Maitai
Maitai is a control plane for production AI that indexes live traffic, evaluates it, and turns it into better models, workflows, and agents.
If your AI already touches money, schedules, or compliance, Maitai's premise — that production traffic should train the next version — is the right one, and few platforms connect observability to fine-tuning and release management in a single loop. The published $200/month Professional tier is the real entry point for teams; $50/month Starter is a single-user sandbox. Named competitors like LangSmith and Braintrust do tracing well but stop short of the fine-tuning and release-management half, so you would bolt on a second tool. Pass if you are still prototyping, or if all you want is dashboards.
Verified 5d ago · liveness 61/100 · cite: rightaichoice.com/tools/maitai
- Teams running AI agents in production where bad outputs cost money or create liability
- Regulated industries needing SOC 2 Type II / HIPAA compliance and VPC or on-prem deployment
- Voice agents that must reason for 10+ minutes and reliably chain tool calls
- ML and platform teams that want fine-tuning, evals and release management in one place
- Solo builders still prototyping — the Starter tier is one active user and the value loop is team-shaped
- Teams that only need traces and dashboards
- Organizations that want prompt management, evals and fine-tuning on separate tools
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Skip Maitai if you only want traces and dashboards and are happy to keep evals, fine-tuning and release management in separate tools.
Indexed traffic retention is tier-bound — 30 days on Starter versus 90 days on Professional — so long-horizon review of last quarter's failures needs the upgrade.
Starter at $50/month flat plus usage fits a single technical operator proving the loop on one workload. Professional at $200/month flat per team plus usage, with 5 active users, 1M indexed requests per month and 90-day retention, is where most production teams land. Enterprise is contact-sales with custom volume pricing, dedicated inference and continuous learning. Compared with observability-only tools, you are paying for the fine-tuning and release half as well.
In short
Maitai — Maitai is a control plane for production AI that indexes live traffic, evaluates it, and turns it into better models, workflows, and agents. Best for Teams running AI agents in production where bad outputs cost money or create liability, Regulated industries needing SOC 2 Type II / HIPAA compliance and VPC or on-prem deployment, Voice agents that must reason for 10+ minutes and reliably chain tool calls. Free to start; paid plans from $50/mo.
What people actually say about Maitai — is it worth it?
We scanned public community sources for Maitai on Jul 24, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Our own analysis of that scan says the posts were off-subject. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
Viability Score
How well maintained and how widely used is Maitai? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Production learning loop that indexes requests, traces, outcomes and feedback from live traffic
- SDK integration to capture traces and manage the AI lifecycle in the Maitai Portal
- Maitai Mojo contextual assistant that analyzes runs and builds datasets on command
- Automated dataset curation for fine-tuning and distillation pipelines
- Real-time monitoring of LLM requests, workflow steps and agent actions
- Offline evals, benchmarking and regression suites before promotion
- Multi-step agent tracing with behavioral evals
- Sentinels that intercept and correct model outputs in real time
- Version control for models, workflows, agents, datasets and test sets with promote and rollback
- Agent ACLs that restrict PII and sensitive data from downstream subagents
- Voice agent reasoning engine with sub-100ms p95 response
- Sub-second classification with confidence flags and reasoning
- Bring your own model keys or use Maitai-hosted models
- On-prem, VPC and air-gapped deployment on AWS, GCP and Azure
- SOC 2 Type II, HIPAA compliant, GDPR ready security with AES-256 encryption and RBAC
About Maitai
Maitai is the layer that sits around the models, workflows, and agents you have already shipped, and turns live traffic into measurable improvement. Requests, traces, tool calls, and user outcomes are indexed and organized so AI behavior becomes searchable evidence rather than log noise. From there you monitor production in real time, curate examples into datasets and test sets, benchmark candidate models before promotion, and release with instant rollback. It is aimed at teams running consequential AI rather than demos: voice agents handling 12-minute calls, customs classification, scheduling agents that charge cards. Maitai reports 350ms p95 response for classification workloads and sub-100ms reasoning for voice agents — the range where generic LLM wrappers start dropping tool calls. The vendor's published figures include 96ms p95 response, 12+ minute average call duration, and 99.8% task completion for voice agents, and 99% accuracy on tariff classification. The toolkit covers three layers. Models: automated dataset curation, fine-tuning, and distillation. Workflows: versioning, built-in profiling, rollback controls. Agents: multi-step tracing, behavioral evals, safety guardrails, plus Sentinels that intercept and correct bad outputs before they reach users, and Agent ACLs that restrict what subagents can see. Maitai Mojo is a contextual assistant inside the Portal — ask it to analyze a test run, or have it build a dataset and kick off a fine-tune. Security is aimed at regulated buyers: SOC 2 Type II, HIPAA compliant, GDPR ready, AES-256 encryption at rest and in transit, RBAC, and deployment in your own VPC or on-premise infrastructure on AWS, GCP, or Azure.
Behind the Verdict
Maitai's bet is that the interesting problem is no longer building an agent — it is what happens to the thousands of real requests that agent handles after launch. Everything on the site is organized around that loop: index requests, traces, workflow steps and agent actions; monitor and evaluate live traffic; curate the examples worth keeping; then improve models, workflows, or agents and promote a version. The strongest part of the product is the release machinery. Models, workflows, agents, datasets and test sets are managed as code with a version history and promote/rollback actions — the site shows a concrete example with v1.2.28 waiting to promote to prod while v1.2.27 is active. Agent ACLs let you pass full context to a router agent while restricting PII from downstream subagents, which is the kind of control that regulated buyers ask about and most observability tools do not offer. Sentinels monitor, intercept and correct model outputs in real time before they reach users. Maitai Mojo is a contextual assistant in the Portal that will analyze a test run or build a dataset and fine-tune a model on command, which shortens the gap between noticing a problem and acting on it. The latency claims are the differentiator for voice and classification work. Maitai publishes 96ms p95 for voice agent reasoning with 12+ minute average calls and 99.8% task completion, and 350ms p95 with 99% accuracy for tariff classification. Those numbers matter because long voice conversations that chain calendar lookups, card charges and SMS confirmations fail when reasoning is slow. Where it fits: teams running agents whose failures cost money or create liability, and regulated organizations that need SOC 2 Type II, HIPAA, RBAC, AES-256, and VPC or on-premise deployment on AWS, GCP or Azure. Where it does not: solo builders. Starter is one active user and the value loop is team-shaped. Teams that only want traces and dashboards will find an observability-only tool cheaper. And teams that deliberately want prompt management, evals and fine-tuning split across separate vendors are fighting the entire premise, since the product's value is that these live in one system. One caveat worth stating plainly: continuous learning depends on consistent user feedback. If your product has no reliable signal about whether an outcome was good or bad, the improve half of the loop runs on weak data.
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Real-world workflow fit
Concrete scenarios for the personas Maitai actually fits — and what changes day-one when you adopt it.
Their scheduling agent ships daily but nobody can prove a prompt change helped. They drop in the Maitai SDK to capture traces, index a week of production traffic, curate the failing traces into a test set, and benchmark the candidate model against the current prod version.
Outcome: Regression suite cleared before promotion, and the team promotes the new version with rollback available if production behavior regresses.
Long calls keep dropping tool calls after the ten-minute mark — calendar lookups succeed but the card charge or SMS confirmation never fires. They route agent reasoning through Maitai's voice reasoning engine and add Sentinels to intercept malformed outputs.
Outcome: Reasoning stays in the sub-100ms p95 range through 12-minute calls, tool calls chain reliably, and corrected outputs reach the caller instead of raw failures.
Generic LLMs guess on tariff codes that carry real penalty exposure. They train a task-specific classification model on curated production examples, keep it in their own VPC, and require reasoning plus confidence flags on every code.
Outcome: Classification runs at 350ms p95 with 99% accuracy, each answer carries defensible reasoning and a confidence flag, and no data leaves their network.
Use Cases
- Build a voice agent that books meetings, charges cards and sends confirmations across 12-minute calls
- Train a task-specific customs classification model that returns tariff codes with reasoning and confidence flags
- Index production agent traffic and curate failing traces into a benchmark set before a model promotion
- Deploy Sentinels to catch and correct bad model outputs before they reach end users
- Restrict PII from downstream subagents with Agent ACLs while keeping full context at the router
- Turn production traces into training data and distill a smaller model for cheaper inference
- Version workflows and roll back a bad release with instant rollback
- Run offline evaluations and regression suites on a candidate model before it goes to production
Limitations
- Continuous learning depends on consistent user feedback; if your product has no reliable signal about whether an outcome was good, the improve half of the loop runs on weak data.
- The vendor also notes that results can suffer on highly specialized industry jargon without adequate training data.
- Enterprise deployment in your own VPC or on-premise involves custom scoping and pricing rather than a published rate.
- The value loop is team-shaped — the Starter tier covers one active user and not every capability scales down to a solo operator.
as of 2026-10-03
Verification history
We have re-verified Maitai 8 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
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Showing the 6 most recent of 8 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Maitai tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Starter
$50/month flat + additional usage
Ideal for
A single technical operator or founder proving the production learning loop on one workload before spending on a team plan.
What this tier adds
Starting tier. $50/month flat plus additional usage for 1 active user, with indexed traffic retained for 30 days and unlimited datasets, test sets, monitors and Sentinels.
Professional
$200/month flat per team + additional usage
Ideal for
A production team of up to 5 working a shared agent or model, where a month of retention is not enough to catch slow regressions.
What this tier adds
Adds up to 5 active users, 1M indexed requests per month, 90-day retention on indexed traffic, 3 hosted models, expanded Mojo/agent/workflow allowances and Slack support.
Enterprise
Custom
Ideal for
Regulated organizations that need their data inside their own network, signed agreements and named support people.
What this tier adds
Adds Continuous Learning, dedicated inference and custom SLAs, on-prem and VPC deployment, SSO/SAML, expanded indexed traffic and retention, plus a dedicated FDE, CSM and Slack channel.
Where the pricing makes sense
The company stage and team size where Maitai's pricing actually pencils out — and where peers do it cheaper.
Starter at $50/month flat plus usage fits a single technical operator proving the loop on one workload. Professional at $200/month flat per team plus usage, with 5 active users, 1M indexed requests per month and 90-day retention, is where most production teams land. Enterprise is contact-sales with custom volume pricing, dedicated inference and continuous learning. Compared with observability-only tools, you are paying for the fine-tuning and release half as well.
Setup time & first value
How long it actually takes to get something useful out of Maitai — broken out by persona, not the marketing-page minute.
Starter, single operator: drop in the SDK, confirm traces are landing in the Portal, and you have indexed production traffic typically the same day. Professional, team of up to 5: plan on a few days to wire the SDK into every service, agree on what counts as a good outcome, and build the first test set. Enterprise with VPC or on-premise deployment: weeks, because infrastructure, SSO/SAML and DPA
Switching to or from Maitai
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From manual log review: drop in the Maitai SDK, let the Portal index requests and agent actions, and turn recurring problem traces into a named test set.
- →From an observability-only tool: keep the traces you already collect as evidence, then add curate, evaluate and promote steps so the data drives a release rather than a dashboard.
- →From a separate fine-tuning pipeline: point automated dataset curation at the same production traffic so training data and monitoring come from one source.
- →From Excel or spreadsheet-based classification QA: move tariff examples into datasets and test sets so accuracy is measured on every candidate model.
- ↗To an observability-only tool: export your indexed traces if all you need is dashboards, and accept losing the curate-evaluate-promote loop.
- ↗To separate point tools: split evals, fine-tuning and release management across vendors, at the cost of rebuilding the lineage between them.
- ↗To a model provider's native tooling: keep your own datasets and test sets, since those are the artifacts that do not travel with the model.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Maitai”, and we withheld 6: 6 could not be judged, because “Maitai” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Maitai.
Official links
Tools that pair well with Maitai
Common stack mates teams adopt alongside Maitai, with the specific reason each pairing earns its keep.
Voiceflow
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Ada
Ada builds AI customer service agents that resolve, act, and improve across voice, chat, email, and social.
Featured Head-to-Head Comparisons
Maitai vs Presto Voice
Maitai and Presto Voice serve completely different domains: Maitai is a self-improving enterprise AI for knowledge management and support, while Presto Voice is a specialized voice AI for QSR drive-thrus. For internal helpdesk or knowledge base automation, choose Maitai. For boosting drive-thru revenue and order accuracy, choose Presto Voice.
Maitai vs B Rokratt
Choose Maitai if you need a customizable, self-improving AI agent to automate enterprise support or internal knowledge retrieval, and have budget for a paid platform. Choose Bürokratt if you are an Estonian citizen or e-resident who needs free, secure access to government e-services via natural language. They serve entirely different audiences and use cases.
Maitai vs Truleo
These tools serve entirely different markets — Truleo is purpose-built for law enforcement, connecting RMS, CAD, jail calls, and body cameras to auto-generate case leads and reduce report writing time by over 80%. Maitai is a self-improving enterprise AI for customer support and internal knowledge, integrating with Slack, Zendesk, and other business tools. Choose based on your domain: if you're in law enforcement, Truleo is the only fit; if you need an AI that adapts to your business knowledge, go with Maitai.
Alternatives to Maitai
View allVoiceflow
Visual platform for building, testing, deploying, and monitoring production AI chat and voice agents across web, SMS, and telephony.
CustomGPT.ai
No-code custom AI agents trained on your content, with every answer cited back to its source.
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