INSTRUMENT VERSUS INTERCEPT

Token Observe vs Arize

Arize is instrumented into your application and reads what it did. Token Observe is a hop your agents call through and decides what they may do.

Arize is instrumented into your application and reads what it did; Token Observe is a hop your agents call through and decides what they may do — and for most teams shopping in this category Arize is the better first purchase. Their tracing documentation sets the shape out plainly: OpenInference instrumentation “wraps your function calls (automatically via integrations, or manually) and captures span data”, spans are exported “to Arize AX using OTLP (gRPC by default)”, and “The Arize collector ingests and visualizes them so you can explore, filter, and debug”. On top of that sit evaluations that run “continuously against live production traces, or on demand against a dataset or experiment” with evaluators kept in an Eval Hub, session-level evaluations measuring “coherence, context retention, and overall goal achievement”, experiments, monitors with automatic or static thresholds that notify Email, Slack, PagerDuty, OpsGenie or an HTTP webhook, and cost computed for every span from token counts against a cost configuration you define, matched per token type and “calculated per million tokens”. Arize also publishes guardrails, and that row is where a careless comparison goes wrong: their guardrails documentation describes a Guard applied to “user input messages (e.g. jailbreak attempts) or LLM output messages (e.g. answer relevance)” whose failed messages trigger a block, a “reask”, or a “fix” returning “a user-defined hard-coded default LLM response”. The difference is not that Arize cannot stop anything; it is that a Guard is an object your application instantiates and calls with the message it already holds, while Token Observe’s verdict is taken by the component that holds the provider credential, at step 6 of eleven ordered steps, before the payload leaves your network. If your question is why did quality drop, which prompt version regressed, or what is this agent’s trace tree doing, Arize answers it and Token Observe does not and is not going to.
How they attach
OpenInference spans over OTLP, gRPC by default, to their collector
How Token Observe attaches
One base URL and one key; the credential moves to the hop
Their metered unit
Spans and ingested GB — 25k and 1 GB a month on the free tier
The part to compare
Payload-bound approvals, pre-egress redaction, a hash-chained record
Not an evaluation platformNo datasets, no experiments, no judge harness, and none planned
On this page
where they win

For the work an AI engineering team does every day, Arize is the right purchase, and it is not close

The loop that improves an agent is offline and iterative, and Arize has built a platform around exactly that loop while Token Observe has deliberately built none of it. Their material describes evals that “run in two modes: continuously against live production traces, or on demand against a dataset or experiment”, evaluators stored in an Eval Hub and reused across projects, session-level evaluations that judge a whole conversation for “coherence, context retention, and overall goal achievement”, human annotations that put “a human label on a trace, span, session, dataset example, or experiment result”, experiments to “validate changes”, an Agent-as-a-Judge that “continuously learns from agent telemetry”, and Signal, which “surfaces what matters, uncovers root causes, and generates review-ready fixes”. Their tracing documentation counts more than thirty native integrations and classifies spans by kind — LLM, Tool, Agent, Retriever, Chain, Embedding, Guardrail, Reranker, Evaluator and Audio — which is the vocabulary you need to debug a multi-step agent and is a vocabulary Token Observe does not have. Semantic caching, evaluation harnesses and session replay were designed for in Token Observe’s data model and deliberately not implemented, and building another standalone tracing and evaluation product is a named strategic non-goal rather than a backlog item. If the gap you are trying to close is quality, Arize closes it and Token Observe does not.

Procurement is the second place they win, and for a regulated buyer it may settle the matter before the technology is discussed. Their compliance page lists SOC 2 Type II, PCI DSS 4.0, HIPAA compliance and CSA STAR Level 1; their trust centre adds ISO/IEC 27001 certification and GDPR, publishes a shared-responsibility model that puts SSO, application permissions and application data on the customer’s side of the line, and links EU data residency. Ask them which certification covers which scope and to which date, because that is the only version of the answer worth having and this page does not hold it for them. Token Observe holds none of it: no SOC 2, no ISO 27001, no ISO 42001, no independent penetration test, and a published licence its own repository describes as a template pending counsel. If your gate is an audit report, the comparison ends there in Arize’s favour.

The third advantage is architectural and it is a genuine argument against buying Token Observe. An instrumentation SDK is out of band: if the exporter fails, the agent keeps working and you lose visibility. Token Observe is in band and fails closed, so if its audit chain does not verify at boot, governed requests receive a 503 carrying ACP_AUDIT_UNAVAILABLE and your agents stop. That trade is deliberate — a control you can bypass by turning it off is not a control — but it means Arize can be adopted by one team with no operational conversation and Token Observe cannot. Arize also runs at a scale Token Observe does not: their home page cites “1 Trillion spans processed” and “1 Billion evals per year” across the platform, and their self-hosted deployment runs on Kubernetes across GCP, Azure, AWS, Oracle Cloud, OpenShift, K3s, Rancher and Tanzu. Token Observe is a single-writer SQLite process on one host at its current target scale. Everything said here about Arize comes from the pages listed in the sources, read on 2 September 2026, and none of it has been tested; where a row reads as an absence, put it to them in writing rather than treating it as a finding.

head to head

Token Observe and Arize, capability by capability

how to read this table

The Arize column paraphrases Arize AI’s own published material as it stood on 2 September 2026. None of it has been independently tested here, products in this category ship quickly, and a capability that is absent from a vendor’s documentation is not the same thing as a capability the product lacks. Check anything that decides it for you against their own current documentation.

Where it sits relative to the request

How it attaches
Arize

Their tracing documentation: instrumentation “wraps your function calls (automatically via integrations, or manually) and captures span data following OpenInference semantic conventions”, and spans are exported to Arize AX “using OTLP (gRPC by default)”.

Token Observe

One environment variable. OPENAI_BASE_URL or ANTHROPIC_BASE_URL points at Token Observe, which then holds the agent credential and the provider credential, so there is no library inside the agent to remove.

What the platform does with the call
Arize

“The Arize collector ingests and visualizes them so you can explore, filter, and debug.” Their home page frames the product in three parts — Observe, Evaluate, Learn — and describes tracing “from the team who founded OpenInference”.

Token Observe

Eleven ordered steps to one decision point: authenticate, resolve the agent, open the trace, sanitise Unicode, scan for sensitive data and injection, take the verdict at step 6, enact it, route, call upstream, govern any tool call the model proposes, then meter and record.

Span vocabulary
Arize

Each span carries a kind — LLM, Tool, Agent, Retriever, Chain, Embedding, Guardrail, Reranker, Evaluator or Audio — with attributes set by auto-instrumentation and extensible by you, across more than thirty native integrations.

Token Observe

One trace per governed request, holding events, normalised usage, policy decisions, redaction outcomes, approvals and tool calls. Application-internal steps that never present a Token Observe credential are not in it.

This is the row where the two products are least substitutable. Arize sees the shape of your chain because it is inside it; Token Observe sees the requests that crossed its boundary. Neither view contains the other.

OpenTelemetry direction
Arize

A destination. Instrumentation is built on OpenTelemetry with OpenInference semantic conventions, and spans are exported to their collector.

Token Observe

An input. An OTLP-over-HTTP receiver takes bounded JSON and protobuf for logs, traces and metrics, answers in the request encoding, and attributes every write to the credential that presented it — used as the observed view of a reconciliation, not as a competing backend.

Failure mode
Arize

Their documentation describes export and collection of spans out of the application. Ask them what an agent does when the exporter or the collector is unreachable; the pages listed in the sources do not state it.

Token Observe

Fail-closed, and deliberately. A boot-time walk of the audit chain that finds corruption latches readiness and audit writes unavailable, governed requests then receive a 503 carrying ACP_AUDIT_UNAVAILABLE, and there is no online clear — recovery means restoring a database whose chain and independently retained head verify.

What it enforces before the payload leaves

Guardrail model
Arize

Their guardrails documentation describes a Guard you instantiate “with their own prompts/datasets” or from pre-built options, applied to “user input messages (e.g. jailbreak attempts) or LLM output messages (e.g. answer relevance)”, and passed “user_message, retrieved context and llm_response” at run-time.

Token Observe

Detection is built into the hop and is heuristic: eleven sensitive-data classes with Luhn, IBAN mod-97 and NHS mod-11 checksums on three of them, and nine weighted injection patterns scored 1.25× higher when the text is a tool result rather than a prompt.

Both products can stop something. The question to put to Arize is where the Guard object runs in your stack and what an agent that does not call it is subject to — a library binds the calls that invoke it, a hop binds the calls that route through it.

What a violation produces
Arize

Failed messages trigger corrective actions: blocking outputs entirely, “reask” re-prompting the model for a new response, or “fix” returning “a user-defined hard-coded default LLM response”.

Token Observe

A typed refusal. The trace closes as blocked, the caller receives ACP_POLICY_BLOCKED, and nothing reached the provider. On a streamed response the plan is fixed before the first byte and a blocking class ends the stream with an in-band frame, because a status line is spent once written.

Detection quality
Arize

Their guardrails page publishes a benchmark, both halves of it: “True Positives: 86.43% of 656 jailbreak prompts failed” and “False Positives: 13.95% of 2000 regular prompts failed”, at “1.41 median latency for end-to-end LLM call on GPT-3.5”, with the dataset embeddings guard intercepting the call when “the cosine distance between the input message and any of the chunks is within the user-specified threshold (default setting is 0.2)”.

Token Observe

Published per kind rather than as an aggregate: 0.70 confidence on a phone number, 0.98 on a checksum-valid IBAN, 0.99 on a PEM private key. Free-text personal data is not detected at all, and the product’s own material calls this a compensating control rather than your only DLP.

Human in the loop
Arize

Human annotations put “a human label on a trace, span, session, dataset example, or experiment result — a category (Correct / Incorrect), a numeric score, or freeform text”, with an Annotator space role for the people who apply them. A human decision that holds a request before it executes is not described in the pages listed in the sources as of 2026-09-02.

Token Observe

A 403 carrying an approval id, bound to the SHA-256 of the canonicalised action plus the execution context it was proposed in, single-use by compare-and-set, expiring at 60 minutes by default and configurable from one minute to seven days.

Spend control
Arize

Cost is computed when a span is received, either from cost attributes the client already set or from a cost configuration looked up by matching llm.model_name and llm.provider, with each token type “matched against the configuration” and the cost “calculated per million tokens”. It is then used to “filter traces or spans where cost exceeds a defined threshold”, “create monitors for high-cost traces”, and build dashboards. The page warns “Cost is not retroactive. To track costs, you must configure pricing before ingesting traces.” It does not describe a budget that refuses a request.

Token Observe

The money verdict is taken after the route resolves and before egress: every provider and fallback the resolved route could execute is priced, the most expensive of those rates is reserved against the agent’s hour, day and month windows in one per-agent transaction, and a budgeted agent whose route has an unpriced reachable target is refused with a 409 rather than metered at zero.

Rate ceilings
Arize

Plan-level volumes meter ingestion — 25k spans and 1 GB a month on Free, 50k and 10 GB on Pro, custom on Enterprise. Their pricing documentation does not state what happens when a monthly span allowance is exceeded.

Token Observe

Requests, tool calls and tokens per minute per agent, checked before the route is resolved, under a kill switch scoped to one agent, one team or the whole estate that is evaluated first in the pipeline.

Permission default
Arize

Their roles govern people: space roles of Admin, Member, Read-only Member and Annotator, plus “fine-grained custom roles that can be assigned at the space or project level” and an is_developer flag controlling who may create User and Service keys.

Token Observe

Deny by default, for agents. An action no role names is refused, an explicit deny beats every allow wherever it is written, and a delegation chain intersects at every hop so agent A cannot escalate by asking agent B.

The same word means two things here. Arize’s roles decide who may read the observability data; Token Observe’s decide what an agent may do to your systems. A buyer who reads one as the other will buy the wrong thing.

What it records, and who may read it

The unit of record
Arize

A trace is “that entire journey as a tree of spans”, each span “one operation (an LLM call, a retrieval, a tool invocation) with its input, output, timing, and metadata”.

Token Observe

One trace per governed request, opened at step 3 before the verdict is taken, so a request refused at step 6 is still recorded and the caller still receives a trace id in x-acp-trace-id.

Search and aggregation
Arize

Filtering of traces and spans by attribute, monitors over span attributes or custom metrics, and dashboards “based on specific token types or cost groupings”, where a token type is one of prompt, completion, audio, image or reasoning.

Token Observe

A compliance officer’s question in English translated into a validated filter object over fourteen allow-listed fields, never into SQL, shown back as editable chips, with a deterministic keyword parser as the fallback. It cannot group, count or correlate across traces.

Aggregation is theirs, and the concession is unqualified. Token Observe refuses generated SQL because trace content is attacker-influenced by construction, and the published cost of that refusal is that “which agents used the same card number twice” is not a question you can ask.

Alerting
Arize

Monitors with “Automatic Thresholds” derived from historical data or “Static Thresholds” you set, entering a “Triggered” state and notifying Email, Slack, PagerDuty, OpsGenie or “HTTP webhooks: JSON POST to your systems on monitor status transitions”. Their monitors page does not describe a monitor blocking or restricting traffic.

Token Observe

Webhook events on governance transitions — an approval requested, a policy decision, a budget window breached, the kill switch engaged — emitted at step 11 alongside the ledger write, with no notification integrations of their kind.

Administrative audit
Arize

Their pricing page lists audit logs among the features of AX Enterprise. The SSO and RBAC documentation page read on 2026-09-02 does not describe them further, so ask them what is recorded, for how long, and whether it can be exported.

Token Observe

Every governance-plane change — an agent created, a policy widened, the kill switch engaged, an approval decided — appended to a hash chain whose entry digest covers the previous entry’s hash plus the canonical JSON of that entry’s own content.

Tamper evidence
Arize

Not described in the pages listed in the sources as of 2026-09-02. Their trust centre names auditability as one of three security pillars and publishes a shared-responsibility model rather than a per-record integrity mechanism.

Token Observe

SHA-256 by default, HMAC-SHA256 when an audit MAC key is held outside the database, the head sealed by a checkpoint MAC at every boot, and optional Ed25519 anchors published off-box on a schedule. Tamper-evident, not tamper-proof: unkeyed, an operator who rewrites a row and recomputes every hash after it verifies clean, and each verification result names which of the two you hold.

Evidence export
Arize

Their documentation describes exploring, filtering and debugging traces in the collector, and dashboards over them. A sealed bundle produced for an auditor is not described in the pages listed in the sources as of 2026-09-02.

Token Observe

A compliance export containing the period’s traces and events, the approvals with approver identity and rationale, the audit entries covering every governance-plane change, a chain verification result naming the sequence number of any break, and a SHA-256 digest of the bundle at a recorded time. Digest-sealed, not signed.

Who may read the record
Arize

Account, organisation and space roles, with public spaces visible to all members of the parent organisation and private spaces gated by space role, plus custom roles at space or project level.

Token Observe

Reads are themselves attributable. Trace list, search, detail and export reads are recorded; spend figures and recertification evidence are gated on the reader’s team scopes as well as their role; and organisation-wide views return 403 rather than a misleading partial answer without an explicit organisation-wide scope.

How it deploys

Deployment options
Arize

SaaS on all three plans, with “SaaS or Self-Hosted” listed under AX Enterprise. Self-hosted runs on “GCP, Azure, AWS, Oracle Cloud, OpenShift, K3s, Rancher, Tanzu, and other Kubernetes environments”, in connected, semi-restricted or air-gapped configurations, with reference Terraform templates for GCP, Azure and AWS.

Token Observe

Self-hosted only, and bring-your-own-key. One Node process, one SQLite file and five surfaces; there is no hosted plan to start on and no managed tier to graduate to.

Where the data sits
Arize

In self-hosted, “all observability data lives entirely within your own environment—your Kubernetes cluster and your own persistent/object storage under your security controls”, and “Arize does not store your data”. Their trust centre links EU data residency for the managed platform.

Token Observe

The vendor receives no product telemetry, phone-home data, prompts, keys or trace database in any configuration, because self-hosted is the only configuration. Governed payloads leave your network only for the model and tool providers you configure, after policy and redaction.

Feature parity across deployments
Arize

Self-hosted is described as “the same core platform as the SaaS offering—full agent and LLM observability, tracing, online and offline evaluations, prompt optimization, datasets/experiments, and the Alyx AI assistant”, with the difference “operational rather than functional”.

Token Observe

One build. Anchoring and the on-behalf-of intersection default to off because each needs a key or an identity provider you supply, not because they sit behind a tier.

Identity plumbing
Arize

“Single Sign-On via SAML2”, with SAML2 endpoints published for US and EU clients, and self-hosted supporting “any SAML 2.0 IdP”.

Token Observe

OIDC SSO plus bounded SCIM user provisioning — userName, name and active lifecycle for viewer accounts with no evidence scopes, permitting disable but not rename or reactivation once an account holds more authority. SAML, SCIM Groups and live-directory reads are not built.

Open-source path
Arize

Phoenix, “built by Arize AI and the open-source community”, built on OpenTelemetry and powered by OpenInference, started with uvx arize-phoenix serve and deployable “on Docker, Kubernetes, or your cloud of choice”. AX is described as “a managed enterprise platform built on the same open standards”. The Phoenix overview page read on 2026-09-02 does not state a licence; check the repository.

Token Observe

Source-available to the customer under a licence the repository itself describes as a template pending counsel. There is no separate community edition, and no feature is withheld from one.

Scale posture
Arize

Their home page cites a trillion spans and a billion evaluations a year across the platform, and self-hosting requires a Kubernetes cluster with persistent block storage and object storage.

Token Observe

A single-writer SQLite process on one host at the current target scale — roughly five to fifty agents owned by one platform team. No replica, no clustering and no vendor-operated uptime SLA, and because it fails closed its availability is a governance property of your environment.

What it costs, and what is certified

Published price
Arize

AX Free at $0, AX Pro at $50 a month, AX Enterprise at custom pricing. All three list unlimited users and unlimited evaluations.

Token Observe

No published price list. You pay your providers directly, because the deployment holds your keys and the vendor never sees the traffic.

What is metered
Arize

Trace spans and ingestion volume — 25k spans and 1 GB a month on Free, 50k and 10 GB on Pro, custom on Enterprise — plus Signal issues at 10, 25 and unlimited respectively.

Token Observe

Nothing is metered by the vendor. The only cost counter is your provider spend, priced per request before egress and written to a ledger in USD columns rounded to eight decimal places.

Span volume is the number to model before you sign anything, and Arize’s own material makes the point: a multi-step agent creates more spans as it adds model calls, tools, retrieval, retries and sub-agents. Estimate spans per agent-run and multiply.

Retention
Arize

15 days on Free, 30 days on Pro, custom on Enterprise.

Token Observe

Unset by default, and unset means keep forever. That is a default to change deliberately rather than a feature: evidence you cannot lawfully still hold is a liability, not an asset.

What sits behind the enterprise line
Arize

Multiple organisations and spaces, enterprise SSO, audit logs, HIPAA and GDPR compliance, dedicated support with SLAs, and self-hosted deployment are all listed under AX Enterprise.

Token Observe

Permissions, policy, approvals, budgets, the flight recorder, the audit chain and anchoring are one product with no feature tier and no licence key gating any of them.

Support
Arize

Community troubleshooting on Free, email support with standard SLAs on Pro, and dedicated support with custom SLAs on Enterprise.

Token Observe

No vendor-operated uptime commitment, and the reason is published: the vendor does not operate your deployment and has no telemetry from it, so an availability number from that party would be unmeasurable by either side.

Published certifications
Arize

Their compliance page lists SOC 2 Type II, PCI DSS 4.0, HIPAA compliance and CSA STAR Level 1; their trust centre lists SOC 2, PCI DSS, ISO/IEC 27001, GDPR and HIPAA.

Token Observe

None published: no SOC 2, no ISO 27001, no ISO 42001 and no independent penetration test. A pre-purchase test is welcome and expressly permitted by the licence, with no gag clause and no pre-approval of results.

A library inside the process, and a hop in front of it

Arize attaches by instrumentation and Token Observe attaches by address, and almost every other difference on this page follows from that one. Their tracing documentation describes three steps — instrumentation that “wraps your function calls (automatically via integrations, or manually)”, export of spans “to Arize AX using OTLP (gRPC by default)”, and a collector that “ingests and visualizes them so you can explore, filter, and debug”. The strength of that arrangement is resolution: because the instrumentation is inside the process, it sees the retriever, the reranker, the sub-agent and the retry, and their span kinds name each of them. Token Observe sees only what crossed its boundary, which is coarser by construction.

The weakness of that arrangement, for the specific job of enforcement, is also structural. Code inside the process binds the calls that invoke it. An agent that takes a path the instrumentation does not wrap, or that calls a provider directly with a key it was given, is outside the picture until something else notices — which is why Token Observe’s answer to unmanaged activity is a separate radar reconciling five evidence sources rather than a claim that the gateway sees everything. A hop that holds the provider credential binds a different set: everything that needs the key, because the key is not anywhere else. That is why the integration is a base URL change rather than an SDK install, and it is also why the integration is heavier to reverse.

Which of those you want is a question about what your agents end in. If every call ends in text a human reads before anything happens — drafting, summarising, chat, code suggestions someone reviews — resolution is worth more than interception, and Arize is the proportionate purchase. If a call can end in a refund issued, a pull request merged, an email sent or a row written, the interesting question stops being what happened and becomes whether the thing that was authorised is the thing that happened, and that question is answered by the component holding the credential or it is not answered at all.

Arize publishes guardrails, so the honest distinction is narrower than it looks

The lazy version of this page would say Arize observes and Token Observe enforces, and their guardrails documentation makes that false. It describes guardrails that “correct undesirable outputs at run-time”, applied to “user input messages (e.g. jailbreak attempts) or LLM output messages (e.g. answer relevance)”, with failed messages triggering corrective actions: blocking outputs entirely, a “reask” that re-prompts the model, or a “fix” that substitutes “a user-defined hard-coded default LLM response”. It publishes a benchmark for that path, and publishes both halves of it — “True Positives: 86.43% of 656 jailbreak prompts failed” alongside “False Positives: 13.95% of 2000 regular prompts failed”, at “1.41 median latency for end-to-end LLM call on GPT-3.5” — which is more than most vendors disclose about their own detector, and a dataset embeddings guard that intercepts the call when “the cosine distance between the input message and any of the chunks is within the user-specified threshold (default setting is 0.2)”.

So the real distinction is about binding and about custody, and it is worth putting to them in those terms. A Guard is instantiated with your prompts or datasets and handed a user message, retrieved context and an LLM response at run-time; it acts on the message it is given, by the code that gives it. Token Observe’s verdict at step 6 is taken by the process that holds the provider key, on the payload as it is about to leave, after Unicode sanitisation has already stripped smuggled invisible characters so the detector and the model read the same string. One is a check your application chooses to run; the other is a condition of reaching the provider at all.

The second difference is what the refusal leaves behind. A blocked or defaulted message in the application is, from an auditor’s point of view, an application behaviour; the record of it is whatever your application logged. A refusal at the hop closes the trace as blocked, returns ACP_POLICY_BLOCKED to the caller, and appends the decision to a hash chain alongside the policy that fired and the approver who did or did not act. That difference matters exactly when somebody external is asking, and not before.

Ask where the Guard runs
Their guardrails page describes instantiation and application to messages; it does not state which process or network position that code occupies. Get the answer in writing for the deployment you would buy.
Ask what an uninstrumented agent is subject to
A guardrail binds the code that calls it. The question is not whether it works but what proportion of your agent traffic reaches it.
Ask what the refusal records
Whether a blocked or defaulted response produces a durable, exportable record with the rule that fired, and who can alter it afterwards.

Telemetry is written for the team that owns the service; evidence is written for somebody else

Retention is the cleanest illustration, and it cuts against Token Observe as often as for it. Arize’s plans retain 15 days on Free, 30 days on Pro and a custom period on Enterprise, which is a sensible shape for debugging: the trace you need is almost always the recent one, and storage is the cost. Token Observe’s trace retention is unset by default and unset means keep forever, which is the right default for an audit question asked eighteen months later and the wrong default for a privacy review — so it is published as a limit rather than as a feature, and it is the first setting to change on a real deployment.

The second is who the record is protected from. Their trust centre names auditability among three security pillars and publishes a shared-responsibility model that puts SSO, application permissions and application data on the customer’s side; per-record integrity is not described on the pages read for this comparison. Token Observe’s audit log is hash-chained, each row’s digest covering its canonical content plus the previous row’s hash, appended inside a transaction that also takes the chain tip so concurrent writers cannot fork it, with a checkpoint MAC sealing the head at every boot and optional Ed25519 anchors published off-box. The claim that buys is precise and small: any copy you kept off-box beats any rewrite made after you took it. It is tamper-evident, not tamper-proof, and unkeyed it does not stop an operator who rewrites a row and recomputes every hash after it.

The third is what an export is for. Arize’s material describes exploring, filtering and dashboarding traces, which is what an engineer needs. A compliance export from Token Observe is a different artefact: the period’s traces and events, the approvals with approver identity and rationale, the audit entries for every governance-plane change, a chain verification result naming the sequence number of any break, and a SHA-256 digest of the bundle generated at a recorded time — verifiable offline by one Node script with no install, no database and no network. The bundle is not itself signed, and saying so is part of the point: durable origin evidence comes from the keyed chain plus the off-box anchor, not from the digest.

which to choose

Where each one is the right answer

Choose Arize when

  • The question you need answered is why quality dropped, which prompt version regressed, or what a multi-step agent actually did — Arize publishes online and offline evals, an Eval Hub of reusable evaluators, session-level evaluations, experiments and agent debugging, and Token Observe has none of them and is not building them.
  • You need datasets, experiments, an evaluator hub or a judge that learns from your telemetry. Those are named strategic non-goals for Token Observe rather than backlog items.
  • Procurement requires published certifications: their compliance page lists SOC 2 Type II, PCI DSS 4.0, HIPAA and CSA STAR Level 1, and their trust centre adds ISO/IEC 27001 and GDPR. Token Observe publishes none.
  • You want to start free and small. AX Free is $0 with 25k spans and 15 days of retention, and AX Pro is $50 a month; Token Observe has no hosted plan and no published price.
  • You cannot accept a fail-closed component in the request path, which is a legitimate position for an estate whose agents draft text a human reads before anything happens.
  • You need the observability platform to run in an air-gapped Kubernetes environment your team already operates, which their self-hosted material describes in detail.

Choose Token Observe when

  • The agents take actions somebody has to answer for — a refund, a deployment, an email, a ticket transition, a database write — and a span attribute recording that it happened is not the control you were asked for.
  • You need a human decision bound to one exact payload rather than to an action type, spendable once, expiring, with the approver recorded against the trace.
  • The sensitive value leaving your network is itself the incident, so it has to be tokenised or refused before egress rather than annotated on the record of a call that completed.
  • The spend ceiling has to hold before the money is spent, including for a model whose price you have not loaded, rather than be read off a dashboard afterwards.
  • Somebody will eventually ask who says the record you are showing me is the record, and a retention tier is not an answer to that question.
  • Agents themselves need to be principals with owners, risk tiers, deny-by-default action permissions and a delegation chain that intersects rather than accumulates.
the usual answer

When you would run both

Running both is the normal answer, and neither product has to give anything up for it. Arize stays where it is, instrumented in your application, answering the questions an AI engineering team asks daily: the trace tree of the run that regressed, the evaluation that scored it, the experiment that fixed it, the monitor that noticed. Token Observe goes in front of the providers as the hop that holds the credential, and answers the question somebody external asks occasionally: on what authority, approved by whom, at what cost, and can you prove the record has not moved. The overlap is genuine — both hold traces, both compute cost from token counts — and it is small enough not to be worth resolving, because Token Observe has no evaluation harness, no prompt playground, no datasets and no experiments, and its own roadmap names building them as a strategic non-goal. Where the two meet cheaply is OpenTelemetry: Arize’s instrumentation is built on it, Token Observe runs an OTLP-over-HTTP receiver as an input, and a collector in the middle can fan the same spans to more than one destination if you want the governed view and the application view reconciled. Route selectively rather than universally — the agents that issue refunds, merge code, send mail or write rows through Token Observe, everything else straight through — because a second component in the request path is a second failure domain, and a chat assistant does not need one.

The category argument sits above this one: Token Observe and llm observability covers what the whole category does and does not do, which is the better page to read if you have not yet shortlisted a product.

Do we have to replace Arize to use Token Observe?

No, and replacing it would be a mistake for most readers. Arize publishes evals that run continuously against live traces or on demand against a dataset, an Eval Hub of reusable evaluators, session-level evaluations, human annotation of traces and spans, experiments, agent debugging through Signal, prompt optimisation and more than thirty tracing integrations; Token Observe has none of that, records only the requests that crossed its boundary, and names another standalone tracing and evaluation product as a strategic non-goal rather than a backlog item. The intended arrangement is both: Arize keeps answering developer questions from inside your application, and Token Observe holds the verdict and the evidence in front of your providers.

Arize publishes guardrails. Is that not enforcement?

It is, and any comparison that says otherwise is wrong. Their guardrails documentation describes guardrails that correct undesirable outputs at run-time, applied to user input messages such as jailbreak attempts or to LLM output messages such as answer relevance, with failed messages blocked entirely, re-asked, or answered with a user-defined hard-coded default response, and it publishes a benchmark in which 86.43% of 656 jailbreak prompts failed the Guard against 13.95% of 2,000 regular prompts, at 1.41 median latency for an end-to-end call on GPT-3.5. The distinction this page draws is narrower: a Guard is an object your application instantiates and hands a message, so it binds the code that calls it, whereas Token Observe’s verdict is taken by the process that holds the provider credential and therefore binds everything that needs the key. Ask them where in your stack that Guard runs, and what an agent that does not call it is subject to.

Can Arize stop an agent from overspending?

Their cost-tracking documentation describes computing cost for every span from token counts against a cost configuration you define, looked up by model name and provider and calculated per million tokens for each token type, then filtering traces or spans above a threshold, creating monitors for high-cost traces, and building dashboards by token type or cost grouping; it also warns that “Cost is not retroactive. To track costs, you must configure pricing before ingesting traces.” It does not describe a budget that refuses a request, and their monitors page describes notification to Email, Slack, PagerDuty, OpsGenie or an HTTP webhook rather than traffic being blocked. That is a question to put to them in writing for the plan you would buy. Token Observe takes the money verdict before egress: it prices every provider and fallback the resolved route could execute, reserves the most expensive of them against the agent’s hour, day and month windows inside one transaction, and refuses a budgeted agent with a 409 when a reachable target has no price rather than metering it at zero.

Which one does an auditor want?

It depends on what they are auditing, and the honest answer splits. If the audit is of your supplier, Arize wins outright: their compliance page lists SOC 2 Type II, PCI DSS 4.0, HIPAA and CSA STAR Level 1, their trust centre adds ISO/IEC 27001 and GDPR, and Token Observe publishes no certification and has had no independent penetration test. If the audit is of a specific action an agent took — on what authority, approved by whom, with what evidence that the record has not been altered since — Token Observe is built for that question: reads of the record are themselves attributable, every governance-plane change is appended to a hash chain, and a compliance export carries the approvals, the audit entries, a chain verification result naming the sequence number of any break, and a SHA-256 digest of the bundle. The export is digest-sealed rather than signed, and the chain is tamper-evident rather than tamper-proof; both distinctions are published rather than glossed.

Have these claims about Arize been tested?

No. Everything in the Arize column comes from the pages listed in the sources, read on 2 September 2026, and none of it has been independently tested — the same caveat Token Observe’s own competitive benchmark states about itself. Where a cell says a capability is not described in their published documentation, that is a statement about what those pages said on that date and not a claim that the capability does not exist; a feature that is merely undocumented reads identically to one that is absent, and the difference matters enough to ask about. Arize ships quickly, so treat any row older than a quarter as a question for the vendor rather than a fact about the product.

Ask about this comparison
Ask where the alternative genuinely wins, or which of the two fits what you are actually trying to do. It will tell you when the answer is the other one.

Prefer to ask a person? Write to us →

get in touch

Tell us which one you are already running.

If Arize is already in your stack, the useful question is not which to buy but what each is for, and where the seam between them sits. Say what you have and you will get a straight answer — including when the answer is that you do not need a second thing.

no form · no qualification step · no sales desk · the other three ways in