Maxim AI

Editor’s note: This Maxim AI review is based on my hands-on test of Maxim AI‘s evaluation platform on the free Developer plan and a Professional trial in October 2026. I weighed simulation depth, evaluator coverage, production tracing, pricing and retention.

Quick verdict: I recommend Maxim AI for teams shipping multi-turn chat or voice agents who want simulation, evals and tracing in one workspace. My main reservations are per-seat pricing of $29 or $49 a month and log retention of just 7 or 30 days on those plans.

Maxim’s strongest feature is simulated users, driven by scenario and persona datasets, that hold whole conversations with any HTTP endpoint instead of scoring isolated prompt responses. In this review, I’ll take a closer look at Maxim AI’s pricing, simulation, evaluators and tracing, so you can see exactly whether it fits your agent stack.

The getmaxim.ai homepage now leads with Bifrost, Maxim’s open-source LLM gateway; the evaluation platform covered here sits under Maxim’s evals product.

Key Takeaways

• Developer is free: 3 seats, 10k logs per month and 3-day retention.
• Professional costs $29 per seat: Simulation runs and online evals start here.
• Text and voice simulation: Voice agents connect through Twilio or Vapi.
• Retention is restrictive: Professional keeps 7 days, Business 30; both charge $1 per 10k extra logs.
• Enterprise-only deployment controls: SAML SSO and in-VPC deployment require a custom contract.

Pros and Cons

How Much Does Maxim AI Cost?

Maxim AI has 4 plans: Developer is free, Professional and Business charge per seat with monthly billing, and Enterprise uses custom annual billing. I calculate seat fees separately from logs because log allowances are per plan, not multiplied by the number of users.

• Developer (Free): For individual developers and small teams connecting their first application. It includes up to 3 seats, 1 workspace, 10k logs per month and 3-day retention. The dataset allowance is 3 datasets with 100 entries each, with email support. No overages are allowed, so logging stops at the cap.
• Professional ($29 per seat/month): My starting point for small agent teams running simulations. It supports unlimited seats, 3 workspaces, 100k monthly logs and 7-day retention. Simulation runs and online evals are included, alongside 10 datasets with 1,000 entries each, email support and a 14-day free trial.
• Business ($49 per seat/month): For teams needing 30-day retention and more operational controls. It includes unlimited workspaces, 500k monthly logs and 30 datasets with 10,000 entries each. RBAC custom roles, PII management, scheduled runs and custom dashboards are included, with private Slack support and a 14-day trial.
• Enterprise (custom, billed annually): For SAML SSO, in-VPC deployment and custom log limits or retention. Its compliance offering covers SOC 2 Type II, ISO 27001, HIPAA and GDPR, with custom BAAs. A dedicated customer success manager handles the account.

Plan	Price	Logs/month	Retention	Workspaces	Key unlocks
Developer	Free	10k, hard cap	3 days	1	3 seats, 3 datasets of 100 entries
Professional	$29/seat/month	100k	7 days	3	Simulation runs, online evals
Business	$49/seat/month	500k	30 days	Unlimited	Custom roles, PII management, scheduled runs
Enterprise	Custom, annual billing	Custom	Custom	Custom agreement	SAML SSO, in-VPC deployment

Professional and Business charge $1 per 10k extra logs. Buying more seats does not extend retention, so I treat the storage window as a separate buying constraint.

Is Maxim AI Good Value for Money?

• 5 Professional seats cost $145/month: My arithmetic puts a small team below Braintrust Pro’s $249 monthly base, which includes unlimited users.
• 5 Business seats cost $245/month: That is $4 under Braintrust Pro’s base, and 6 Business seats ($294) already cost more than Braintrust’s unlimited-user plan.
• 1M Business logs bring that total to $295: The additional 500k logs cost $50 above the 5-seat subscription.
• Langfuse and LangSmith price entry differently: Langfuse Core costs $29 a month flat, while LangSmith Plus charges $39 per seat, $10 more than Professional.
• External human raters need no paid seat: Emailed rating links keep occasional subject-matter reviewers off the subscription.

Reviewer’s Notes: I recommend Developer for wiring tracing, then Professional for small agent teams needing simulations. I recommend Business only when 30-day retention, RBAC or scheduled runs justify the higher seat price. For large PM and QA teams, compare total headcount costs against Braintrust before committing.

My Experience With Maxim AI

I connected my agent through an HTTP endpoint before adding production tracing. No SDK instrumentation was needed for endpoint testing, so the evaluation setup stayed separate from the application logging work.

Signing Up and Adding a Model Key

I signed in at app.getmaxim.ai with Google, which works on every plan, and opened a workspace. Under Settings > Models, I added a provider key before configuring the evaluation workflow.

Maxim requires at least one provider with GPT-3.5 and GPT-4 class access, so the free Developer plan still needs your own model key. That provider key is separate from the Maxim API key the tracing SDK uses later.

Connecting My Agent Through an HTTP Endpoint

I opened Agents > HTTP Endpoints > Create Endpoint and configured the request:

1. URL and headers: I entered the endpoint address and request headers.
2. Request body: I used {{input}} to insert the dataset input into the request.
3. Output Mapping: After clicking Run, I selected the response field to evaluate, using the data.response mapping pattern.

Any dataset column can be passed as a {{column}} variable in the body or headers, and Maxim supports pre-request and post-request scripts on the endpoint.

I recommend this route when the agent already exposes an API. The Output Mapping keeps evaluation focused on the answer field rather than the entire response envelope.

Building a Dataset and Running My First Test

I opened Library > Datasets and created a dataset with input and expected_output columns. CSV upload and synthetic generation are available; generating expected outputs requires context documents.

I added store evaluators through Library > Evaluators, then opened Test on the endpoint. After selecting the dataset and evaluators, I clicked Trigger Test Run.

The Runs tab shows scores per evaluator and lets me drill into each entry’s reasoning. For multi-turn testing, I created an agent dataset with Scenario, Persona and Expected steps columns, then selected Simulated session instead of the single-turn route.

I kept the scenario dataset separate from the input/output dataset because Maxim uses those columns to direct simulated users rather than replaying individual questions.

Instrumenting Production Tracing

I created a Log Repository and generated a credential under Settings > API Keys. I installed the Python SDK for tracing; the package commands for Python and JavaScript are:

pip install maxim-py
npm install @maximai/maxim-js

I initialized Maxim with the API key and obtained a logger using the repository ID. One-line framework integrations include:

• LangChain and LangGraph
• OpenAI Agents SDK and CrewAI
• LiveKit for voice applications

An existing OpenTelemetry exporter can use the OTLP endpoint instead of a Maxim-specific integration.

Reviewer’s Notes: I recommend the HTTP endpoint route as the quickest starting point for an API-backed agent. My reservation about staying on Developer is its 3-day retention: move useful examples into datasets promptly rather than relying on the log history.

Agent Simulation: Testing Multi-Turn Chat and Voice Agents

An agent can pass every single-turn test and still fail the conversation, which is the gap Maxim’s simulation targets. Its session-level evaluators score the whole interaction, while scenario and persona fields drive the simulated user’s behavior.

Text Simulation

I used Simulated session mode on the HTTP endpoint with an agent dataset. Maxim’s simulation controls cover:

• Scenario: The task the simulated user needs to complete.
• Persona: The user’s characteristics and conversational behavior.
• Expected steps: The actions the agent should take during the interaction.
• Maximum turns: An optional limit; without it, the simulation ends when the scenario completes.
• Reference tools and context: Additional material for directing the simulation.

Synthetic generation can create these datasets from documents, with context required for generating expected steps. A Custom Simulation prompt gives full control over the simulated user, including variables, partials and end conditions.

For a refund-handling agent, the scenario can require purchase verification and a policy-guided resolution within 5 turns. Per-session transcripts let the evaluator examine the sequence, rather than treating the final answer as the entire test.

Voice Simulation

Maxim connects voice agents through Twilio or Vapi, configured under Settings with an account SID and auth key. A voice agent uses its phone number, and a scenario dataset supplies the simulated calls.

The simulated caller speaks through OpenAI Realtime or ElevenLabs. Voice runs capture:

• Transcripts and audio recordings for reviewing the conversation.
• Average response latency and talk ratio for interaction timing.
• Average pitch and words per minute for speech characteristics.

However, voice runs accept built-in evaluators only, such as WER, interruptions and abrupt disconnection, and custom evaluators are not yet supported. Voice agents also cannot use single-turn testing.

Coval is the voice-first alternative, with Enterprise starting at $4,500 monthly. I favor Maxim when support agents, booking flows or multi-step tool use need conversational testing alongside broader prompt evaluation.

Evaluators, Datasets and Human Review

A low automated score can go straight to an external rater without another paid seat, which is the part of Maxim’s review workflow I value most. The four custom evaluator types also leave room for checks beyond an LLM judge.

The Evaluator Store

I selected store evaluators through Library > Evaluators, which also lists evaluators from OpenAI, Ragas and Google Vertex AI:

• Agent behavior: Task success, agent trajectory and tool selection.
• Retrieval quality: Faithfulness, context precision and context recall.
• Safety and correctness: Toxicity, PII detection and SQL correctness.
• Statistical comparison: BLEU, ROUGE, F1 and semantic similarity.

Custom AI evaluators accept binary, 1-to-5 scale, string or number outputs. Their {{input}}, {{output}} and {{context}} variables map to dataset columns or trace fields.

API-based evaluators call an external evaluation service, while programmatic evaluators run code-based checks. Human evaluators add explicit instructions and pass criteria for raters.

Programmatic validators cover valid JSON, URL, email, UUID and date formats. I paired task success with a valid-JSON check, so a fluent answer cannot pass while breaking the required structure.

Human Annotation

Human evaluators can use Annotate on report or Send via email. The emailed dashboard does not require a paid Maxim seat, which makes it my preferred route for occasional subject-matter review.

Sampling can select a percentage of entries or apply custom logic, such as sending only low automated scores to a person. Raters can submit rewritten outputs that feed back into datasets.

Those corrections count against dataset caps: Developer allows 100 entries per dataset, Professional 1,000 and Business 10,000. Size the regression set before choosing the plan, not after collecting corrected examples.

CI/CD

The maximhq/actions GitHub Action runs prompt tests with:

• Secret: MAXIM_API_KEY
• Variables: WORKSPACE_ID, DATASET_ID and PROMPT_VERSION_ID
• Evaluator selection: A comma-separated evaluator list

I recommend Promptfoo or Confident AI’s DeepEval instead when code-first evaluation is the primary requirement and Maxim’s shared review interface adds little value.

Tracing, Online Evals and Alerts in Production

Maxim’s online evaluation reaches sessions, traces and individual spans, but Professional retains the underlying logs for only 7 days. Retention, not evaluation granularity, is the main constraint for production investigations.

A Log Repository groups application activity into:

• Sessions and traces for conversations and individual requests.
• Spans and generations for intermediate work and model calls.
• Retrievals and tool calls for agent dependencies.
• Events, errors and user feedback for operational and quality signals.

Repository Overview displays total traces alongside latency, error rate and evaluation pass rate. The Logs tab adds request-level details, including tokens and cost.

OTLP ingestion uses https://api.getmaxim.ai/v1/otel with x-maxim-repo-id and x-maxim-api-key headers. Maxim accepts GenAI, OpenInference and AI SDK semantic conventions, and data connectors can forward information to other OpenTelemetry backends.

Online Evals

I favor session-level scoring for conversational outcomes and trace-level scoring for individual turns. Session and trace evaluators are configured in the UI with filters and sampling, while span or node evaluators attach through the SDK.

Retroactive evaluations arrived in October 2025, so stored logs can be scored after ingestion. Evaluated logs can also become dataset entries, connecting production failures to later regression tests.

Alerts

I configured an alert through Logs > repository > Alerts > Create alert after adding a notification integration. Log-metric alerts support:

• Latency
• Token usage
• Cost

An alert combines a threshold with minimum occurrences and a time window, such as latency above 1,000 ms on 5 occasions within 5 minutes. Quality alerts use evaluator scores instead; Slack and PagerDuty are notification options.

Reviewer’s Notes: I recommend curating useful Professional traces into a dataset within the week, before the 7-day log window closes. Langfuse Hobby keeps 30 days free, while Braintrust Starter keeps 14 days. I prefer those options when retaining historical traces matters more than Maxim’s integrated simulation workflow.

Prompt Management and the Bifrost Gateway

Maxim’s prompt deployment ships prompt versions to application code, while Bifrost routes model requests. The two carry separate pricing, even though Bifrost can forward traces into Maxim.

Prompt Playground and Deployment

Maxim’s playground supports reusable partials alongside versioned prompts. Its prompt tooling includes:

• Tool calls and MCP tools
• Retrieval context
• No-code agents built as prompt chains

A version deploys through rules such as Environment = prod, then application code retrieves it with maxim.getPrompt and QueryBuilder. I favor this approach when prompt changes need to ship independently of application releases.

When multiple deployments exist through the UI, only the latest is available for use, so check that behavior against your release process before relying on parallel deployments.

Bifrost

Bifrost is an Apache-2.0 Go gateway with about 9,150 GitHub stars; transport release v2.3.2 shipped on October 20, 2026. Its OpenAI-compatible API includes:

• Failover and load balancing
• Semantic caching
• Budgets and virtual keys
• MCP gateway capabilities

SSO, RBAC, guardrails and clustering belong to Bifrost Enterprise.

My main concern is management authentication being off by default. A critical unauthenticated remote-code-execution flaw tied to that default was disclosed in September 2026 and fixed in 2.1.0; upgrade and enable authentication before exposing the management port.

Maxim’s public platform release notes stop at December 2025, although the JavaScript SDK and Bifrost shipped releases in 2026.

How Does Maxim AI Compare to Competitors?

I pick Maxim when simulated multi-turn and voice conversations matter more than trace volume; my preferred Maxim AI alternatives change with headcount, retention and deployment requirements.

• Braintrust: My alternative for evals-first teams with many collaborators. Every tier includes unlimited users; Starter is free with 1 GB of processed data and 14-day retention, while Pro costs $249 monthly with 5 GB of processed data and 30-day retention. I favor its flat base price when Maxim’s seat fees become the larger expense.
• Langfuse: My pick for open-source deployment and longer free retention. Hobby includes 50k units and 30 days of data; Core costs $29 monthly and Pro $199. It is self-hostable, but lacks built-in agent simulation, so it does not replace Maxim’s conversational tests.
• LangSmith: I favor it for LangChain and LangGraph teams prioritizing their existing ecosystem. Developer includes 1 seat and 5k base traces free; Plus costs $39 per seat monthly. Its per-seat structure does not solve the headcount concern I have with Maxim.
• Arize AX and Phoenix: I recommend Phoenix for local open-source observability, or AX for a hosted option. AX Free includes 25k spans, while Pro costs $50 monthly for 50k spans and 10 GB. Enterprise adds SSO and online evals on traces.
• Confident AI: My alternative for teams centering evaluation on DeepEval’s code-first metrics. Paid plans have flat organization fees of $200 and $2,000 monthly, with unlimited seats on the $200 plan. Its free tier includes 2 seats and 1 GB-month of traces.
• Galileo: I shortlist it for Luna small-model evaluators that score agent traffic. Galileo is now sold by Splunk on annual billing, so buying runs through Splunk rather than a self-serve seat plan like Maxim’s.
• Coval: My voice-first alternative when call simulation dominates the workload. Its Simulate plan has a 7-day free trial, Enterprise starts at $4,500 monthly, and simulation metering uses minutes. I favor Maxim when the same team also needs text-agent datasets and prompt evaluation.

Tool	Free tier	Paid entry	Pricing model	Best for
Maxim AI	3 seats, 10k logs	$29/seat/month	Seats plus log overages	Multi-turn and voice agent simulation
Braintrust	Unlimited users, 1 GB processed data	$249/month	Flat base plus usage	Collaborative evals
Langfuse	50k units, 30-day retention	$29/month	Flat base plus units	Open-source observability
LangSmith	1 seat, 5k base traces	$39/seat/month	Seats plus trace usage	LangChain and LangGraph
Arize AX	25k spans, 1 GB	$50/month	Plan plus span volume	Hosted observability
Confident AI	2 seats, 1 GB-month of traces	$200/month	Organization fee plus usage	DeepEval workflows

How I Reviewed Maxim AI

I set up Developer and a Professional trial, connected an agent through an HTTP endpoint and built a scenario/persona dataset. I ran single-turn and simulated-session tests using store and custom evaluators.

I instrumented tracing with the Python SDK and configured an alert. I weighted conversation-level testing and the path from production logs back into regression datasets most heavily.

I compared Maxim’s seat fees and retention against Braintrust and Langfuse, then checked its fit alongside LangSmith, Arize and Confident AI. I also weighed evaluator coverage against voice-run restrictions, and separated the evals platform’s Enterprise deployment requirements from Bifrost’s open-source hosting option.

Prices current as of October 2026.

Should You Use Maxim AI to Test Your AI Agents?

Yes, I recommend Maxim AI for multi-turn chat and voice agents when simulation, evaluation and tracing need to share one workflow. Its scenario/persona datasets and external human-rating links suit teams where engineers work with PM or QA reviewers.

I recommend Professional for a small team that needs simulation runs and can work within 7-day retention. Business earns its higher price when 30-day history, custom roles or scheduled runs are requirements, not optional extras.

Skip Maxim for large paid-seat rosters without a clear simulation need: Braintrust’s $249 base includes unlimited users. For longer free retention or self-hosting without an Enterprise contract, I prefer Langfuse; for tracing alone, Maxim’s simulation strengths do not justify choosing it.

Start with the free Developer plan, connect one HTTP endpoint and build a dataset. Then use the 14-day Professional trial to run the multi-turn scenarios that matter to your agent before committing to paid seats.

FAQs

Is Maxim AI free?

Yes, Maxim AI’s Developer plan is free for up to 3 seats and 1 workspace. It includes 10k logs monthly and 3 datasets with up to 100 entries each.

I recommend it for initial evaluation setup, but retention is only 3 days. No log overages are allowed, so logging stops when the monthly allowance is exhausted rather than generating an extra charge.

How much does Maxim AI cost per month?

Maxim AI costs $29 per seat monthly on Professional or $49 on Business, both billed monthly. My 5-seat calculations are $145 and $245 respectively, before overages.

Professional and Business charge $1 per 10k extra logs beyond their included allowances. Enterprise has custom annual billing. See How Much Does Maxim AI Cost? above for the plan limits and retention comparison.

What is Maxim AI used for?

Maxim AI is used to simulate, evaluate and observe AI agents across development and production. I recommend it primarily for conversational testing, where scenario-driven simulated users exercise more than an isolated prompt.

Text simulation targets agent endpoints, while voice simulation connects through Twilio or Vapi. Evaluators score behavior, and production traces can feed examples back into regression datasets.

Can I self-host Maxim AI?

Yes, Maxim AI offers in-VPC deployment on Enterprise, but its evaluation platform is not a free open-source self-hosting package. Zero Touch places both control and data planes in the customer’s VPC; Data Plane mode retains a hosted control plane.

I distinguish those paid deployments from Bifrost, Maxim’s open-source LLM gateway, which is free to self-host and has separate Enterprise options.