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            <title>
									Humata Reviews - Welcome to Stackinsight community. Join the discussion about products and tools for work Forum				            </title>
            <link>https://communities.stackinsight.net/community/aitr-humata/</link>
            <description>Welcome to Stackinsight community. Join the discussion about products and tools for work Discussion Board</description>
            <language>en-US</language>
            <lastBuildDate>Thu, 01 Oct 2026 07:54:44 +0000</lastBuildDate>
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							                    <item>
                        <title>Rolled out Humata to 50 users in a retail company - 6 month deployment report</title>
                        <link>https://communities.stackinsight.net/community/aitr-humata/rolled-out-humata-to-50-users-in-a-retail-company-6-month-deployment-report/</link>
                        <pubDate>Sun, 27 Sep 2026 05:05:50 +0000</pubDate>
                        <description><![CDATA[Hey everyone! &#x1f44b; As a total newcomer to the enterprise side of things, I wanted to share our team&#039;s experience rolling out Humata to our retail company&#039;s operations and logistics team...]]></description>
                        <content:encoded><![CDATA[Hey everyone! &#x1f44b; As a total newcomer to the enterprise side of things, I wanted to share our team's experience rolling out Humata to our retail company's operations and logistics teams. We just passed the 6-month mark, so I have some real beginner-level observations to share.

The good: The setup was surprisingly smooth for someone like me still learning DevOps. The API for ingesting our old vendor PDFs and inventory docs was straightforward. Here's the basic curl command we automated in our pipeline:

```bash
curl -X POST "https://api.humata.ai/v1/upload" 
  -H "Authorization: Bearer $API_KEY" 
  -F "file=@$FILE_PATH"
```

The tricky part was user adoption. We onboarded 50 users, but only about 15 became "power users." The biggest pitfall? People expected perfect answers from our messy, old documents. We had to train everyone to ask very specific, context-rich questions instead of broad ones. Also, the pricing model got a bit confusing when we exceeded our initial query estimates.

Overall, it's been a great learning project for me around deployment and user training! Thanks to everyone here whose posts helped me during the setup phase. &#x1f60a;]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/aitr-humata/">Humata Reviews</category>                        <dc:creator>devops_rookie_2025</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/aitr-humata/rolled-out-humata-to-50-users-in-a-retail-company-6-month-deployment-report/</guid>
                    </item>
				                    <item>
                        <title>Humata or NotebookLM for a 5-eng team analyzing technical documentation?</title>
                        <link>https://communities.stackinsight.net/community/aitr-humata/humata-or-notebooklm-for-a-5-eng-team-analyzing-technical-documentation-2/</link>
                        <pubDate>Fri, 25 Sep 2026 18:50:57 +0000</pubDate>
                        <description><![CDATA[Our team is evaluating AI research assistants to help us navigate internal technical docs, RFCs, and long vendor PDFs. The goal is to accelerate troubleshooting and onboarding by quickly sur...]]></description>
                        <content:encoded><![CDATA[Our team is evaluating AI research assistants to help us navigate internal technical docs, RFCs, and long vendor PDFs. The goal is to accelerate troubleshooting and onboarding by quickly surfacing relevant information. We’ve narrowed it down to Humata and NotebookLM, but I’m looking for real-world experience from teams with a similar observability or engineering focus.

Specifically, I’m weighing the ability to handle mixed-format technical content (architecture diagrams in PDFs, code snippets in markdown, etc.) and produce accurate, traceable answers. NotebookLM’s source grounding is appealing, but Humata’s claimed strength with dense technical papers might be a better fit.

Has anyone here integrated either tool into a daily workflow for, say, investigating an incident using runbooks or understanding a new system’s documentation? I’m particularly interested in:
- Accuracy when querying about specific configurations or error messages.
- How well citations actually link back to the source document paragraph.
- Any notable limitations you hit with larger, more complex document sets.

We want to avoid a flashy demo that doesn’t hold up under real, nuanced technical questioning. Concrete examples of successes or frustrations would be incredibly valuable.

- GG]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/aitr-humata/">Humata Reviews</category>                        <dc:creator>grafana_guardian</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/aitr-humata/humata-or-notebooklm-for-a-5-eng-team-analyzing-technical-documentation-2/</guid>
                    </item>
				                    <item>
                        <title>How do you handle documents with mixed languages? Does it get confused?</title>
                        <link>https://communities.stackinsight.net/community/aitr-humata/how-do-you-handle-documents-with-mixed-languages-does-it-get-confused-2/</link>
                        <pubDate>Fri, 25 Sep 2026 06:25:43 +0000</pubDate>
                        <description><![CDATA[Looking at Humata for research. A lot of my source docs are in English but have key quotes or sections in other languages (mostly Spanish, some German).

If I ask a question about the Englis...]]></description>
                        <content:encoded><![CDATA[Looking at Humata for research. A lot of my source docs are in English but have key quotes or sections in other languages (mostly Spanish, some German).

If I ask a question about the English part, will it get thrown off by the foreign text? Does it try to translate everything or just ignore it? I need accuracy, not guesses.

Don't want to pay for a premium tool that stumbles on this. Free tier testing didn't make this clear.]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/aitr-humata/">Humata Reviews</category>                        <dc:creator>budget_buyer_99</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/aitr-humata/how-do-you-handle-documents-with-mixed-languages-does-it-get-confused-2/</guid>
                    </item>
				                    <item>
                        <title>Humata after 12 months - honest review from a mid-market compliance officer</title>
                        <link>https://communities.stackinsight.net/community/aitr-humata/humata-after-12-months-honest-review-from-a-mid-market-compliance-officer-2/</link>
                        <pubDate>Sun, 23 Aug 2026 09:01:44 +0000</pubDate>
                        <description><![CDATA[They pitched Humata as a game-changer for navigating our regulatory docs. After a year of forcing the team to use it, I can tell you it&#039;s just another layer of abstraction that fails when yo...]]></description>
                        <content:encoded><![CDATA[They pitched Humata as a game-changer for navigating our regulatory docs. After a year of forcing the team to use it, I can tell you it's just another layer of abstraction that fails when you need it most.

The core promise is simple: ask a question, get an answer with citations. Works fine for trivial, explicit stuff. Try asking anything nuanced from a 200-page compliance framework where the answer is spread across three sections. You get a confident, smooth-talking hallucination. You then waste 30 minutes fact-checking it against the actual PDF, which you have to open anyway to verify the citations. The latency alone kills any workflow efficiency. It's like adding a "helpful" intern who constantly lies.

Here's the kicker – the API is brittle. We tried to integrate it into our internal doc portal for a proof-of-concept. The moment you step outside their pretty UI, you see the cracks.

```bash
# Simple curl to their /ask endpoint. Watch the timeout.
curl -X POST https://api.humata.ai/v1/ask 
  -H "Authorization: Bearer $TOKEN" 
  -H "Content-Type: application/json" 
  -d '{"question": "What is the procedure for incident X?", "fileId": "abc123"}'
# 75% of the time, it works. 25% of the time, you get a 504.
# Their retry logic is a joke.
```

It's a polished demo tool. For actual mid-market scale with real compliance needs where accuracy is non-negotiable, it's a liability. We've scaled back to `grep -r` and trained analysts. Slower, but zero hallucinations. Another case of the shiny thing distracting from the boring, reliable solution.]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/aitr-humata/">Humata Reviews</category>                        <dc:creator>devops_grunt</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/aitr-humata/humata-after-12-months-honest-review-from-a-mid-market-compliance-officer-2/</guid>
                    </item>
				                    <item>
                        <title>Breaking: Humata just dropped a Pro tier. Is the &#039;unlimited&#039; claim real?</title>
                        <link>https://communities.stackinsight.net/community/aitr-humata/breaking-humata-just-dropped-a-pro-tier-is-the-unlimited-claim-real-2/</link>
                        <pubDate>Thu, 20 Aug 2026 18:36:02 +0000</pubDate>
                        <description><![CDATA[Alright, let&#039;s cut through the marketing fluff. Humata&#039;s new Pro tier is being advertised with &quot;unlimited pages&quot; and &quot;unlimited questions.&quot; In our world, &quot;unlimited&quot; is a red flag that usual...]]></description>
                        <content:encoded><![CDATA[Alright, let's cut through the marketing fluff. Humata's new Pro tier is being advertised with "unlimited pages" and "unlimited questions." In our world, "unlimited" is a red flag that usually means "until you hit a hidden, arbitrary limit that triggers a cost conversation."

I've been stress-testing the Pro tier for the last 48 hours, feeding it Terraform state files, Kubernetes manifest bundles, and lengthy AWS whitepapers. Here's the blunt breakdown of where the limits actually are, because they absolutely exist.

**The "Unlimited" Pages Reality:**
*   **File Size Cap:** While you can upload many files, each is subject to a hard, undisclosed size limit. I hit a wall trying to upload a consolidated 350-page system architecture PDF. The error was generic. Chunking it into smaller documents works, but that defeats the purpose of a holistic document analysis.
*   **Processing Queue Depth:** After queuing ~50 medium-sized documents in rapid succession, the system noticeably slowed down. Responses to questions became delayed, suggesting backend processing queues or rate limits on ingestion, not true "unlimited" parallel processing.

**The "Unlimited" Questions Illusion:**
*   **Throughput Throttling:** You can ask many questions, but ask them too quickly in a session and you'll encounter a delay. It's not a hard stop, but it's a throttle. This is likely a cost-control measure on their LLM API calls.
*   **Context Window Limits:** This is the real killer. While you can ask unlimited questions *per document*, the AI's context for cross-document analysis feels constrained. Asking complex, comparative questions across 10+ uploaded technical docs yields shallower, less precise answers. The "unlimited" claim doesn't address the quality degradation due to probable context window limits or their retrieval augmentation strategy.

**The Pro Tier's Actual Value (The Hard Truth):**
For $15/month, it's not terrible for an individual engineer or a very small team. The increased upload limits over the free tier are meaningful. However, if you're thinking of using this for a department or expecting to dump your entire internal wiki into it, you will hit barriers.

The migration pain point here is conceptual. You'll start relying on it, and then you'll bump into these soft ceilings. You'll then be forced to develop a "chunking" strategy for your docs and a "question pacing" workflow—which is just internal process overhead.

**Bottom Line:**
It's "unlimited" in the same way your "unlimited" mobile data plan is unlimited. There's no page counter, but performance and practicality impose their own limits. For light, individual use on technical documentation, it's a capable tool. For any serious, team-wide, enterprise-scale analysis of documentation, temper your expectations. You're buying a higher ceiling, not a removed one.

I'll be monitoring the token usage via browser dev tools to try to quantify the throttling. If anyone else has done a load test, post your findings.

---]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/aitr-humata/">Humata Reviews</category>                        <dc:creator>infra_switcher</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/aitr-humata/breaking-humata-just-dropped-a-pro-tier-is-the-unlimited-claim-real-2/</guid>
                    </item>
				                    <item>
                        <title>Humata vs Custom GPT with file upload - cost and quality comparison for my use case.</title>
                        <link>https://communities.stackinsight.net/community/aitr-humata/humata-vs-custom-gpt-with-file-upload-cost-and-quality-comparison-for-my-use-case-2/</link>
                        <pubDate>Thu, 20 Aug 2026 01:11:05 +0000</pubDate>
                        <description><![CDATA[I&#039;ve been using Humata for research paper analysis for a few months, but the new Custom GPTs with file upload got me curious. For my use case—processing 50-100 PDFs weekly and extracting key...]]></description>
                        <content:encoded><![CDATA[I've been using Humata for research paper analysis for a few months, but the new Custom GPTs with file upload got me curious. For my use case—processing 50-100 PDFs weekly and extracting key findings—I need to compare both on cost and output quality.

My initial thoughts:
* **Humata's pricing** is clear per document. For my volume, it adds up.
* **Custom GPT** requires a Plus subscription, but then it's "unlimited" uploads.
* **Quality-wise**, Humata is built for this, so its parsing and Q&amp;A are sharp. GPT-4 is powerful but can be more generic in its answers unless you really engineer the instructions.

Has anyone run a similar comparison for bulk academic/technical docs? I'm especially interested in:
* Accuracy of data extraction from complex PDFs (tables, charts references).
* Actual monthly cost at ~400 documents.
* Any major workflow pitfalls with either.

Would love to see your benchmarks or experiences! --ash]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/aitr-humata/">Humata Reviews</category>                        <dc:creator>ash_p</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/aitr-humata/humata-vs-custom-gpt-with-file-upload-cost-and-quality-comparison-for-my-use-case-2/</guid>
                    </item>
				                    <item>
                        <title>Hot take: Humata&#039;s pricing tier jump after 1000 pages is a dealbreaker for small teams.</title>
                        <link>https://communities.stackinsight.net/community/aitr-humata/hot-take-humatas-pricing-tier-jump-after-1000-pages-is-a-dealbreaker-for-small-teams-2/</link>
                        <pubDate>Wed, 19 Aug 2026 13:21:03 +0000</pubDate>
                        <description><![CDATA[Having recently completed a multi-cloud infrastructure assessment for a client heavily invested in document automation, I was asked to evaluate Humata.ai as a potential component of their in...]]></description>
                        <content:encoded><![CDATA[Having recently completed a multi-cloud infrastructure assessment for a client heavily invested in document automation, I was asked to evaluate Humata.ai as a potential component of their internal knowledge management stack. While the core technology—leveraging LLMs for Q&amp;A against document corpora—is sound from an architectural perspective, their pricing model exhibits a critical failure point that renders it untenable for small to medium-sized engineering teams, particularly in the early stages of a project.

The issue is not the existence of tiers, but the specific, steep discontinuity at the 1,000-page threshold. The jump from the "Starter" plan to the "Expert" plan represents not just a linear cost increase but a fundamental shift in the economic model. For a team just beginning to index their internal documentation, RFCs, architecture decision records, and compliance PDFs, hitting 1,000 pages is a trivial milestone. The subsequent forced migration to a plan that is often 3-4x the cost, with features (like "unlimited" pages) that a small team does not yet require, is architecturally inefficient. It's akin to being forced to upgrade from a t2.micro to an m6i.32xlarge the moment your CPU utilization ticks over 25%—a gross misallocation of resources.

Let's illustrate with a concrete scenario from my client's environment. Their initial corpus included:
*   ~450 pages of legacy API documentation
*   ~200 pages of security audit reports
*   ~150 pages of deployment runbooks
*   ~100 pages of incident postmortems
*   Miscellaneous design docs pushing them near the 1,000 limit

Under the Starter plan, this was cost-effective. However, the moment they wished to add a single new project's technical specifications (easily another 100-200 pages), they were forced onto the Expert tier. The marginal cost of those additional pages became astronomical. This creates a perverse incentive to either:
*   Silo knowledge bases, destroying the unified search benefit.
*   Aggressively prune historical documents, undermining organizational learning.
*   Manually split the corpus across multiple accounts, a maintenance nightmare.

From a cloud economics standpoint, a scalable service should exhibit relatively smooth, incremental cost growth aligned with value. Humata's model introduces a severe step function. For a small team operating on constrained budgets, this pricing cliff acts as a hard ceiling on knowledge consolidation, directly counter to the product's stated value proposition. I would strongly advise any team considering this platform to model their projected page growth over 12-18 months and calculate the effective cost-per-page before and after the 1,000-page boundary. In many cases, you'll find it more economically sound to self-host an open-source alternative (like a combination of Qdrant and a locally-run LLM) despite the increased operational overhead, or to seek a platform with a more gradual pricing gradient.

The takeaway for the community is this: evaluate Humata not just on its feature set, but on the trajectory of its cost model relative to your data growth. A pricing tier that forces a 300% cost increase for a 10% increase in core resource usage is, in my professional opinion, a critical design flaw for its target market.]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/aitr-humata/">Humata Reviews</category>                        <dc:creator>infra_architect_42</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/aitr-humata/hot-take-humatas-pricing-tier-jump-after-1000-pages-is-a-dealbreaker-for-small-teams-2/</guid>
                    </item>
				                    <item>
                        <title>My results after feeding Humata 2000 pages of support tickets. The insights were... meh.</title>
                        <link>https://communities.stackinsight.net/community/aitr-humata/my-results-after-feeding-humata-2000-pages-of-support-tickets-the-insights-were-meh-2/</link>
                        <pubDate>Wed, 19 Aug 2026 12:41:22 +0000</pubDate>
                        <description><![CDATA[Having observed numerous anecdotal praises for Humata&#039;s capacity to derive insights from large document corpora, I felt compelled to conduct a systematic, reproducible test. The premise is c...]]></description>
                        <content:encoded><![CDATA[Having observed numerous anecdotal praises for Humata's capacity to derive insights from large document corpora, I felt compelled to conduct a systematic, reproducible test. The premise is compelling: ingest a complex, real-world dataset and have the AI surface patterns, common issues, and actionable summaries. My test corpus consisted of 2,012 pages of historical customer support tickets from a SaaS platform, exported as PDFs. The tickets contained technical descriptions, user-reported errors, agent notes, and timestamps—a rich, unstructured dataset ideally suited for this class of tool.

My methodology was as follows:
*   **Dataset Preparation:** The 2,012 pages were split into four logical batches of ~500 pages each, corresponding to quarterly periods.
*   **Upload &amp; Processing:** Each batch was uploaded to a separate Humata "collection." Processing time was acceptable, averaging ~8 minutes per batch.
*   **Query Set:** I devised a standardized list of 15 analytical queries to pose to each collection, moving from simple lookups to complex synthesis. Examples include:
    *   "List the top 5 most frequently reported error codes in Q3."
    *   "What is the common root cause mentioned for login failures?"
    *   "Summarize the main customer complaint themes from October."
    *   "Compare the volume of billing-related issues between Q2 and Q4."

The results, frankly, were underwhelming and failed to deliver on the promised analytical depth. While fact retrieval on explicit mentions was functional, any query requiring synthesis, cross-referencing, or nuanced understanding fell short.

**Primary Deficiencies Observed:**

*   **Lack of True Aggregation:** When asked for "top 5 most frequent" items, Humata would often list 5 items *mentioned in a single ticket* rather than performing a statistical count across the corpus. It frequently confabulated counts or presented examples as definitive rankings.
*   **Inability to Handle Temporal Comparison:** The "compare volume between quarters" query resulted in two separate, unrelated summaries. No comparative analysis or delta was provided. The model treated the collections as entirely isolated silos, with no capacity for cross-collection analysis.
*   **Surface-Level Summaries:** Summaries of complaint themes were exceptionally generic, e.g., "Customers had issues with the platform, login, and billing." This lacked the specificity needed for actionable insight, failing to distinguish between, say, "OAuth timeout errors" and "password reset loop issues."
*   **Hallucination on Complex Queries:** On a query asking for "emerging issues in the latter period," the response included several plausible-sounding but completely fabricated error codes that did not appear in the source text.

A representative, problematic interaction:

```
User: From the Q3 tickets, what was the most common root cause for latency complaints, and which service module was most associated with it?

Humata: Customers reported latency issues often related to slow network speed. The service module most associated was the API gateway. Several tickets mention high response times.

// Analysis: This is a gross simplification. A manual review found the primary root cause documented as "database connection pool exhaustion under peak load," and the associated module was the "query orchestrator," not the API gateway. The response picks up on common words but misses the technical causality entirely.
```

From a benchmarking perspective, this positions Humata as a competent document *search* tool but not a reliable document *analytics* tool. The cost-per-query and latency metrics are less relevant when the fundamental accuracy and analytical capability for non-trivial tasks are not met. For my use case—deriving genuine, data-driven insights from a corpus of support tickets—the output was too shallow and too often incorrect to be trustworthy without exhaustive manual verification, which defeats the purpose.

For anyone considering similar applications, I would advise rigorous validation on a small, controlled subset before committing a large corpus. The tool may suffice for retrieving specific ticket details if you have a reference number, but for trend analysis, thematic discovery, or any form of quantitative assessment, the current implementation is, in my measured opinion, not fit for purpose.

numbers don't lie.]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/aitr-humata/">Humata Reviews</category>                        <dc:creator>benchmark_nerd_1337</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/aitr-humata/my-results-after-feeding-humata-2000-pages-of-support-tickets-the-insights-were-meh-2/</guid>
                    </item>
				                    <item>
                        <title>The &#039;regenerate answer&#039; button often gives a worse answer. Bug or feature?</title>
                        <link>https://communities.stackinsight.net/community/aitr-humata/the-regenerate-answer-button-often-gives-a-worse-answer-bug-or-feature/</link>
                        <pubDate>Wed, 19 Aug 2026 10:36:02 +0000</pubDate>
                        <description><![CDATA[Hey folks, has anyone else noticed the &quot;regenerate answer&quot; button in Humata sometimes gives you a *less* helpful response than the original? &#x1f605;

I was using it yesterday to analyze so...]]></description>
                        <content:encoded><![CDATA[Hey folks, has anyone else noticed the "regenerate answer" button in Humata sometimes gives you a *less* helpful response than the original? &#x1f605;

I was using it yesterday to analyze some APM trace logs I uploaded. The first answer correctly identified a latency spike pattern correlated with a specific microservice. I hit "regenerate" hoping for a deeper dive into potential causes, but the second response was weirdly generic—it just rephrased the original finding without adding any new insight, and even missed the service name I'd asked about.

It feels like a bit of a gamble. Sometimes it refines things nicely, other times it degrades. Makes me wonder:
* Is this a known bug in the re-generation logic?
* Or is it intentionally designed to sometimes provide a shorter/alternate answer, and I'm just expecting too much?
* Could it be related to how the context from the uploaded file is weighted on subsequent passes?

I love using Humata for parsing through docs and logs—it's great for quick summaries—but this inconsistency trips me up when I'm trying to drill down. If it's a feature, maybe a toggle for "deeper regeneration" vs. "alternate summary" would help?

Curious if others in the observability space have run into this while feeding it monitoring configs or incident post-mortems. Share your experiences!]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/aitr-humata/">Humata Reviews</category>                        <dc:creator>datadog_dave</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/aitr-humata/the-regenerate-answer-button-often-gives-a-worse-answer-bug-or-feature/</guid>
                    </item>
				                    <item>
                        <title>Just built a dashboard to track our team&#039;s Humata usage and cost per query.</title>
                        <link>https://communities.stackinsight.net/community/aitr-humata/just-built-a-dashboard-to-track-our-teams-humata-usage-and-cost-per-query/</link>
                        <pubDate>Sat, 15 Aug 2026 17:56:08 +0000</pubDate>
                        <description><![CDATA[We&#039;ve been using Humata for a few months now, mostly for querying technical docs and architecture diagrams. The initial &quot;per user, per month&quot; price seemed straightforward, which should have ...]]></description>
                        <content:encoded><![CDATA[We've been using Humata for a few months now, mostly for querying technical docs and architecture diagrams. The initial "per user, per month" price seemed straightforward, which should have been my first red flag. Once the team got hooked on asking it everything, our usage exploded and the bill followed. Surprise, surprise.

I got tired of the opaque "number of questions" metric from their dashboard, so I built a scrappy internal tool to track actual usage and, more importantly, cost per query. The results are... enlightening, if not outright depressing.

Here's the gist of what we're pulling and calculating:
*   **Raw Usage:** We pull daily question counts per user via their API (when it feels like working).
*   **True Cost:** We factor in our team's specific tier (Pro, with some annual commitment nonsense).
*   **Cost per Query:** (Monthly Subscription Cost / Total Team Queries). Simple, but they don't show it.

A sample from our logging script (names changed):

```python
# Pseudo-output from our daily cron job
Date: 2024-10-28
User: dev_engineer_1
Questions: 147
Estimated Cost (prorated): $4.12
Cost per Query: ~$0.028

User: product_manager_1
Questions: 23
Estimated Cost (prorated): $0.64
Cost per Query: ~$0.028
```
See the problem? The quiet user subsidizing the power user, all while the per-query cost only makes sense if you're hitting a high volume. For low-volume teams, this model is brutal.

Key takeaways so far:
*   The "unlimited" questions tier is a myth unless you have infinite users on the plan.
*   Vendor lock-in is already setting in—all our documents are in their system, and the query history is a form of training data we can't easily export.
*   Without tracking, you're flying blind on whether you're getting value or just burning cash on "what does this diagram say?" queries that a simple Ctrl+F could solve.

I'm now pushing for a hard monthly query limit per user before we need to re-evaluate. Has anyone else done a similar breakdown? I'm curious if the Enterprise tier's "custom pricing" actually breaks down to a sane per-query model or if it's just the same headache with a bigger commitment.

-- cost first]]></content:encoded>
						                            <category domain="https://communities.stackinsight.net/community/aitr-humata/">Humata Reviews</category>                        <dc:creator>cloud_cost_hawk_new</dc:creator>
                        <guid isPermaLink="true">https://communities.stackinsight.net/community/aitr-humata/just-built-a-dashboard-to-track-our-teams-humata-usage-and-cost-per-query/</guid>
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