Confident AI vs Maxim AI (2026)

Native integrations post notifications to Slack and Discord through OAuth, to Microsoft Teams by webhook, and to PagerDuty and email, and let a person open Linear or GitHub Issues tickets from a trace. AI Connections call the customer's own AI endpoint to run evaluations, and connected MCP servers supply tool definitions so tool calls can be scored. These bring data in and send events out for the platform, and none connects an agent to a real system to read, write or take actions there.

SourceConfident AI, confident-ai.com/docs project integrations, ai-connections and mcp-serversread 2026-09-21

No code agents call third party services through API nodes at any point in a chain and deploy without code changes. One worked example is an agent that categorizes support emails, creates help desk tickets and sends replies. Prompts can use tool calls and connected MCP servers in the playground, and alerts go to Slack and PagerDuty.

SourceMaxim, getmaxim.ai/docs llms.txt (API nodes, agent deployment, customer support email agent guide, prompt tool calls, MCP)read 2026-09-21

Trace Workflows shows the pipeline that runs after a trace arrives (dataset ingestion, queue ingestion, evaluation rules and classifiers) as a graph, and the tracing SDKs record agent spans from the customer's own orchestration. There are no workflows that sequence, branch or retry steps, or combine deterministic nodes with agent steps.

SourceConfident AI, confident-ai.com/docs llm-tracing workflows and span-typesread 2026-09-21

In the no code agent builder, blocks connect in a visual editor, including API nodes that call third party services and loops that repeat steps. Errors show at every node, and agents deploy without code changes for querying through the SDK. API steps that run the same way every time can sit next to prompt steps. Maxim does not describe branching, retries or fallback paths inside a workflow.

SourceMaxim, getmaxim.ai/docs llms.txt (no-code builder nodes, loops, API nodes, deployment, error debugging)read 2026-09-21

Online evaluations run server-side on each trace and span as it is ingested, evaluation rules and ingestion tasks act on incoming data automatically, threshold alerts run on a recurring schedule and notify Slack, Discord, Microsoft Teams, PagerDuty or email, datasets can be evaluated on a schedule, and governance assessments run daily. All of it starts on an incoming event or a schedule, with no person asking.

SourceConfident AI, confident-ai.com/docs online-evals, workflows, alerts and ai-governance, and confident-ai.com/pricingread 2026-09-21

Online evaluations score production logs automatically, test runs go out on a schedule and from CI/CD pipelines, and alerts on performance and quality thresholds go to Slack and PagerDuty. Work starts on a schedule, an incoming log or a threshold without a person asking.

SourceMaxim, getmaxim.ai/docs llms.txt (online evals, scheduled runs, alerts and notifications, Slack and PagerDuty integrations)read 2026-09-21

Retrieval metrics such as contextual precision, contextual recall and faithfulness score the customer's own retrieval pipeline, retriever spans record the chunks that pipeline returned, and data source connectors feed documents into synthetic dataset generation. There is no index, retrieval layer or knowledge API that grounds an agent's behavior in company data.

SourceConfident AI, confident-ai.com/docs metrics, span-types and data-source-connectorsread 2026-09-21

Retrieval quality is tested, not supplied. The customer connects its own RAG pipeline to prompt tests, and test runs evaluate retrieval. There is no document ingestion, index, retrieval layer or knowledge API that grounds an agent in company data.

SourceMaxim, getmaxim.ai/docs llms.txt (prompt retrieval testing, evaluate retrieval quality)read 2026-09-21

Traces, threads and versioned datasets persist as observability and test records of what the customer's app did, and multi-turn metrics check whether that app retained knowledge across a conversation. There is no session, conversation, workflow or long term memory that an agent reads and writes.

SourceConfident AI, confident-ai.com/docs threads, dataset management and multi-turn metricsread 2026-09-21

Logs, sessions, datasets and versioned prompts persist as records of the customer's application. There is no session, workflow or long term memory that an agent reads and writes.

SourceMaxim, getmaxim.ai/docs llms.txtread 2026-09-21

AI Governance, an Enterprise feature, groups controls into policies that are assessed daily and can block a release in CI/CD unless every control above Low importance passes, and each control resolves to a status that shows why it failed.

Annotation queues, fed automatically by queue ingestion tasks, custom annotation forms and end user feedback put people in the review loop on traces and threads. That gives a policy checkpoint on releases and a place for human review.

There is no approval step or guardrail that holds or blocks an agent's own action in production, and no control to pause and resume a run.

SourceConfident AI, confident-ai.com/docs ai-governance, human-in-the-loop and threat-detectionread 2026-09-21

Human raters review test runs and production logs through human annotation, alongside automated evaluators, and datasets are curated from those annotations. This reviews the agent's outputs. There is no approval step, runtime guardrail or pause and resume control over an agent's actions.

SourceMaxim, getmaxim.ai/docs llms.txt (human annotation, human annotation on logs, curate datasets)read 2026-09-21

Self serve SAML SSO works with Okta, Azure AD and Google Workspace. Custom roles, policies and permissions apply at organization and project level, with all data separated by project, project and organization audit logs record every API and user action by user or API key, retention periods are configurable, and data is encrypted at rest and in transit. Confident AI states SOC 2 Type II compliance and HIPAA compliance with BAAs available, and says customer data is never used for training.

SourceConfident AI, confident-ai.com/docs sso, rbac, audit-logs and data-handling, and confident-ai.com homepageread 2026-09-21

Access control runs through organization and workspace roles for team members, workspace level RBAC, and SAML 2.0 single sign on with Okta and Google Workspace, each with its own setup guide, plus two factor authentication and a vault for secrets. Maxim Enterprise adds audit logs, and the platform can be self hosted. There is a trust center at trust.getmaxim.ai, but Maxim's public pages name no SOC 2, ISO 27001 or other attestation.

SourceMaxim, getmaxim.ai/docs llms.txt (members and roles, workspace RBAC, SAML SSO, two factor authentication, vault, audit logs)read 2026-10-01

Tracing records traces, spans and threads, with typed spans for LLM calls, retrievers (the chunks returned), tools (which tool ran, with what arguments and what it returned) and agent runs that roll the nested steps up, plus logged prompts, token usage and cost. Project and organization audit logs record every API and user action separately from traces, traces export as CSV on demand or on a schedule to S3, trace forwarding sends every ingested trace to the customer's own OpenTelemetry collector, and retention periods are set per project or inherited from the organization.

SourceConfident AI, confident-ai.com/docs span-types, exports, trace-forwarding, audit-logs and data-retentionread 2026-09-21

Production traffic is logged into repositories with distributed tracing of sessions, traces and spans. The platform also ingests OpenTelemetry traces over OTLP, tracks live issues, runs automated quality checks on production logs, and exports logs with their evaluation data to CSV with filters.

SourceMaxim, getmaxim.ai/docs overview and llms.txt (tracing, OTLP ingestion, exports)read 2026-09-21

The managed cloud stores and processes data in the United States by default or in the European Union when chosen at signup, regions are listed in the US, EU, UK, Japan, Canada and Australia, and further residency is available on request. On Enterprise, self hosting runs the whole platform inside the customer's own AWS, GCP or Azure account from a published Terraform module and Helm chart in an existing VPC or VNet, with guides for scaling, disaster recovery and air gapped networks.

SourceConfident AI, confident-ai.com/docs self-hosting and data-residency, and confident-ai.com homepageread 2026-09-21

Deployment is either a managed service or self hosted, with full VPC isolation (Zero Touch) or a hybrid setup that places the data plane in the customer's own cloud.

SourceMaxim, getmaxim.ai/docs llms.txt (self-hosting overview, data plane deployment)read 2026-09-21

Four official agent skills (confident-client, deepeval, confident-tracing and confident-otel) teach a coding agent to build eval suites, instrument an app and administer the account, and ship as plugins ready to install for Claude Code, Cursor and Codex. Red teaming assessments come preset to OWASP Top 10 for LLMs and NIST AI RMF. These are packaged, editable starting points, and there are no ready made agents, packaged employees or deployable workflows.

SourceConfident AI, confident-ai.com/docs coding-agents skills and plugins, and confident-ai.com/llms.txtread 2026-09-21

The ready made material is for evaluation. It includes an evaluator store of off the shelf evaluators, test presets, and guides that walk through building agents such as a customer support email agent. There are no ready made agents, templates or packaged workflows a customer adopts for their own work.

SourceMaxim, getmaxim.ai/docs llms.txt (evaluator store, presets, guides)read 2026-09-21

Each project chooses the provider and model behind its LLM as a judge metrics, using the customer's own keys for OpenAI, Anthropic, Gemini, xAI, DeepSeek, Mistral or Perplexity, Amazon Bedrock or Vertex AI, or the Portkey and LiteLLM gateways. Keys are configured once at organization level and inherited by projects that opt in, or set per project, and without customer keys Confident AI's managed defaults apply. Model choice stays with the customer's admins, on their own providers and keys.

SourceConfident AI, confident-ai.com/docs evaluation-models and organization model-credentialsread 2026-09-21

Teams set up their own model providers before running prompts, agents and evaluators, and the playground compares output quality, cost and latency across combinations of prompts, models and parameters, with custom token pricing for accurate cost reporting.

SourceMaxim, getmaxim.ai/docs llms.txt (running your first eval, custom pricing) and overviewread 2026-09-21

A REST API covers datasets, test runs, traces, prompts, annotations, dashboards, governance policies and organization administration, with project and organization API keys that can be rotated and revoked. DeepEval is the open source Python SDK and confident-trace provides Python and TypeScript tracing SDKs, with OpenTelemetry ingestion from any language. Confident AI serves its own MCP server with OAuth dynamic registration for coding agents, publishes official agent skills and plugins, and fits CI through pytest and pull and merge request eval gates for GitHub and GitLab.

SourceConfident AI, confident-ai.com/docs api-reference, coding-agents/mcp, integrations and pr-eval-gate, and confident-ai.com/llms.txtread 2026-09-21

There are Python and TypeScript SDKs and public REST APIs, and the OpenAPI description covers prompts, prompt tools, evaluators, workflows, datasets, alerts and integrations. Evaluations run in CI/CD through GitHub Actions, and MCP servers connect to prompts in the playground.

SourceMaxim, getmaxim.ai/docs llms.txt (SDKs, public APIs, CI/CD, MCP)read 2026-09-21

DeepEval and Confident AI test AI apps against versioned datasets of goldens with single turn and multi turn metrics, custom G-Eval and code based metrics, and simulated multi turn conversations. Evals run as pytest unit tests in CI/CD with pass or fail thresholds, and the PR Eval Gate runs the app over a pinned dataset on every pull request and posts a passing or failing GitHub check. Online evaluations score production traces as they arrive, metric scores can be aligned against human labels, and production traces are curated back into datasets.

SourceConfident AI, confident-ai.com/docs llm-evaluation, unit-testing-cicd, pr-eval-gate and online-evalsread 2026-09-21

Offline evaluations run prompts, no code agents and HTTP endpoint agents against datasets, using an evaluator store and custom AI, programmatic, statistical and human evaluators. The platform also simulates multi turn conversations, compares versions in run reports, schedules test runs, runs evaluations in CI/CD through GitHub Actions, and evaluates production logs online.

SourceMaxim, getmaxim.ai/docs llms.txt (offline evals, simulation, scheduled runs, CI/CD, online evals) and overviewread 2026-09-21

The product covers evaluation, tracing, human review, red teaming, governance, settings and self hosting, and does not control a browser, desktop or computer.

SourceConfident AI, confident-ai.com/docs and llms-full.txtread 2026-09-21

Maxim covers evaluation, simulation, observability and a no code agent builder. There is no browser, desktop or computer control by an agent.

SourceMaxim, getmaxim.ai/docs llms.txtread 2026-09-21