AI Tools

How to Choose AI Tools

Start With the Deliverable, Not the Model

Write down the artefact you need and the shape it has to arrive in. A support team that wants summarized ticket threads is buying something different from an analyst who needs structured rows extracted from invoices, even though both might be described as a text tool. Pin down the input types first — documents, spreadsheets, images, audio, screen recordings — along with the volume per day and how much manual correction you will tolerate.

Then write one acceptance example by hand: a real input paired with the output you would accept. That example forces the format question early (free prose, a markdown table, or a schema-constrained record) and it becomes the first item in the evaluation set you run against candidates. Teams that skip this step compare feature grids instead, and end up with a tool that is excellent at a task nobody needs or that emits prose where a downstream system expects fields it can parse.

Where the Model Actually Runs

Hosted API access sends each request to a provider’s servers and bills per token: you get the newest capability, the largest models, and no infrastructure to run, in exchange for network latency, cost that scales with traffic, and data leaving your environment. A browser tool wraps the same class of model in a chat interface with file upload and history, which is easy to adopt but harder to script or audit. On-device or locally hosted open-weight models keep prompts and documents inside your own network, at the cost of smaller models, quantised weights that can lose accuracy on hard reasoning, and hardware to provision.

The choice usually splits by task rather than by company. A drafting assistant for published marketing copy can sit behind a hosted endpoint; a workflow that reads employment contracts or clinical notes may need a local deployment or an enterprise agreement with a zero-retention commitment. Ask which region processes requests, whether prompts and outputs are retained, and whether your content trains models by default.

Context Window, File Inputs, and Token Budgets

Every model has a context window: the total tokens of instructions, retrieved passages, conversation history, and generated output it can hold at once. Long PDFs, dense spreadsheets, and hour-long transcripts consume it quickly, and the fix is almost always retrieval rather than a bigger window — split documents into chunks, embed them, and pass only the passages a query needs. Check per-file size caps, page limits, maximum image resolution, and audio length limits before assuming a document-heavy job is covered.

Output length is capped separately from the input window. A tool asked to condense a long report into a fixed-format record can truncate silently when it hits that ceiling. Test candidates with your longest realistic input, not the tidy sample in the vendor’s walkthrough.

Integration Surface and the Return Path

Decide how the tool connects before you fall for its output. A documented API with SDKs, webhooks, and authenticated requests lets you put the model behind your own interface and control the prompt. Chat-only products that rely on clipboard exports force a person to shuttle text between systems, which rarely survives a busy week.

On the API side, look at authentication (a scoped key versus an OAuth grant), rate limits and burst allowances, and whether responses can be constrained to a JSON schema your code can validate. Check identity plumbing if staff will use the tool day to day: single sign-on, directory-based provisioning, and group permissions matter more than a demo. Finally confirm the return path — does output land in the system that owns the record, or stop as a file someone must move by hand?

Data Handling, Retention, and Residency

Read the data terms as a purchase document. The questions that decide most deals: is submitted content used for training by default or only with an explicit opt-in, how long are prompts and outputs retained for abuse monitoring, can retention be configured to zero, which subprocessors touch the data, and in which regions processing happens. Business tiers usually add a data processing agreement, configurable retention, and admin controls that consumer plans do not offer.

Map where sensitive material enters, too. Many disclosures come from staff pasting drafts into personal accounts because the sanctioned tool was slower — a workflow problem as much as a policy one. Choose tools whose admin console gives you visibility, and make the approved path the easiest one to take.

Seats, Permissions, and Administration

Count the people who need access, not the people who asked. Seat-based plans price per named user, so dormant accounts are pure cost, while pooled usage or credit models shift spend toward consumption and reward light users. Look at how roles work: can you grant admin, builder, or read-only viewer? Can a workspace owner publish a shared prompt library and revoke it later?

Check the administrative surface — member management, per-seat usage reporting, export of conversation history, and what happens to a departing employee’s work. A tool with no audit trail is hard to justify once it touches customer-facing output or regulated records.

Evaluate Before Rollout and Price the Exit

Run a structured evaluation rather than a vibe check. Assemble representative tasks with known-good answers, then score each candidate on accuracy, format compliance, latency, and the minutes of review each output still needs. Pilot with the people who will use it daily, keep the current process running alongside, and decide in advance which result would make you walk away.

Cost the exit before you sign. Lock-in shows up in prompt libraries that only work in one vendor’s format, automations wired to a single proprietary endpoint, and archives of generated content that are awkward to export. Ask what you can take with you: prompt definitions, evaluation sets, usage logs, stored outputs. A tool you can leave is a tool you can negotiate with.

FAQ

Should we evaluate on a free tier or the paid plan we would actually buy?

Use the free tier for hands-on exploration, but treat it as a capability test rather than a buying decision. Free plans usually cap context length, message volume, and file uploads, and they often apply different data-use terms than business plans, so what you learn about retention there may not carry over. Run the final comparison on the tier you intend to deploy.

How do we choose between two tools that both seem to do the job?

Put the same set of representative tasks through both, including your longest input and one awkward edge case, and score the results against the acceptance example you wrote before shopping. Weigh the review time each output still needs rather than whether the draft looks right, and check which one you can automate through its API without extra engineering.

Does an enterprise tier really change the privacy terms?

Usually. Business and enterprise tiers tend to add configurable retention, training exclusions, administrative controls, and a signed data processing agreement, while consumer plans often use submitted content for service improvement by default. Confirm in writing which terms attach to the specific plan, and ask which region handles requests.

What do people most often underestimate when comparing tools?

The review step. A tool that drafts in seconds but produces output a person must fact-check line by line can consume more time than doing the work directly. Measure total elapsed time from request to approved output, verification included, before committing a team to it.