Apache AI tools: useful parts, real development costs

Apache can supply the parts of an AI application, but none of these projects is a ready-to-run small-business assistant. Texera needs IT help; OpenNLP, Burr and the rest need developers who will test the result and maintain it.

Start with a task you can judge. Sorting copied maintenance notes, reconciling yesterday's production report or checking a quote form for missing dimensions is a better starting point than asking for an AI that runs the business. You need examples of correct and incorrect output before you can choose a model or a workflow engine.

This group mixes several jobs. OpenNLP analyzes language, Burr coordinates application steps, Texera assembles analytical workflows and TVM compiles models for hardware. Downloading one does not supply all the others. We read official documentation and vendor prices on October 29, 2026; we did not install, train or benchmark these projects for the series.

October 30, 2026 update: added setup time and costs when an AI agent does the typing.

Every project in this group

Registry dates may lag current work; file dates are artifact modification times. OpenNLP's directory entry was stale: the official news dates milestone 3.0.0-M6 and stable 2.5.12 to September 11, 2026. The milestone is not the stable release. Mahout's stale directory entry was also replaced: QuMat 0.6.0 was published May 31, 2026. That is the GitHub publication date, not the later announcement date. Incubating projects are not yet fully endorsed by the ASF; incubation is not a technical-quality guarantee.

Texera: a shared analytical workflow still needs an operator

Texera gives analysts a visual way to assemble and inspect data workflows, with collaboration and custom code where needed. It may fit a business with a capable analyst who wants a repeatable process instead of passing spreadsheet copies around. Its release page identifies 1.2.0-incubating; the published release is dated August 14, 2026. A visual interface does not remove the server and data-management work.

For example, a 12 person machine shop wants rejected parts, material batches and test results in one morning report. Two technical staff build a workflow over exported files, and the foreman reviews the exception list. First they reconcile the output against a known batch. Only then should they consider whether a predictive model adds anything. The low labor case below prices operation of that trial, with two paid editors in the hosted comparison.

A two-location coffee business with 14 staff wants purchasing decisions informed by register totals and waste logs. Two managers edit a daily workflow, and other staff read the result. Use the high labor case for the same two editors and a less tidy operating routine. The normal-day change is one repeatable report with visible steps; there is no invented forecast-accuracy or waste-reduction percentage attached to it.

Use the documented installation size, then measure your own workflow

The versioned Docker guide specifies a minimum of 2 CPU cores, 4 GB RAM and 20 GB disk. Its recommendation is 8 CPU cores, 16 GB RAM and 50 GB disk. It provides a single-machine Compose route and describes persistent volumes. Follow the release's bundle and instructions rather than assembling unrelated images from search results.

Our cost model selects the shared Large allocation, with 8 vCPU, 32 GiB RAM and 100 GiB disk, as a planning allowance above the memory and disk recommendation. Virtual CPUs are not a benchmark result. Test a representative workflow, concurrent editors and retained output before approving that size. A minimum installation that opens successfully does not prove the largest monthly dataset will finish.

IT must protect access, maintain the containers, back up PostgreSQL and workflow files, and confirm that results survive a restart. A single host has a single point of failure. The local Kubernetes guide is another development route, not evidence that production universally requires three nodes.

Start with sanitized exports and an answer a person can calculate independently. Stop a failed run visibly rather than silently reusing yesterday's result. Skip Texera for one simple report a month or when nobody owns the dataset and restore procedure.

Cost against hosted analytical notebooks

Prices are US dollars before tax, read October 29, 2026. Our common labor rate is $68.09/hour: the BLS sysadmin median wage of $47.66 divided by the 70.0% wage share. It is an employee compensation proxy. Outside IT providers charge more, so get a quote rather than treating these figures as their tariff.

DigitalOcean Small is $24/month for 2 vCPU, 4 GiB RAM and 80 GiB disk. The selected Large plan is $252/month for 8 dedicated vCPU, 32 GiB RAM and 100 GiB disk. These allocations are our planning assumptions. Retained backup volume is fixed at 100 GB at both ends; B2 gives (100 minus 10) x $0.00695 = $0.6255/month. No excess egress or chargeable Class D calls are assumed.

Setup = setup hours x $68.09. Monthly = host + backup + monthly administration hours x $68.09. The 36 month total = setup + 36 x monthly. Savings = alternative total minus self hosted total. Break even is ceiling(setup difference divided by monthly savings), or never when self hosting already costs more each month.

These are operation-only comparisons for prepared workflows. Bespoke development, data cleaning, annotation, model training, evaluation, integration, migration, staff training, tax and major incidents are excluded on both routes. Alternative incremental setup is zero at both ends for an existing account or prepared workflow, not a promise of free adoption. Do not use a cheap runtime calculation as a quote for building an AI product.

Deepnote Team lists $39/editor/month billed yearly. Two editors cost $78/month equivalent, paid as $936/year; 36 months is $2,808 at an unchanged price. Viewers are not paid editors on the checked plan. Extra compute and AI usage can increase the bill. Deepnote notebooks are an alternative way to do the analytical work, not a managed Texera service or a guarantee of identical features.

Our Texera model uses one Large server, 24 to 60 setup hours and 6 to 16 monthly administration hours. We apply the IT oversight allowance to its multiple services even though they share one host. Both cases hold two hosted editors and 100 GB retained backups fixed.

At that small team size, the hosted base plan is cheaper. Texera needs a reason such as a required visual workflow or a data-handling constraint that justifies the extra operation. Have the analyst test both ways of producing the same report before buying a larger server.

Setup time when Claude or ChatGPT does the typing

We keep Texera's Large server, 100 GB backups and two-editor comparison unchanged. Our IT planning assumptions reduce setup to 65% to 90% of manual hours and monthly administration to 80% to 100%. These are scenarios, not measured Texera deployments; coding studies do not establish server setup savings. Review already occupies 6 to 18 setup hours and 3 to 8 monthly hours within the AI totals. Prompting, testing and corrections count as human work; unattended agent time does not.

Manual total = S x $68.09 + 36 x (H + A x $68.09). AI total = S x q x $68.09 + T0 + 36 x (H + A x r x $68.09 + Tm). S/A are manual setup/monthly hours, q/r the AI shares, H = $252.6255 monthly hosting plus backup, and T0/Tm setup/monthly tool fees. We round totals only after calculating.

We charge one $100 setup month for either subscription, then allocate $50 monthly for 36 operating months only when other work uses the remaining half. With exclusive use, add $1,800 to either subscription total. Equal prices do not mean equal allowances; check limits before work starts. [49, 50]

API fees use our cumulative IT budgets: setup input/output of 1 million/100,000 tokens low or 5 million/500,000 high; monthly input/output of 500,000/50,000 low or 2 million/200,000 high. Low input is 30% ordinary, 10% short cache writes and 60% reads; high input is uncached. Retries and billed reasoning are included; these are separate metered charges, with no subscription credits deducted. [51, 52]

Claude Code or Codex can prepare Texera's Compose configuration and restore commands for IT review. Desktop Commander on the authorized server can install dependencies, edit configuration and read container logs; its free local service is included here. Optional Remote Pro adds $20 for setup and $10 monthly at the same allocation, or $380 total. [53, 54, 55]

The Claude in Chrome extension can inspect the provider panel, Cloudflare DNS and Texera workflow UI while you watch. ChatGPT Work offers a separate cloud browser where available; confirm account access rather than promise an equivalent Chrome extension. [56, 57]

Use a new or staging server with approved commands or a restricted sandbox, and keep secrets out of chat. Use subscriptions for supervised supported tools and API keys for custom automated pipelines. Review changes, watch dashboard actions and test a PostgreSQL and workflow-file restore; we still own backups, updates and the decision. [58, 59]

Our conclusion stays unchanged: Deepnote's $2,808 base subscription remains cheaper. In the high case, recurring tool charges exceed setup savings within 36 months because monthly labor does not fall.

OpenNLP: analyze text locally without pretending it is a chatbot

OpenNLP supplies Java APIs and command-line tools for tasks such as splitting sentences, identifying names and categorizing documents. It does not answer arbitrary questions like a generative assistant. Your developer chooses the model, prepares inputs and evaluates the errors.

The download page lists stable 2.5.12 and milestone 3.0.0-M6. Both were announced September 11, 2026. For a stable trial, use the 2.5.12 documentation and appropriate models; do not select a milestone merely because its first version number is larger. The 2.x branch requires JDK 17, while the 3.x development branch requires JDK 21. The branch and release matter when checking requirements.

For example, a six person property manager wants names and places extracted from stored maintenance notes into a review queue. A developer tests local extraction on copied notes and leaves staff to confirm the address and urgency. The low case uses a monthly batch of 10,000 texts of 550 characters each, including reprocessing older notes. It is a workload assumption, not a claim about how many new requests that business receives.

A seven person independent retailer wants names and locations mentioned in feedback pulled into a weekly review file. A developer runs the same narrow extraction task and staff inspect the mistakes. The high case holds the 10,000-text batch fixed while allowing more maintenance time. Custom product categories or sentiment analysis are different tasks and need their own model, tests and price comparison.

A small CPU trial is a budget allowance, not a hardware promise

The 2.5.12 manual documents command-line and library use. The checked manual and FAQ do not publish one CPU, RAM or disk minimum for all models. Our Small server is a 2 vCPU/4 GiB CPU-inference pilot. Memory depends on the model and batch; a developer must measure both. We found no GPU requirement for the conventional tasks examined, which is not a statement about every optional integration.

Pretrained models are a starting point, not evidence that your notes will be understood correctly. Keep a held-out set of manually checked examples, including abbreviations, misspellings and missing addresses. Measure missed items as well as false matches. Staff should see uncertainty and be able to correct the output without fighting an automatic decision.

The security page makes an important distinction: input text is untrusted, while models, dictionaries and training data are trusted assets. Treat an unvetted model download with the care you would apply to executable software. Local processing alone does not make an arbitrary model safe.

Skip a custom model when a few reliable rules solve the task, or when nobody can label enough examples to tell good output from bad output. Start with the official binary distribution or dependency instructions, a trusted model and copied records. Keep the original text so a reviewer can understand an extraction.

Cost against one standard Comprehend analysis

Amazon Comprehend pricing charges standard analysis in 100-character units, with a minimum of three units per request. Its example uses 10,000 comments of 550 characters: 10,000 x ceiling(550/100) x $0.0001 = $6. This is one standard API operation at ongoing list pricing, excluding the introductory free tier. Calling several analyses multiplies requests; custom classification and training have different prices.

We compare that narrow extraction workload with one Small local server, 4 to 12 setup hours and 1 to 3 monthly hours. Integration, labeled data and evaluation are common exclusions, not free work. Neither service has been tested on the hypothetical business's text.

The managed API is cheaper at this volume. Local processing may still be required by a documented data-control policy or a custom language need. That is a separate decision from the runtime bill, and the model still has to pass the business's tests.

Burr: write the review step into the application

Burr is a Python framework for workflows that move through states and actions. Those actions can call ordinary code or a model API. The framework records what happens, but a developer still builds the useful application and decides which permissions each action receives.

The official downloads list 0.43.0-incubating with an August 25, 2026 publisher date. The GitHub release was published August 29. The table keeps the catalog's GitHub publication date; the two dates are different evidence, not silently interchangeable.

For example, a nine person landscaping crew submits completed-job forms. A developer uses Burr to check missing fields, prepare proposed follow-up tasks and stop for office review. On a normal day, the office person sees what is missing and decides what to send or record. The low cost case below covers a lightweight workflow coordinator without model calls. It does not include building the forms or connecting the job system.

A five person print shop wants quote requests checked for dimensions, material and due date before the clerk prices them. The developer expresses those checks as ordinary code, keeps a record of the steps and waits for the clerk's decision. The high case allows more runtime administration. No language model is needed for that narrow completeness check; adding one introduces its own charges and error modes.

Stopping a workflow is not the same as obtaining approval

The installation guide starts with the official Python package. Core support begins at Python 3.9; the current optional CLI extras require Python 3.10. No universal RAM or CPU minimum is published in the checked instructions. Our 4 GiB Small server is an allowance for lightweight coordination, not hardware for hosting a large language model.

The human-review documentation describes pausing before an action. Your surrounding application must collect the person's decision and start the next run. Burr does not itself supply a complete approval screen or prove that a valid approval occurred. Record who approved which proposed action, check authorization and make retries safe against duplicate effects.

The deployment guide discusses local tracking for development or smaller workloads and object-storage-backed tracking for larger ones. Traces can contain prompts, business records and outputs. Decide retention and access before collecting everything. Keep the permissions for reading a form separate from permissions to send a quote or place an order.

Start with deterministic checks, copied input and a review-only output. Skip a custom Burr application when an existing connector workflow meets the requirement and someone already knows how to maintain it.

Cost against a hosted automation starting plan

Zapier Professional starts at $19.99/month billed yearly, or $239.88/year. Its 36 month starting subscription is $719.64, rounded to $720 in the table. The checked base card did not establish a task allowance for that price. Confirm the actual task count and required features before treating it as a workload quote.

This is a conditional subscription-floor comparison for a simple prepared workflow, not a claim that every Burr application fits that tier or that Zapier provides identical custom state handling. Both examples avoid model calls. Burr's model APIs, local inference hardware, application programming and integrations would be extra. Our runtime assumes one Small server, 4 to 12 setup hours and 1 to 3 monthly hours.

The starting hosted subscription is cheaper than this local operating floor. A developer may still need Burr for a custom state machine inside an existing application. Ask for the application design and actual usage quote before relying on the displayed difference.

Needs your IT department or a provider

Texera is the only IT-tier catalog entry in this group and is covered above. Its documented minimum is a starting point for installation; its recommended configuration and your measured workflow should drive the quote. Ask who owns the data stores, how workflows and results are restored together, which users can run code, and what happens when the sole host fails. Our 6 to 16 monthly hours are an operating allowance, not a guaranteed incident response time. Deepnote's $39/editor/month annual plan is a different hosted analytical route, with usage charges to check.

Your developer's toolbox

• Burr (Incubating): a developer writes stateful Python workflows, including the review step described above; the framework does not supply a finished business assistant.
• cTAKES: specialized clinical-text software may use it to extract medical concepts. Before storing patient text, check HIPAA requirements and the terms for models and data; this is not a clinic chatbot or a compliance claim.
• Magpie: your software maintainer may use its agent-assisted triage and proposed fixes. Human review still decides what code reaches your business application.
• Mahout: the current project emphasizes Qumat and QDP for quantum machine learning. The table uses the current QuMat release instead of the stale 0.5 directory record; classic code is in maintenance mode.
• Maka (Incubating): a developer may build an agent workspace with action records and permissions. An activity log does not prove an action was authorized or correct.
• OpenNLP: your developer supplies language models and evaluates extraction errors; it is not a general answering chatbot.
• Singa: a model developer may use it to build and train neural networks. Training hardware and labeled data need a separate budget.
• Sourcelume: training-data provenance records can describe declared origin and licensing. They do not independently establish legal permission to use every item, and no numbered release was verified here.
• SystemDS: developers can run scripted analysis and machine-learning workflows. The owner should ask what data and test set support the resulting business prediction.
• TVM: a developer may compile models for particular processors. The compiler does not supply the trained business model, its training data or an accuracy guarantee.
• UIMA: document-processing applications can combine analysis components through this framework; Java SDK and Ruta releases have their own version numbers.

Risks and support

Treat an attractive demo as a proposed workflow, then test it on examples it has not seen. Keep the original inputs, model version, code version and the person's review decision together. A readable record lets you investigate mistakes; it does not prevent them by itself. Avoid giving an experimental workflow authority to send money, change prices or delete records merely to make the demo look complete.

Review OpenNLP security, Burr security and ASF guidance. Texera's community channels and release notes are the place to check deployment issues. We did not verify a priced commercial maintenance package for these three Apache installations. Ask a prospective provider for supported versions, update duties and response terms; the alternative vendors support their own products.

A quiet registry date is not enough to declare a project abandoned. Check current release pages, mailing lists and the Apache Attic. Mahout's newer research direction and stale registry number show why the distinction matters. OpenNLP's official news, by contrast, explicitly records the older 1.9.x line reaching end of life on August 10, 2026.

The Apache License 2.0 permits commercial use and modification under its conditions, including applicable notices on redistribution. It provides no warranty or blanket rights to model weights and training datasets distributed under other terms. This is a plain-language explanation, not legal advice; the license also does not let you imply ASF endorsement.

Download the kit

Download the AI and machine-learning kit for trial cards, review questions and filled cost worksheets. The small-budget discipline in The $100 Network applies: pay for a specific useful task, and know who will maintain it.

Related reading

• Apache projects for small business
• Big data and lakehouse
• Workflow automation
• Search and knowledge bases
• Self hosted AI server guide

Fact-check notes and sources

Project and new price checks were read October 29, 2026, from 21:38 to 21:43 UTC, with the Mahout release rechecked at 22:04 UTC; catalog and shared assumptions were checked earlier that day. All business examples are illustrations. None is a customer case study, installation result or accuracy benchmark. Starting-plan and incomplete-usage comparisons remain labeled as such.

1. Apache Burr (Incubating), Build Reliable AI Agents and Applications. Read October 29, 2026.

2. burr release v0.43.0-incubating on GitHub. Read October 29, 2026.

3. apache/ctakes on GitHub. Read October 29, 2026.

4. projects.apache.org, projects.json. Read October 29, 2026.

5. apache/magpie on GitHub. Read October 29, 2026.

6. magpie release 0.1.0 on GitHub. Read October 29, 2026.

7. Home, Apache Mahout. Read October 29, 2026.

8. mahout release mahout-qumat-0.6.0 on GitHub. Read October 29, 2026.

9. Apache Maka (Incubating). Read October 29, 2026.

10. Index of /incubator/maka/0.2.0-incubating. Read October 29, 2026.

11. Apache OpenNLP. Read October 29, 2026.

12. News, Apache OpenNLP. Read October 29, 2026.

13. Apache SINGA · Distributed deep learning system. Read October 29, 2026.

14. Index of /singa/5.1.0. Read October 29, 2026.

15. Apache Sourcelume. Read October 29, 2026.

16. Apache SystemDS, An open source ML system for the end-to-end data science lifecycle. Read October 29, 2026.

17. Index of /systemds/3.4.0. Read October 29, 2026.

18. Apache Texera. Read October 29, 2026.

19. texera release v1.2.0-incubating on GitHub. Read October 29, 2026.

20. Apache TVM. Read October 29, 2026.

21. tvm release v0.27.0.post1 on GitHub. Read October 29, 2026.

22. Apache UIMA, Apache UIMA. Read October 29, 2026.

23. Apache UIMA, Downloads. Read October 29, 2026.

24. Incubation Policy. Read October 29, 2026.

25. Apache Texera 1.2.0-incubating, Apache Texera. Read October 29, 2026.

26. Installing Apache Texera using Docker, Apache Texera. Read October 29, 2026.

27. How to run Texera on local Kubernetes, Apache Texera. Read October 29, 2026.

28. Occupational Employment and Wage Statistics Profiles. Read October 29, 2026.

29. Bureau of Labor Statistics Data. Read October 29, 2026.

30. Droplet Pricing, DigitalOcean. Read October 29, 2026.

31. B2 Cloud Storage Pricing, Backblaze. Read October 29, 2026.

32. Deepnote Pricing. Read October 29, 2026.

33. Download, Apache OpenNLP. Read October 29, 2026.

34. opennlp opennlp-2.x branch on GitHub. Read October 29, 2026.

35. apache/opennlp on GitHub. Read October 29, 2026.

36. Apache OpenNLP Developer Documentation. Read October 29, 2026.

37. FAQ, Apache OpenNLP. Read October 29, 2026.

38. Security, Apache OpenNLP. Read October 29, 2026.

39. Amazon Comprehend Pricing. Read October 29, 2026.

40. burr.incubator.apache.org, downloads. Read October 29, 2026.

41. Installation, Apache Burr. Read October 29, 2026.

42. Human in the Loop, Apache Burr. Read October 29, 2026.

43. Deployment, Apache Burr. Read October 29, 2026.

44. Plans & Pricing, Zapier. Read October 29, 2026.

45. Security, Apache Burr. Read October 29, 2026.

46. ASF Security Team, Apache Software Foundation. Read October 29, 2026.

47. The Apache Attic, The Apache Attic was created in November 2008 to provide process and. Read October 29, 2026.

48. apache.org, LICENSE 2.0. Read October 29, 2026.

49. Claude Max plans. Observed October 29, 2026, 23:31:40 UTC; supplied research.

50. ChatGPT pricing. Observed October 29, 2026, about 23:27 UTC; supplied research.

51. Claude API pricing. Observed October 29, 2026, 23:31:14 UTC; supplied research.

52. OpenAI API pricing. Observed October 29, 2026, 23:31:13 UTC; supplied research.

53. Codex CLI. Observed October 29, 2026, 23:32:01 UTC; supplied research.

54. Desktop Commander pricing. Observed October 29, 2026, 23:31:08 UTC; supplied research.

55. Desktop Commander documentation. Observed October 29, 2026, 23:31:41 UTC; supplied research.

56. Claude in Chrome. Observed October 29, 2026, 23:31:11 UTC; supplied research.

57. ChatGPT Work cloud browser. Observed October 29, 2026, 23:32:01 UTC; supplied research.

58. Anthropic Consumer Terms. Observed October 29, 2026, 23:31:08 UTC; supplied research.

59. OpenAI Terms of Use. Observed October 29, 2026, about 23:27 UTC; supplied research.

60. METR experienced developer study. Observed October 29, 2026, 23:30:58 UTC; supplied research.

61. Codex with a ChatGPT plan. Observed October 29, 2026, 23:31:16 UTC; supplied research.

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