong GDPR emphasis.

ClickUp this platform is a project hub, comparable to Notion, but it also offers straightforward database-style views as the other tools in this post. Free Forever (unlimited users/tasks; 100MB storage). Unlimited: $7/user/month; Business: $12/user/month; Enterprise: custom. Optional AI (ClickUp Brain) is +$7/user/month.

Hosting Options

Baserow provides true flexibility: cloud-hosted or fully self-hosted (open-source MIT). Cloud instances in Germany/Amsterdam for GDPR-aligned residency—ideal for strict data sovereignty.

Airtable is cloud-only (primarily US-hosted on AWS). Enterprise Scale can request EU residency (Frankfurt). Benefits: consistent features/updates; trade-off: no on-prem control.

SeaTable supports cloud and self-hosting. Cloud runs on Swiss provider Exoscale’s German data centers (since 2021). Self-hosting via Docker on Linux grants full control—attractive for regulated industries.

ClickUp is SaaS-only with global data centers (US, Ireland, Australia). It prioritizes accessibility and a uniform experience over data-sovereignty customization.

Main Features

Baserow delivers core database power: unlimited databases, multiple views (grid, gallery, kanban, calendar), 25+ field types, row-level permissions, no-code Application Builder, REST API coverage, forms, conditional fields, and growing automations—especially performant when self-hosted.

Airtable offers an extensive feature set: diverse views, Interface Designer, robust automations (up to 100k runs/month on Business), advanced sync, powerful formulas, and deep integrations. Recent foc    us adds generative AI for content and analysis.

SeaTable emphasizes scale and EU compliance: 50,000+ rows on paid plans, many views (calendar, timeline, kanban, statistics), 100+ plugins, Python scripting, SQL queries, and integrations with n8n/Make/Zapier.

ClickUp approaches from project management: tasks, time tracking, goals, mind maps, whiteboards, chat, table view with relational fields, automations, docs, and reporting. It’s a unified workspace rather than a pure DBaaS.

Flexibility (Integration Options)

Baserow is API-first: full REST coverage, webhooks, and open-source extensibility. Fewer native integrations but strong connections via Zapier, n8n, and Make. Great for dev teams who like to tinker.

Airtable leads with hundreds of native integrations (Slack, Google Workspace, Salesforce, Microsoft 365, etc.). The automation platform and webhooks enable sophisticated workflows, though the closed ecosystem limits deep customization.

SeaTable balances native connectors with automation-platform compatibility, plus REST API and Python scripting. Self-hosted deployments are highly customizable; marketplace breadth is smaller but growing.

ClickUp offers 1,000+ integrations (GitHub/GitLab, Slack/Teams, Google Drive/Dropbox). Public API supports custom builds; embedded views pull external data into ClickUp spaces. Advanced integration depth often sits behind higher tiers.

Conclusion

Baserow excels when data sovereignty, self-hosting, and open-source flexibility are must-haves—especially for technical teams optimizing costs and control.

Airtable remains the polished, ecosystem-rich leader for collaboration and automation—but expect higher per-seat costs and cloud-only deployment.

SeaTable is a compelling EU-centric option, pairing generous scale with GDPR-aligned hosting and self-hosting flexibility—though some may scrutinize the China-Germany JV structure.

ClickUp is best when you want an all-in-one work hub with light database features; for deep relational data or massive datasets, the pure DBaaS tools fit better.

Summary Table

Feature / Category	Baserow	Airtable	SeaTable	ClickUp
Origin	Netherlands	United States	Germany	United States
Row / Table Scale & Limits	Unlimited self-hosted; 3,000 rows/workspace (free cloud)	Scales to millions (Enterprise)	Unlimited self-hosted; “Big Data Backend” for large datasets	Task-oriented; practical view ≈ 5,000 tasks
Views & Interface	Grid, gallery, kanban, calendar, form, survey	Grid, gallery, kanban, timeline, calendar; interface builder	Spreadsheet-style, 20+ column types, kanban, calendar	Table, list, board, Gantt, timeline, mind map
Scripting / Automation / API	REST, webhooks, open-source plugins; n8n/Make	REST, scripting blocks, automation builder, AI assists	REST, JavaScript & Python, automation plugins	Custom fields, automations, REST; less deep DB logic
Integration & Ecosystem	Zapier, n8n, Make; open extensibility	Large marketplace (Slack, Google Drive, etc.)	API + plugins; on-prem integrations	Workflow tool integrations (Slack, Google Workspace, GitHub, ...)
Self-Hosting / On-Prem	✅ Yes – open-source Docker	❌ No – cloud only	✅ Yes – cloud or self-host	❌ No – SaaS only
AI Features Included	❌ None (extend via plugins)	✅ Airtable AI (gen-AI, classification, formulas)	⚙️ Limited; connect via API	✅ ClickUp AI (add-on)
Pricing (annual)	Free; Premium $10 / Advanced $20 pp/mo	Free; Team $20 / Business $45 pp/mo	Free; Plus €7 / Enterprise €14 pp/mo	Free; Unlimited $7 / Business $12 pp/mo
Strengths	Open-source, cost control, self-host	Polished UI, strong automation & ecosystem	EU compliance, scale, flexible hosting	Unified productivity (work, tasks, docs)
Best For	Dev teams needing open internal tools	Teams wanting cloud DB + automations	Orgs needing on-prem/EU residency	Work-management with DB-style views

Book a call to learn more on how to choose between these platforms.

Watch videos of these platforms below.

Baserow

AirTable

SeaTable

ClickUp

massimobensi.com

Frequently Asked Questions (FAQ)

Q: What is DBaaS, and how is it different from a spreadsheet?

A: DBaaS platforms look familiar but are real relational databases. They enforce data types, support linked tables, scale to larger datasets, and include automation & APIs—database power without SQL admin.

Q: Can I migrate my existing data between these platforms?

A: Yes, with caveats. All support CSV import/export. Airtable and Baserow are relatively straightforward. SeaTable imports Excel/Airtable formats. ClickUp can import CSVs but complex relations may need re-builds. Expect to recreate automations, forms, and views.

Q: Which platform has the steepest learning curve?

A: Airtable and ClickUp offer the gentlest onboarding. Baserow is moderate (simple basics; technical for self-hosting). SeaTable rewards power users—Python/SQL unlock advanced capabilities.

Q: The pricing per user adds up. Any budget-friendly approaches?

A: Baserow self-hosted removes per-user fees (infra required). SeaTable’s €7 tier is generous. Very small teams may get by on free plans: Baserow (3,000 rows), Airtable (1,000 records), SeaTable (10,000 rows), ClickUp (unlimited tasks; limited storage).

Q: What if I exceed row limits?

A: Airtable blocks new records until you upgrade or prune. Baserow warns and may allow slight overages. SeaTable enforces strict limits with upgrade prompts. ClickUp limits apply to database views; task storage remains generous on free.

Q: Are the AI features worth it?

A: Depends. Airtable AI aids content generation, classification, and formulas. ClickUp Brain helps with summaries and writing across work. If you only need structured data management, AI add-ons might not justify the spend.

Q: Self-hosted vs. cloud—how to choose?

A: Choose self-hosted if you need strict data residency, unlimited scale without per-seat costs, or custom security posture—and have IT capacity. Choose cloud for convenience, zero maintenance, and rapid updates.

Q: Is my data safe, and what about GDPR?

A: All four offer standard security. For GDPR: SeaTable and Baserow provide EU-centric hosting and positioning; Airtable’s EU residency is Enterprise-only; ClickUp hosts globally. If GDPR is critical, prioritize EU hosting or self-hosting.

Q: What happens to my data if I stop paying?

A: Cloud tools typically allow export during a grace period. Always maintain regular exports. With self-hosted Baserow/SeaTable, you retain full data control regardless of subscription changes.

Q: Can these platforms replace Excel/Sheets entirely?

A: For relational data, collaboration, and automations—yes. For heavy financial modeling, pivot wizardry, or cell-level formula gymnastics—traditional spreadsheets still win.

Q: Real-world record limits before slowdown?

A: Airtable is smooth up to ~50k records/base (plan-dependent ceilings rise higher). Baserow handles 100k+ (especially self-hosted with solid resources). SeaTable explicitly supports 50k+. ClickUp’s DB views fit ~10–20k before friction.

Q: Can I build customer-facing apps?

A: Yes, within limits. Baserow’s Application Builder and Airtable’s Interface Designer support branded frontends and permissions. SeaTable allows external sharing. ClickUp is primarily internal, with guest access. True white-labeling often requires Enterprise tiers or companion tools.

Q: Do I need to code?

A: No for basics. Coding unlocks more: Baserow API customization, SeaTable Python/SQL, and complex automations. Airtable/ClickUp remain friendly to non-technical users while still offering APIs.

Q: Will these integrate with my stack?

A: Yes—breadth varies. Airtable has the richest native ecosystem; ClickUp follows for work tools. Baserow and SeaTable rely more on Zapier/Make/n8n or custom integrations via API.

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Shopify Baggage: What You Need to Know (and Why It’s Slowing Everyone Down)

Estimated Reading Time

6–7 minutes ⏱️

Key Takeaways

• Customer data is fragmented since the 2025-01 API version, increasing complexity and risk.

• What used to be one clean REST payload now requires stitching together multiple webhooks and GraphQL fields.

• Why it matters? Because since October 2025, webhooks had automatically switched to this version, breaking existing integrations (that didn't catch up yet).

• Naming and casing inconsistencies (e.g., not_subscribed vs UNSUBSCRIBED) slow teams down.

• Webhook timing quirks can send an “update” before a “create,” breaking standard pipelines.

• Winning teams invest in merge logic, validation, and GraphQL fluency to stay ahead. 🎯

Table of Contents

• From “One and Done” to “Assemble It Yourself”

• The New Model: Granular but Messy

• The Business Impact

• The Inconsistency Headache

• The Webhook Timing Problem

• So Where Do We Go From Here?

• Final Thoughts

From “One and Done” to “Assemble It Yourself”

If your business runs on Shopify data — analytics, marketing, customer management, or automation — you’ve probably felt the shift. With version 2025-01, Shopify changed how customer data is structured and delivered.

Not long ago, life was simple: a single REST call or webhook yielded the entire customer record — name, email, tags, marketing consent, total spent (and more). Fast, predictable, and easy to build on. That’s no longer the case. 🚧

The New Model: Granular but Messy

Starting October 2025, when one year passed since version 2024-10, webhooks had automatically switched to 2025-01, possibly breaking existing integrations, by splitting customer data into sub-entities. Instead of two webhooks (Create/Update) and one nearly identical payload, you now reconcile six different webhooks, each one with its own subset of data:

• Customer Create / Update (2 webhooks)

• Tags Added / Tags Removed (2 webhooks)

• Marketing Consent Changed (1 webhook)

• Purchasing Summary (Total Spent) (1 webhook)

In theory, this is modern and event-driven. In practice, you must stitch these streams together just to recreate yesterday’s “one-and-done” view of your customers.

The Business Impact

More moving parts = more hidden costs. Your teams now correlate events, manage sequencing, add retries, and reconcile mismatched fields. What was a simple integration often becomes an event pipeline with queues, merge logic, and continuous validation.

From a leadership lens, fragmented or delayed data degrades personalization, analytics quality, and compliance confidence — while quietly inflating maintenance budgets.

The Inconsistency Headache

Here’s a real example: a webhook might return email_marketing_consent: "not_subscribed", but GraphQL expects customerEmailMarketingConsent: "UNSUBSCRIBED". Casing matters, and names differ. This pattern shows up all over across GraphQL, unlike the more standardized REST world.

Outcome: tasks take longer than they look. Fields like total_spent vs amountSpent require quadruple-checks for name, case, and semantics at every layer of code where they are used. Small updates can balloon into hours of careful debugging.

“Standards save hours; inconsistency taxes every sprint.”

The Webhook Timing Problem

Another gotcha: Shopify sends an update event with every create — and the update often arrives before (!) the create. If your system assumes strict ordering, you’ll process an update for a record that doesn’t exist yet. ⚠️

This seemingly tiny detail can cause failed merges, phantom errors, and missing records at scale unless you build in idempotency, buffering, and “first-seen” guards.

So Where Do We Go From Here?

Pragmatic moves that pay off:

• Audit integrations for dependencies on the old “complete” payloads.

• Add merge logic to reconcile multiple webhook streams with ordering safeguards.

• Validate continuously to catch data drift and naming mismatches early.

• Lean into GraphQL and document field mappings (name + case) as a living contract.

Yes, it’s extra work — but it’s the path to reliable customer views and automation in Shopify’s evolving ecosystem.

Final Thoughts

Conceptually, Shopify’s shift to modular, event-driven architecture makes sense. Practically, it introduces friction you’ll have to design around. The teams that invest early in resilient pipelines will ship faster, fail less, and keep trust in their data — even as the platform keeps moving.

Bottom line: standardize your mappings, harden your sequencing, and treat merge logic as product, not plumbing.

Book a call to learn more about Shopify inner-details. 

In years of integrating it with different systems I got to learn a few things about it...

Watch a video oon Shopify.

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Frequently Asked Questions (FAQ)

1. Why did Shopify move to this new API structure in the first place?

Shopify’s goal is modernization. The platform is shifting toward a modular, event-driven, GraphQL-based architecture — something that scales better for large merchants and complex data needs. In other words, Shopify wants to give developers more granular control and performance. The trade-off, though, is complexity on the integration side. While the change makes sense long-term, it creates short-term friction for anyone used to REST’s “one payload per entity” simplicity.

2. Does this mean REST APIs are going away completely?

Not right away — but the trend is clear. REST APIs are still supported, yet Shopify’s newer capabilities (like fine-grained permissions, sub-entity queries, and advanced mutations) are GraphQL-only. Over time, we’ll likely see REST endpoints deprecated or left to stagnate. Businesses should start investing in GraphQL literacy now so they’re not caught off-guard when that shift accelerates.

3. How big of a problem are naming inconsistencies really?

Surprisingly big. 🚨 Field naming, casing, and structure differences (like not_subscribed vs UNSUBSCRIBED) don’t just slow down developers — they cause data mismatches, broken transformations, and extra maintenance. When you scale to dozens of data fields and thousands of customer events, even small inconsistencies multiply into days of debugging. The fix? Create an internal “data dictionary” mapping all GraphQL and REST field equivalents, so you never guess field names again.

4. How should we handle the “update before create” webhook problem?

This is one of the trickier timing issues. When Shopify sends an update before a create, standard systems throw errors because the record doesn’t exist yet. To solve it, add logic that:

• Checks if the customer exists before processing updates.

• Buffers events for a few seconds (e.g., with a message queue or temporary store).

• Implements idempotency — so “create” and “update” events for the same record can safely run in any order.

It’s not glamorous, but it saves hours of cleanup later.

5. What’s the best way to merge data from multiple webhooks?

You’ll need a merge pipeline that aggregates multiple webhook types into a single unified customer profile. The simplest way to do this is to use:

• A custom update logic that "remembers" existing fields values (updated by different flows), before performing the update.

Think of it like assembling a puzzle — each webhook gives you one piece, and your merge logic ensures the picture’s complete before it’s saved or synced elsewhere.

6. Is GraphQL actually better than REST for Shopify integrations?

It depends on your goals. GraphQL is more flexible and efficient when you only need fragments of specific data. You can request specific fields, reducing payload size and improving speed. But it’s also less forgiving: naming, structure, and syntax errors are common, and documentation can lag behind. REST is simpler, but it’s being slowly deprecated by Shopify.

7. What’s your advice for non-technical leaders navigating these changes?

If you’re not writing the code yourself, focus on three things:

• Resourcing – Give your developers time and budget to refactor integrations regularly. These aren’t “quick fixes.”, but rather necessary scheduled maintenance. You do it for your car, do it also for your business integration.

• Visibility – Use dashboards or alerts that monitor data completeness and API errors.

• Partnerships – If you rely heavily on Shopify data, consider partnering with vendors who specialize in e-commerce data pipelines or middleware. It’s cheaper than maintaining brittle custom code.

In short: understand the cost of not adapting — because data inconsistency will quietly erode trust, accuracy, and customer experience.

8. When should we expect more API changes from Shopify?

Shopify typically releases new API versions every quarter, deprecating older ones about a year later. So, if 2025-01 just rolled out, expect 2025-04 and 2025-07 to bring more incremental shifts — likely continuing the push toward GraphQL and modularization. Staying ahead means keeping your integration layer flexible, documented, and ready to evolve each cycle.

9. How do we future-proof our Shopify data strategy?

Future-proofing comes down to three principles:

• Decouple your integrations from business logic (so you can swap APIs without rewriting everything).

• Automate testing for schema changes or field mismatches after every Shopify update.

• Centralize mapping in one schema or data model that your whole team references — not scattered across code.

If you follow these, API version changes become routine maintenance — not full rebuilds.

10. What’s the single biggest takeaway from all this?

Adapt early. Shopify’s architecture is evolving fast, and the businesses that modernize their data pipelines now will save months of pain later. Embrace GraphQL, treat merge logic as core infrastructure, and never assume two fields are named the same — even if they look like they should be. 😉

Comparison between n8n, OpenAI Agent Platform, Google Opal and Make.com as automation and AI agent platforms

Introduction

Organizations in the present day depend on automation systems and AI agent platforms to optimize their operations at scale. The platforms differ from each other through their design methods which range between visual construction of workflows and development of conversational AI applications. This page assesses n8n, OpenAI Agent Platform, Google Opal and Make.com through their pricing models, core functionality, hosting flexibility and LLM options.

 

Pricing💰

The n8n platform provides unlimited workflows and steps and users to all plans while charging customers based on the number of workflow executions. The plans starts from $20 per month with 2,500 executions, up to $50 per month with higher execution limits. The Community Edition of n8n operates as a free version for users who want to host it themselves. There is a 50% discounted plan for startups with fewer than 20 employees and less than $5M revenue.

 

The OpenAI Agent Platform uses API-based token pricing instead of subscription-based pricing for its services. The Standard model pricing structure applies to GPT-5 models which cost between $2.50 and $10 per million input tokens based on the model version.

 

The Google Opal service operates without cost during its experimental US-only public beta phase as a Google Labs project. It is difficult to predict its future pricing structure because no commercial rates have been disclosed.

 

Make.com operates with an operations-based pricing system which includes a free plan that allows 1,000 operations per month. The platform offers different pricing plans which start with hobby use and progress to enterprise solutions through operation-based pricing instead of workflow-based pricing.

The n8n UI.

Hosting Options 🏠

n8n provides users with complete hosting flexibility since it supports both self-hosted deployments and cloud-based options. The Community Edition of n8n allows users to deploy n8n on their infrastructure on Docker or VPS or Kubernetes systems for full control over their operations. All plans from n8n support deployment through cloud hosting and self-hosting options for users who want to keep their data more private.

 

The OpenAI Agent Platform operates exclusively in the cloud through API requests that direct data to OpenAI's processing infrastructure. Organizations need to transmit their data to OpenAI servers because there are no available self-hosting options.

 

The entire system of Google Opal operates within Google Cloud infrastructure. Users cannot host the web-based Google Labs experiment because it only operates through opal.withgoogle.com and requires Google account authentication.

 

Make.com operates as a cloud-based SaaS platform which does not support self-hosting capabilities. Users access Make.com through their web interface while the service runs all workflows on Make's infrastructure.

The OpenAI Agent Platform UI.

Core Features ⚙

n8n stands out through its 400+ pre-built integrations and its visual node-based editor which supports complex logic, conditional branching and custom JavaScript/Python code execution. The platform features built-in database functionality and headless browser automation which makes external service usage optional.

 

The OpenAI Agent Platform enables developers to access OpenAI models through its API endpoints. Developers can create custom agents through function calling and assistants API and retrieval-augmented generation methods. The platform requires developers to write code for implementation instead of using visual interfaces.

 

Users can describe workflow requirements through natural language in Google Opal which then creates visual workflow designs automatically. The platform allows users to work in both conversational and visual modes while supporting file uploads and Google Drive connections and YouTube URL processing. The platform includes pre-built templates which help users complete common tasks and finished applications can be distributed through Google account sharing.

 

The platform Make.com provides 1,500+ integrated applications through its visual scenario building interface. The platform provides simple access to users who lack technical skills yet offers complex features for data transformation and error management and scheduling capabilities. The platform added AI functionality to its recent software updates.

The Google Opal UI.

Flexibility (LLM Choices) 💪

The LLM flexibility of n8n stands out because it provides users with access to a wide range of integration options. Users can access OpenAI, Anthropic Claude, Google PaLM, Hugging Face models and custom API endpoints through the platform. Organizations can select their preferred LLM models for specific use cases because the platform supports multiple models and allows seamless provider transitions without workflow reconstruction. The OpenAI Agent Platform operates exclusively with OpenAI models from the GPT family.

 

The Google Opal platform operates exclusively with Google AI infrastructure and models. The platform utilizes Google multimodal features but users cannot add third-party LLM providers or select different models.

 

The AI modules of Make.com now support multiple providers but the level of LLM flexibility depends on the specific integration. The platform initially focused on app-to-app automation but AI features became part of its offerings after its initial launch.

The Make.com UI.

Conclusions ❗

These platforms operate for different functional requirements. 

n8n provides technical teams with flexibility and self-hosting capabilities (with increased privacy and security), and LLM provider independence which makes it suitable for organizations needing complex automation and compliance solutions.

The OpenAI Agent Platform provides developers with access to advanced models through programmatic interfaces while requiring them to work within OpenAI's system framework. 

The experimental phase of Google Opal provides non-technical users with easy AI application development for prototyping, yet its production readiness remains uncertain. 

The platform Make.com provides businesses with user-friendly automation features and broad integration options while delivering average AI functionality.

The critical differentiators are control versus convenience. 

Self-hosting and LLM choice make n8n most adaptable, while managed platforms (Make.com, Opal) lower technical barriers. 

Organizations should weigh infrastructure preferences, required integrations, LLM strategy, and team technical depth when selecting their automation platform.

Book a call to learn more on how to choose between these platforms.

Watch videos of these platforms below.

n8n

OpenAI Agent Platform

Google Opal

Make.com

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Frequently Asked Questions (FAQ)

Q: What platforms are compared in this article?

A: The post compares four major platforms: n8n, OpenAI Agent Platform, Google Opal and Make.com. The comparison covers pricing models, core functionality, hosting/flexibility and support for large language models (LLMs).

Q: Why compare n8n vs OpenAI Agent Platform vs Google Opal vs Make.com?

A: Because businesses and automation practitioners need to choose the right automation/AI-agent platform based on their specific needs: whether visual workflow building, multi-LLM support, self-hosting, or enterprise scale. The post helps highlight where each tool excels or lags.

Q: What are key differentiators between these platforms?

A: Key differentiators include:

• Hosting/flexibility: whether you can self-host or require platform-managed environment.
• LLM options: support for models beyond one provider (e.g., OpenAI, Google, custom).
• Workflow automation vs true agentic behaviour (tool selection, memory, decision making).
• Visual builder and integration ecosystem: how easy it is for non-developers to build.
• Pricing and execution limits (e.g., workflow runs, agents, steps) as noted in the article. 

Q: When is n8n the best choice?

A: n8n is ideal if you want: self-hosting or open-source flexibility, the ability to integrate many systems/tools, multi-LLM support, and you don’t mind a bit more technical setup or logic wiring. The article highlights n8n’s strength in integration and agentic work.

Q: When might OpenAI Agent Platform or Google Opal be better choices?

A: If you prefer a more managed, simplified interface, or you are already embedded in the provider’s ecosystem (OpenAI or Google) and value rapid deployment rather than maximum flexibility, these options may be more suitable. The article touches on how the platforms differ in ease of use and hosting model.

Q: What should I check before choosing an AI-agent or workflow platform?

A: You should consider:

• Do you need self-hosting or managed hosting?
• What LLMs and tools do you need to integrate?
• How many executions / workflows / agents will you run (pricing/execution limits)?
• How technical your team is (visual no-code vs low-code vs code).
• What integrations and automation logic you need (e.g., conditional logic, memory, tool chaining).

The article lays out these criteria for evaluation.

Q: Are there trade-offs between “agentic AI” platforms and standard workflow automation tools?

A: Yes. Agentic AI platforms (those labelled “agent” rather than “just workflow”) tend to focus on tool selection, memory/context management, reasoning and decision-making, whereas traditional workflow automation tools focus on triggers/actions/integrations. The article points out that if you need only “agent” behaviour (autonomous decision making) you may pick a different platform than if you just need “automate tasks”. However, the flexibility of automation platforms allows to connect any agent to specific workflow tasks.

Q: How does the article address pricing among these platforms?

A: It provides an overview of the pricing model for n8n (e.g., unlimited workflows/steps in plans, self-hosted community edition free) and discusses how pricing may differ across the platforms.

Q: Will these platforms lock me into a single AI model or provider?

A: It depends. Some platforms are tightly coupled to a provider’s model (e.g., OpenAI) whereas others (like n8n) emphasise model-agnostic flexibility and integrations with many LLMs. The article mentions the importance of avoiding vendor lock-in if flexibility is critical.

Q: Can non-technical users adopt these platforms easily?

A: The ease of adoption varies: platforms offering no-code visual builders are easier but may sacrifice flexibility; platforms like n8n offer more power but require deeper logic/flow setup. The article assesses these tradeoffs.