The Hidden Cost of Ad Hoc Cloud Analytics (and How DataGPT saves millions of dollars on data analysis)

Gerard Kostin

• 22 min read
The Hidden Cost of Ad Hoc Cloud Analytics (and How DataGPT saves millions of dollars on data analysis)

Short abstract

Ad hoc data analysis is crucial in today’s data-driven enterprises – whether it’s an e-commerce giant drilling into holiday sales anomalies, a supply chain team tracing a bottleneck in shipments, or a streaming service investigating a sudden drop in viewer engagement.

The insights from exploratory queries can be immensely valuable, but so can the cost of obtaining them on cloud platforms not designed for free-form exploration. We’ve seen how quickly those costs add up: from five-figure monthly bills for modest teams, to six- or seven-figure annual bills at data-heavy companies, and even overnight surprises in the tens of thousands for a single user’s experiment. The root cause is the pay-as-you-go nature of cloud data warehouses combined with explosive data growth and a lack of cost controls and cost predictability.

Traditional remedies include instituting query cost monitoring, governance policies, resource caps, and query optimization campaigns – all useful practices, but they do not really help when it comes to running black-box AI-generated queries on top of the data Warehouse. What DataGPT brings to the table is a more fundamental solution through technology: by diverting AI-driven analytical workloads to a highly optimized compute engine and controlling it’s efficiency and advanced analysis with analytical engine, it allows organizations to ask AI unlimited number of questions on top of their data without worrying that each question is silently swiping the credit card. The AI can be as inquisitive as it wants, because it’s querying a platform built for efficient computation and AI-powered advanced analysis.

Ad hoc analysis doesn’t have to be the budget-busting wildcard of cloud analytics. With smarter tooling like DataGPT – essentially an “AI middle layer” that ensures queries are executed cost-efficiently – enterprises can regain control. They get the best of both worlds: the agility and insight of massive data-volume exploration, and the peace of mind that comes with predictable, minimal query costs. As the data landscape continues to evolve, approaches that combine AI with cost awareness and analytical power will become increasingly vital for any data-driven business that wants to stay both innovative and cost-efficient.

The message is clear: tame your ad hoc analytics costs, or they will tame your budget – and DataGPT hints at how companies can achieve this at scale.

 

Main Content

Modern enterprises across e-commerce, manufacturing, supply chain, and streaming services are accumulating massive volumes of data. With this wealth of information comes the need for ad hoc analysis – exploratory queries and root-cause investigations that go beyond standard dashboards. Unfortunately, many companies have learned the hard way that these on-the-fly queries can incur jaw-dropping costs on cloud data platforms like Google BigQuery, Databricks, and Snowflake. In fact, a survey found 80% of data management professionals struggle to predict data-related cloud costs (​ciodive.com). When every query against a petabyte-scale warehouse carries a price tag, “querying blind” can quickly burn through tens or even hundreds of thousands of dollars.

 

Why Ad Hoc Queries Are So Hard to Control (BigQuery, Snowflake, Databricks)

As has been said, enterprises often want to empower their analysts, data scientists, or even automated AI agents to freely explore data – especially for troubleshooting spikes in metrics or answering spontaneous business questions.

Exploratory “why did X happen” questions often require scanning large volumes of data repeatedly in different ways. In an enterprise setting (e.g., analyzing millions of Google Analytics 4 events to find why revenue dropped last week), an analyst might run dozens of queries by slicing and dicing data by different dimensions. Each query can scan gigabytes or terabytes of data, so costs accumulate quickly under cloud data warehouse pricing models. The convenience of these platforms – being able to query massive datasets with SQL – can lead to “brute force” analysis that is effective but expensive at scale. As one engineer bluntly put it, using BigQuery can feel “like crack... any idiot who can write SQL can do incredible things. Then WHAM. Your $300 dollar bill is $30k.”​ (reddit.com).

 

There are a number of AI-powered data analytics tools around that claim that can to help solve this, but it comes at a huge hidden cost. For AI-driven analytics or automated root-cause analysis (where an AI agent might run dozens of hypotheses as SQL queries), the cost issues above are especially pertinent. Such workflows generate many queries in an iterative fashion. For example, an AI trying to diagnose a metric drop could systematically slice data by every dimension available, effectively running hundreds of queries a human might not bother to execute. In BigQuery, that could mean hundreds of TB scanned; in Snowflake/Databricks, hours of cumulative compute – potentially costing hundreds or thousands of dollars for a thorough automated analysis session. This can make it financially infeasible to let an AI freely explore data at scale unless constraints are imposed.

 

Uncontrolled query freedom of AI-based SQL wrappers on cloud data warehouses comes at a price, and this is why :

  • Pay-Per-Use Pricing: Cloud warehouses charge based on data scanned or compute time used per query. BigQuery, for instance, bills $6.25 per terabyte scanned on-demand, and Snowflake and Databricks charge based on compute seconds or credits. This usage-based model means every ad hoc query has a variable cost. If an AI SQL Wrapper writes an inefficient SQL (e.g. a SELECT * or unfiltered join on multi-terabyte tables), the query can scan terabytes and rack up hundreds of dollars in a single run. Run such queries repeatedly or at scale, and the costs multiply linearly.
  • Massive Data Volumes: In almost every data-driven industry (e-commerce, production/manufacturing, supply chain, streaming media), data volumes are enormous. It’s not uncommon for a retailer or streaming service to accumulate petabytes of log data, sales records, or sensor readings. Exploring a full year of data might involve billions of rows. Thus, even one unoptimized exploratory query can chew through an epic amount of data (check out this Shopify’s 75 GB/query example (shopify.engineering)). The more data you have, the higher the stakes (and cost) of each unconstrained query.
  • Unpredictable, Iterative Workflows: Unlike scheduled reporting, ad hoc analysis is unpredictable by nature. Business Users might ask dozens of questions in a day from an AI SQL wrapper, iterating and refining to find a root cause or insight (Business teams using self-service AI assistants might ask a sequence of follow-up questions (“zoom in on last week”, “now breakdown by region”, etc.), not realizing each step triggers new scans). This iterative process means costs can accumulate silently in the background, until the monthly cloud bill arrives. Indeed, forecasting cloud query costs is so tough that 8 in 10 companies report difficulty predicting their data spend (​ciodive.com).
  • Lack of Cost Visibility & Guardrails: Many organizations enable data access before they implement granular cost monitoring or quotas. By default, BigQuery will happily execute a petabyte-query without warning (it does show a cost estimate in the UI, but a user can easily click run without noticing). Snowflake and Databricks usage is often reported in aggregate compute-hours, making it hard to attribute costs to a specific team or user without custom tagging. Opendoor’s data team, for example, realized they “lacked specific details on the costs of individual query executions” and built a custom solution to tag and track each query’s cost in Snowflake (​medium.com). Without such measures, it’s impossible to realize which queries or users are burning dollars until after the fact. This is why cost overruns often come as a surprise.
  • AI and SQL-Generating Tools – Powerful but Cost-Blind: The rise of AI assistants that generate SQL (e.g. natural language to SQL tools, or LLMs integrated directly with your warehouse) has made data querying easier for non-experts. However, these tools lack cost-awareness – their priority is getting the answer, not minimizing bytes processed. An AI agent might naively query an entire table to “be safe,” use subqueries or UDFs that aren’t cost-efficient, or run multiple intermediate queries to arrive at an answer. For example, if a business user asks, “Why did our video streaming latency spike last night?”, an AI-driven tool might launch a dozens of queries full scanning the entire table to find main contributing factors (moreover AI agents are known to be trapped in a complex analysis workflows that could lead to Agents trying to self-heal and running the same “wrong” queries again and again multiplying cost with no value). Without careful optimization, this automated exploratory querying can generate a huge bill in a short time. The company essentially trades analyst time for compute time – and if the AI is unchecked, it may trade too much compute! As one FinOps expert noted, inefficient queries like broad SELECT * scans are a common culprit behind high Snowflake bills (​revefi.com). AI-generated SQL needs human-in-the-loop oversight or automated optimizers, which kills the self-serving nature of such tools.

All these factors make ad hoc analysis costs hard to predict and control especially with black boxed AI SQL wrapper that can run tons of “crazy” queries that a normal data analyst would never run. It’s like giving everyone in your company a company credit card with a usage-based taxi service – without dashboards or limits, you might only learn at month’s end who took the $500 limousine rides. Indeed, cloud data spending can escalate with few obvious warning signs, as Capital One experienced before adopting rigorous governance – cloud analytics provide “vast power, and, when not carefully managed, escalating costs” (​ciodive.com).

 

To make it clear, below, we break down BigQuery, Databricks, and Snowflake costs overview for iterative ad hoc analysis.

BigQuery: Pay-Per-Query Pricing and Repeated Scan Costs

Pricing Model: BigQuery uses an on-demand, pay-as-you-go model for queries. You are charged based on the amount of data scanned by each query. In the traditional on-demand plan, queries cost $6.25 per TB after a free 1 TB monthly allowance (​BigQuery Pricing: Considerations & Strategies , medium.com, Pricing  |  BigQuery: Cloud Data Warehouse  |  Google Cloud ). Storage cost in BigQuery is separate and relatively cheap (~$0.02 per GB-month), but compute/query costs dominate in ad hoc analysis​(reddit.com). BigQuery is a serverless, columnar warehouse, which means there are no indexes; every query potentially performs a full column scan. It relies on partitioning and clustering for performance instead of traditional indexes (​blog.panoply.io). This design is flexible for any query, but means a poorly filtered query can end up scanning all rows in the dataset. For example, selecting all columns with SELECT * will scan an entire table’s data, even if you use a LIMIT, which can surprise users by running up large bills, and if you interact with BQ from an external IDE or Python, you would not even see any warnings about that. (​news.ycombinator.com.)

Why Ad Hoc Queries Get Expensive: In an exploratory investigation, you might rerun similar queries many times. Each slight variation (different filters, groupings, etc.) is a new query that scans data anew (unless results are cached, which only applies if the identical query was run recently on unchanged data). With event-level GA4 analytics data, it’s not uncommon to have billions of rows or many terabytes per month. Even if you partition by date and limit queries to a week or one day, each query may scan hundreds of gigabytes. At ~$5–6 per TB, scanning say 500 GB of data costs about $2.50–$3.00. A single “meaty” analysis query can thus cost a few dollars – and importantly, “one meaty analysis query costs more than a month of data collection and storage” for GA4 data​ (reddit.com). Running 10-20 such queries while iterating on hypotheses could rack up $20–$50 in query fees in a short session. Do this frequently or at scale with multiple analysts, and monthly costs can skyrocket. Developers note that inefficient queries (scanning more data than necessary) are the real budget-killer: “GBQ storage is dirt cheap. The real costs are in querying the data (inefficiently)”​ (reddit.com). There are BigQuery features like aggregated preview tables or BI Engine caching to mitigate costs, but ad hoc SQL exploration often bypasses those by directly querying raw event tables.

Example Scenario: Suppose your GA4 events table is 5 TB for the last month. Asking “why did revenue drop last week” might involve:

  • Querying the entire last week of events (say 1 TB) to compute total revenue and compare to prior weeks (~$6 scanned cost for that query).
  • Then breaking down last week’s revenue by country, by device, by traffic source, etc. – each of those queries might scan the same 1 TB (another ~$6 each time).
  • A few more drills into specific segments (particular product categories or user cohorts) again scan large portions of data.

After a dozen such queries, you’ve potentially processed ~12 TB cumulatively, costing on the order of $60–$75 just to answer one investigative question in depth. It’s easy to see how a team of analysts can burn through hundreds or thousands of dollars in BigQuery in a month without careful query optimization or cost monitoring. This pay-per-byte model is powerful for infrequent big queries, but for numerous repetitive explorations it becomes costly. Users have reported bill shocks when usage scales. In one Reddit discussion, a user described how a startup’s BigQuery bill jumped from a manageable few hundred dollars to tens of thousands once heavy analytical querying kicked in​ (reddit.com).

Databricks: Compute Cluster Billing and Interactive Use Costs

Pricing Model: Databricks is a cloud-based platform built on Apache Spark, and it charges for compute time rather than data scanned. Databricks introduces its own unit of compute called a Databricks Unit (DBU), which roughly corresponds to the processing resources used per hour. You pay for the cluster resources and Databricks DBUs while your queries or notebooks run. Billing is per second with no long-term commitments (on-demand), so you only pay for what you use​ (cloudzero.com). However, you must keep a cluster or SQL endpoint running to execute queries, which incurs cost even when idle (until you shut it down or it auto-terminates). The cost per DBU-hour varies by instance size and tier: for example, on AWS a Standard Databricks cluster might be ~$0.15 per DBU, while a Serverless SQL warehouse can be around $0.70 per DBU (this includes the underlying VM cost)​ (chaosgenius.io). Each node in a cluster consumes a certain number of DBUs proportional to its size – e.g., an 8-core node might use ~1 DBU/hour in Standard tier (just as an illustration). In practice, a small Databricks all-purpose cluster might use ~2–4 DBUs/hour total (cost on the order of $0.30–$1.00/hour), whereas a beefier analytics cluster could consume dozens of DBUs/hour. The key is that every second of cluster uptime costs money, whether you are actively querying or not.

Databricks recently introduced the Photon engine and serverless SQL endpoints to improve performance for BI-style queries. Photon can execute SQL queries faster (vectorized execution), meaning you get more work done per second of compute. This can lower the cost per query if queries finish quicker, but you’re still paying per second of usage. Serverless endpoints simplify scaling and eliminate managing clusters, but they charge a premium DBU rate (as noted, up to ~$0.70/DBU on premium tier) (​chaosgenius.io). In other words, Databricks can handle ad hoc analysis either via an interactive cluster (Spark notebook) or a SQL warehouse, but both accrue costs proportional to compute time used.

Why Ad Hoc Queries Get Expensive: With Databricks, the cost challenge for exploratory analysis comes from long-running interactive sessions and potentially under-utilized clusters. If an analyst spins up a cluster to run one query, they might incur a minimum few minutes of runtime (for Spark job startup, etc.) and then leave the cluster running during thinking time between queries. For example, if a user keeps a cluster running for an hour to poke around data, that whole hour is billed. The bite is smaller than Snowflake’s largest warehouses in per-hour cost, but it can add up with multiple users or large clusters. Suppose a team of analysts each runs a Databricks SQL endpoint for interactive queries; even if each query is fast, the cluster might be kept active to serve subsequent queries with low latency. Ten analysts running moderate-sized clusters in parallel could be consuming dozens of DBUs continuously. Real-world reports show this can amount to significant spend – e.g. one team noted they were paying about $5,000 per month for Databricks SQL querying and dashboards, which drew negative attention from leadership​ (reddit.com). That kind of bill suggests many concurrent users or a few large always-on warehouses.

Another factor is that repeated large Spark jobs (scanning large datasets) incur both cloud VM costs and Databricks overhead. If the data (let’s say GA4 events in Parquet format) is large, each query must read and shuffle a lot of data over the cluster’s executors. Unlike BigQuery which charges by data read, Databricks charges by time, but reading more data takes more time – either way, more data processed = higher cost. There are ways to optimize this (caching data in memory across queries, using Delta Lake indices like z-order, etc.), which can make iterative queries faster after the first run. However, ad hoc analysis often involves different queries each time, so caching benefits may be limited unless the user explicitly prepares the data. If an AI or analyst is issuing many queries one after another, the cluster might scale up to handle the load, increasing DBU usage.

Example Scenario: Consider analyzing the same GA4 dataset on Databricks. You might start an interactive notebook on a cluster with 4 nodes to have snappy performance on the large data. This cluster might be, ~8 DBUs/hour (roughly $1–$2/hour assuming standard/premium plan​(chaosgenius.io)). If you explore for 3 hours in a day, that’s ~24 DBU-hours, perhaps on the order of $5 in DBU charges, plus the underlying infrastructure cost (which could be another ~$5 if not using serverless). So maybe ~$10 for an afternoon’s analysis by one user. That doesn’t sound too bad, but multiply it by a team doing this daily and by 20 workdays: now you’re looking at a few thousand dollars per month. Moreover, if the analyst needs a larger cluster for heavier queries (say a 32-DBU/hour cluster, ~$8–$15/hour), costs rise proportionally. One user on Reddit shared that their Databricks usage for SQL analytics across the business was burning ~$5K monthly​ (reddit.com). This happens when ad hoc queries and BI dashboards run on Databricks without tight cost controls – the flexibility to run any query comes at the cost of continuously running compute.

In summary, per-second billing and the need to keep compute ready for interactive use can make ad hoc workloads pricey on Databricks. It’s essentially renting a Spark cluster by the second: fast and granular, but not cheap if left running or used heavily.

Snowflake: On-Demand Credits and Query Compute Costs

Pricing Model: Snowflake uses a credit-based on-demand pricing model. You configure virtual warehouses (compute clusters) of a given size and Snowflake charges credits based on the warehouse’s running time. Each warehouse size (X-Small, Small, Medium, etc.) has a fixed credit consumption per hour (XS = 1 credit/hour, Small = 2, Medium = 4, and so on, doubling each tier) (​blog.panoply.io). Credits have a monetary cost that depends on your Snowflake edition and cloud provider; on-demand Standard Edition is roughly $2 to $3 per credit (Enterprise editions can be higher)​ (capitalone.com). For example, an X-Small warehouse might cost about $2/hour, a Medium ~$8/hour at $2/credit (or $12/hour at $3/credit). Snowflake bills per-second while the warehouse is running, with a 60-second minimum each time a warehouse is started​ (keebo.ai). Warehouses can be set to auto-suspend when idle to save money, and auto-resume on query. Storage is billed separately (around $23/TB/month for compressed storage​ (blog.panoply.io), but as with BigQuery, storage fees are usually not the concern – it’s the compute credits for queries that drive costs.

Why Ad Hoc Queries Get Expensive: Snowflake’s on-demand model means every query execution burns credit based on time. If an analyst runs a complex query that takes, say, 10 minutes on a Medium warehouse, that uses about 1/6 of an hour * 4 credits/hour = 0.67 credits, which might cost on the order of $2 (assuming ~$3/credit) (​capitalone.com). A series of such queries will add up linearly. The iterative nature of “why” analysis means you might run dozens of queries in search of insight. Ten queries at ~$2 each is ~$20; a hundred queries could be $200. This is not too different from BigQuery’s $/TB in outcome – either way, lots of large queries cost a lot. But Snowflake has some unique patterns to watch for: short, frequent queries and the 1-minute billing minimum. If an analyst runs many quick, selective queries (each only a few seconds), Snowflake will either keep the warehouse running (incurring continuous charges) or if it pauses between them, each resume triggers a new minimum charge. For instance, “if your warehouse activates for a 3-second query, you’re billed for a full minute. If it reactivates 10 seconds later for another query, that triggers another 60-second minimum.”​ (keebo.ai )Thus, a user running a sequence of lightweight queries with little idle time might inadvertently pay for a lot of one-minute blocks. One best practice is to set a sensible auto-suspend delay (not too short) so that you don’t constantly stop/start the warehouse between an analyst’s interactions – or conversely, to group work into fewer, longer sessions (​keebo.ai). Either way, an active analyst effectively keeps the meter running.

At enterprise scale, concurrent use is another factor: Snowflake can spin up multiple warehouse clusters for concurrency (e.g. multi-cluster warehouse for many users) – great for performance isolation, but it can multiply costs if many are querying simultaneously. Also, Snowflake’s ease of use can lead teams to over-provision warehouse sizes “just to be safe” on performance, which means higher credits consumed per second. One Snowflake user noted in a discussion that in their project Snowflake ended up 2–3x more expensive than BigQuery for similar workloads (​reddit.com). This can happen if, for example, Snowflake is kept running at a higher capacity or if queries aren’t tuned and take longer than on BigQuery. Snowflake does have a results cache (so repeating the exact same query within 24h on unchanged data is free) and it benefits from columnar compression and pruning, but ad hoc queries by nature tend to be new each time, so caches rarely hit.

Example Scenario: Suppose you use Snowflake to analyze GA4 data. You might choose a Medium warehouse to get decent speed. Now, if you run an intensive query on last week’s data that takes 5 minutes, that consumed ~0.333 hours * 4 credits/hour = 1.33 credits, maybe ~$4. If you run 10 such queries exploring different aspects, that’s 13 credits ($40) in total. If queries are shorter, you might save time, but then the overhead of many short runs might inflate it in other ways. For example, 50 quick queries that each use 5 seconds of runtime – if the warehouse stayed active the whole time, maybe they cumulatively use only a few minutes of runtime (<1 credit). But if the warehouse was pausing and resuming around them, you could have paid for 50 minutes (50 * 1-min minimum), which at 4 credits/hr is 3.33 credits ($10) even though the queries themselves only used ~4–5 minutes. Now scale this to multiple analysts or repeated investigations per week, and it’s easy to see monthly Snowflake compute consumption climbing into the hundreds of credits. In Snowflake’s favor, you can scale down to smaller warehouses for light workloads or schedule automatic suspensions to control cost. However, the on-demand convenience also means it’s easy to spend a lot without realizing – you get performance when you need it, and the bill comes later. A Capital One engineering blog noted: “While $2 for a 10-minute query might not sound expensive, these queries quickly add up.”​ (capitalone.com)

 

Ultimately, the pay-as-you-go nature of these cloud warehouses demands vigilance for ad hoc use. They enable unparalleled agility – you can throw a petabyte of data at a complex question and get an answer in seconds or minutes – but the financial cost scales with that power. For iterative “why” analysis, this means there’s essentially a tax on curiosity: every additional angle you investigate has a real dollar cost. In an enterprise setting with large data, those costs are non-trivial.

 

The DataGPT Approach: Optimized, Cost-Friendly AI Querying

How can enterprises enjoy the benefits of on-demand, AI-driven deep data analysis without the budget-busting surprises? DataGPT offers an alternative approach. As described in the abstract, DataGPT compiles AI-generated queries to run on its own optimized compute engine and proprietary analytical engine, rather than hitting the primary data warehouse for every question. This design fundamentally changes the cost equation of AI-driven analytics:

DataGPT slashes enterprise querying costs dramatically compared to direct queries on BigQuery or Databricks.

  • Specialized, Efficient Compute: Instead of using expensive general-purpose warehouses for each ad hoc query, DataGPT uses a purpose-built compute engine optimized for AI-query workloads combined with a proprietary Analytical engine it delivering 100% accurate analysis in seconds at a small fraction of the cost. This engine can leverage efficient algorithms, caching, and on-the-fly data aggregations to answer the AI’s questions with minimal resource usage. The proprietary compute engine is tuned for analytical queries, delivering results with far less compute than a brute-force scan of a data lake. The result is massively lower per-query compute costs – so much lower that DataGPT can charge customers a premium and still save them money.
  • Orders-of-Magnitude Cost Reduction: Internal benchmarks (see figure above) show DataGPT delivering analyses at a tiny fraction of the cost incurred on BigQuery or Databricks. For example, a simple single-dimension lookup that might cost $0.20 on BigQuery runs for about $0.003 on DataGPT – over 60× cheaper. As the analysis complexity grows, the savings become even more dramatic. A time-series trend analysis that could cost $1.70 on BigQuery is only $0.0004 with DataGPT, thousands of times cheaper. For a complex key driver analysis (finding which factors drive a metric) on a large dataset, one enterprise benchmark showed BigQuery charging $2,773 and Databricks about $173, while DataGPT completed the same analysis for around $0.02. That is a cost reduction of more than 4 orders of magnitude (≈73,000× cheaper than BigQuery) for that task. When additional techniques like bootstrapping (running multiple simulations for statistical confidence) were applied, the BigQuery cost was projected around $41,894 (and $2,618 on Databricks), versus roughly $0.05 on DataGPT – an astonishing 445,000× cost savings. In practical terms, an analysis that might cost a Fortune 500 company $100k in cloud query fees could cost just a few bucks with DataGPT’s engine. These are game-changing differences.
  • Cost-Aware Query Compilation: DataGPT’s AI isn’t just throwing SQL at a warehouse and hoping for the best. Instead, it essentially translates the user’s question into an optimized query plan within its own analytical engine. It can decide how to process data in the most efficient way in each individual query, avoiding scanning irrelevant data, and apply performance tricks but still support very deep and advanced data analysis algorithms such as trend, correlation analysis or anomaly detection that cloud warehouses don’t automatically do for you. This is like having a smart Data Analyst: the AI’s intent is executed with a strategy that minimizes waste. The user still gets the answer they need, but behind the scenes DataGPT processes data way more efficiently due to a native integration with athe nalytical engine than an equivalent direct SQL on the raw data. The performance is often way faster too, since the engine is tailored to these queries.
  • Isolated, Predictable Costs: Because DataGPT uses a proprietary database, enterprises aren’t directly burning their Snowflake/BigQuery credits for each question. They pay for DataGPT’s service (which is data-volume-based and not usage-based and at a far lower rate ). This provides more predictable budgeting. Instead of the volatile spikes of cloud billing. In essence, DataGPT acts as a buffer and optimizer between the user and the expensive underlying warehouse. The heavy lifting is done on a cost-efficient platform. It’s worth noting that DataGPT’s compute is “way cheaper” for the company itself, allowing them to charge customers a high premium while still saving the customer lots of money (a win-win business model, as the figure suggests).

 

Appendix

Real-World Case Studies from Data-Heavy Industries

  • E-commerce: Shopify’s data team discovered a single analytic query pattern that **would have cost nearly $1 million per month if run at scale on BigQuery​ (shopify.engineering). The query scanned ~75 GB each time; at an estimated 2.6 million executions per month (for a planned marketing tool), the BigQuery usage would approach 2.5 petabytes scanned monthly – almost $1M in charges. Thankfully, they caught this early and re-optimized before general release.
  • Analytics SaaS (Streaming Events): Mixpanel, an analytics provider, found that as they modernized their internal data stack, even their small data team was incurring “five figures a month” in BigQuery analysis charges​ (engineering.mixpanel.com). The monthly BigQuery bill for internal analytics reached tens of thousands of dollars, simply labeled under “Analysis,” until they investigated and optimized costly query patterns. Similarly, Capchase (a fintech SaaS) noted that BigQuery costs “can spiral out of control quickly if you’re not careful”, and by implementing optimizations (reducing scanned data by ~54%), they saved “tens of thousands of dollars each month.” (​medium.com)
  • Retail & Marketing Analytics: A national weight-loss brand (with a subscription food delivery model) ran heavy marketing analytics on Snowflake. An unoptimized marketing application caused **compute and storage costs to spike linearly, trending toward “millions of dollars in cost overruns,” according to a case study (​intricity.com). In other words, the Snowflake bill grew so fast with usage that executive leadership took notice – the company had to urgently seek cost-cutting measures to avoid a multi-million dollar annual bill.
  • Streaming Services & Supply Chain: Industries like video streaming or global logistics generate billions of event records and sensor readings daily, making ad hoc queries especially expensive. One Snowflake user reported a single data pipeline costing $22,000 per month in compute just to continuously stream data out for analysis (for context, that’s over $260K/year for one workload) – an untenable cost without optimization. And in one public incident, a researcher analyzing web archive data on BigQuery accidentally processed ~2.5 petabytes with a few unbounded queries, resulting in a $14,000 charge in just 2 hours​ . The user had no alerts or limits in place and was shocked to “lose $14k in the blink of an eye”​(discuss.httparchive.org). These examples underscore how quickly costs can balloon during exploratory analysis on large-scale data.

Developer Experiences and Cost Complaints

Many engineers and data professionals have shared cautionary tales about unexpected costs when using these platforms for ad hoc analysis directly:

  • BigQuery: Several users have commented on the risk of high query costs. One Reddit user described BigQuery’s allure and sudden expense: “It supercharges a startup – any idiot who can write SQL can do incredible things. Then WHAM. Your $300 bill is $30k.” (​reddit.com) This highlights how easily costs can explode as data volume and query count grow.
  • Databricks: Users have reported that interactive Databricks usage can become pricey. For example, a data engineering team paying about $5,000/month for Databricks SQL queries and dashboards got “heat from senior leadership” over the expense (​reddit.com). In discussions, some mention that serverless Databricks can be more expensive than expected for large workloads, and careful cluster management is needed to avoid waste. The flexibility of spinning up powerful Spark clusters for ad hoc work is a double-edged sword – one comment wryly noted, “Oh look, databricks can be expensive. Who knew? Did leadership not get that info from the databricks sales reps?”​(reddit.com).
  • Snowflake: Snowflake’s cost surprises often come from its consumption model. Users on forums have pointed out the lack of granular per-query cost visibility (until recently) made it hard to attribute and control costs​ (news.ycombinator.com). One Reddit commenter found that in practice, Snowflake was 2–3× more expensive than BigQuery for their use case, “well. In my project the cost from Snowflake is 2x or even 3x. Compared to Bigquery.“ (​reddit.com). Another discussion highlighted how running many small queries can incur disproportionate charges due to the 1-minute billing minimum, advising to tune auto-suspend settings carefully (​keebo.ai). Snowflake’s own documentation and users encourage monitoring “credit burn” closely, since a warehouse running continuously during business hours (e.g., serving a BI tool that executes frequent queries) can rack up significant credits if not right-sized​ (reddit.com).

These real-world experiences underscore a common theme: costs scale with usage, and “uncapped” exploratory usage can lead to budget pain, especially when it does in the uncontrolled way that most of the AI SQL wrapper-based solutions provide. What starts as a quick analysis can turn expensive when multiplied by large data volumes and repeated queries.