Book a Demo

ChatGPT or Claude for business?

By · Published

Should your business use ChatGPT or Claude?

Try both on real work. Pick the one that fits the job. Then keep the rest of your setup in a system your company can manage.

The model matters. Your files, rules, tools, and tests matter too.

The short answer

Choose the model that does your task best. Check cost, speed, quality, and risk. A broad ranking cannot tell you which model will fit each job.

For work that may become a set process, put the model in an agent harness you can manage.

An agent harness is the setup around the model. It holds the rules, files, saved context, tool links, access rules, tests, and logs for a job. It can call a model, check the result, and set the next step.

A harness is not private or local just because it is a harness. Check where it runs and stores data. Check which tools it can use. Check which model firm gets each request.

Start with the job, not the brand

Use real work from your company. Remove private data when the test does not need it.

For each model, check:

  • Did it follow the rules?
  • Did it use the right facts?
  • Can your team check the answer?
  • Did it miss a key detail?
  • How long did the full job take?
  • What did the full job cost?
  • Did it work with the tools and files you need?

One model may be best for a writing task. Another may be best for a long file or a tool-based task. Results can change with a new prompt, model, tool, or source file. Run the same tests after a change.

What the harness lets you keep

Say your team uses AI to make a weekly order report. Your setup has a report form and approved data sources. It has rules for missing orders. It also has a review step and a log.

If your firm controls that setup, you may be able to switch the model and keep the rest. Your rules, files, and source links can stay in place.

There are limits. A switch does not make each feature portable. Models use tools in different ways. The same prompt may get a new result. One model firm may have a tool that the other does not.

Test the whole job after a switch. Test missing facts, bad tool results, blocked access, and other ways it may fail. Check each action and approval rule again.

Company control is not model privacy

Your company may control the harness files and rules. That does not mean the model runs in your office or on your server.

When a harness calls a cloud model API, that firm still gets the request. It also gets the data and context sent with it. “Not used for training” does not mean “not sent.” It also does not mean “not kept.”

OpenAI says it does not train its models on API data by default. Its guide also lists logs and saved app data. The time it keeps data can vary by the API, your settings, and your account. Read OpenAI's API data guide.

Anthropic says it deletes API inputs and outputs from its back end within 30 days by default. It lists cases that may have a longer or shorter term. These include some tools, safety needs, the law, and special deals. Read Anthropic's data retention guide.

Terms can change. Check the product, API, model, region, account settings, and deal you use. Send only the data that is allowed for the job.

Local storage and local inference are different

A harness can keep files on a company PC and still send some text to a cloud model. It can also run on a host and call a local model. Where you store data and where the model runs are two different choices.

Some harnesses work with many model firms. Some can use local model links. OpenCode shows both types in its docs. This shows that a switch can be built. It does not mean each model or tool will work the same way. See OpenCode's model setup. See its provider setup.

Before you call a setup local or private, ask:

  • Where does the harness run?
  • Where does it keep files, logs, and saved context?
  • Which text and files leave it?
  • Which firm gets them?
  • How long can each firm keep them?
  • Who can read or change the setup?

Control does not settle ownership or security

Keeping your setup can make it easier to move or check. It does not settle who owns each input or output. It does not change a firm's license terms. It also does not prove privacy or security.

Review the deal, access rights, data rules, logs, backups, and tool rights. Get legal, privacy, or security help when the risk calls for it.

Give the harness only what the job needs. Keep a person in charge of spend, customer promises, deleted data, access changes, and other high-impact acts.

A practical choice process

  1. Pick one small, useful job.
  2. Write what a good result must have.
  3. Choose a few normal and hard test cases.
  4. Run the same cases with each model.
  5. Check quality, review time, speed, cost, and failures.
  6. Pick a model for that job, not for all work.
  7. Keep rules, source links, tests, and tool rights in the harness.
  8. Test again before a model or tool change goes live.

The best answer may be ChatGPT for one job and Claude for the next. It may change over time. A company-run harness can help you change the model and keep the work around it.

That gives you more control of your work setup. It is not a promise of full portability, ownership, privacy, security, or results.