How to Migrate from Maxim AI to Zespan (2026 Guide)

Maxim AI positions itself across the full AI lifecycle: prompt experimentation, agent simulation, pre-deployment testing, and production observability. That breadth is genuinely useful for teams whose main problem is iterating on prompts before release.

Teams move to Zespan when the centre of gravity shifts from pre-deployment experimentation to production behaviour, and when per-user pricing starts limiting who can see what is happening.

Before you start: is migrating worth it for you?

Be clear-eyed here, because this is the comparison where staying put is most often the right answer.

Stay on Maxim if your primary workflow is prompt experimentation and simulation before release. Playground++, the Prompt CMS, and the simulation framework are substantial pieces of tooling built specifically for that loop, and Zespan does not try to match them feature for feature.

Move to Zespan if:

• Production is where your problems actually live. If your incidents are about what agents did in production at 3am rather than how a prompt scored in a test harness, you want a production-first tool.
• Per-user pricing is gating visibility. $29 to $49 per user per month means every additional person who needs to look at agent behaviour has a price tag. Teams end up rationing seats, which defeats the purpose.
• Log limits are shaping your instrumentation. The Developer tier caps at 10k agent logs a month, with paid tiers moving to 100k+. When you start deciding what not to trace in order to stay under a cap, your observability has an accuracy problem.
• Retention is short. Retention runs from 7 days up to 30 days depending on plan. Incident forensics frequently needs to look further back than that.
• You want the loop closed. Maxim feeds production failures into datasets for testing. Zespan goes a step further and proposes the fix, replays it against the failures the incident captured, and gates shipping on human approval.

Step 1: Instrument alongside

Leave your Maxim SDK exactly where it is and add Zespan next to it:

import { zespan } from "@zespan/sdk";

zespan.init({
  apiKey: process.env.ZESPAN_API_KEY
});

Zespan auto-patches providers and registers framework handlers, so you are not rewriting instrumentation call sites.

Step 2: Understand what changes in the data model

Maxim concept	Zespan equivalent	What is different
Agent log	Trace	Zespan does not meter you per log, so you can instrument fully
Span / node	Span	Typed by agent semantics, with delegation as its own event
Dataset (Data Engine)	Dataset	Zespan builds these from captured production failures automatically
Evaluator	Evaluation	12 LLM-as-judge templates run automatically on every trace
Simulation run	Simulation	Zespan scores quality and cost together on the same run
Prompt CMS / Playground++	Prompt Management / Playground	Zespan's is lighter; this is the main capability gap
—	Agent	Registered as a first-class entity on first run
—	Issue	Recurring failures cluster into one tracked object
—	Closed-loop fix proposal	Fix generated, replayed against real failures, gated on approval

Step 3: Port only the datasets you actually use

Export the curated evaluation cases you genuinely depend on in CI and import them as Zespan datasets. Do not bulk-migrate everything.

The reason: Zespan converts real captured production failures into regression test cases on its own. After two or three weeks of live traffic, a meaningful portion of a hand-curated dataset is usually superseded by cases drawn from what actually broke.

Step 4: Reconcile evaluation coverage before cutting over

This is the step people skip and regret. Before removing Maxim, write down every evaluator you currently rely on and confirm each one is either:

1. Covered by one of Zespan's 12 built-in templates, or
2. Ported as a custom evaluation, or
3. Consciously dropped because it was not earning its keep.

Do not cut over with an unreviewed list. Evaluation coverage is the one thing that silently degrades without anyone noticing.

Step 5: Run parallel, then cut over

Keep both live for a release cycle and compare trace volume, cost attribution against your provider invoice, and eval pass rates on comparable checks.

Then remove the Maxim SDK, and reconcile seats — this is where the per-user cost disappears.

What you should expect to gain

• Flat pricing. One price regardless of how many people need access, so you stop rationing visibility.
• No log-volume ceiling shaping what you instrument.
• Delegation, agent identity, and tool choice as native concepts.
• A closed loop: incident to fix candidate to replay against real captured failures to human approval.

What you should expect to give up

Stated plainly, because this is the real trade:

• Prompt experimentation depth. Playground++ and the Prompt CMS are more developed than Zespan's equivalents. If prompt iteration is your main daily workflow, you will feel this.
• Simulation breadth. Maxim invests more in pre-deployment simulation. Zespan's simulations exist and are scored on cost and quality, but the pre-release testing surface is narrower.

If your team's centre of gravity is pre-deployment, Maxim is the better tool and you should stay. If it is production, the trade favours Zespan.

Next steps

• Read the comparison section on the homepage for the capability table across all four platforms.
• See What is AI agent observability? for the underlying model.
• Start free with 10,000 traces a month, no card required.