API7 Gateway 3.10.1 Released: Enhanced AI Gateway Observability, Reliability, and Governance

Key Takeaways

Production LLM traffic needs more than HTTP status and latency: teams also need token usage, time to first token, streaming mode, tool-call activity, and end-user context.
API7 Gateway 3.10.1 adds LLM-focused Prometheus metrics and built-in variables that bring this context into gateway logs.
AI Proxy Multi adds bounded fallback controls so teams can decide when another model instance should be tried without automatically doubling latency after slow failures.
Request-size limits, correct timeout responses, and healthier instance selection make AI traffic behavior easier to interpret during incidents.
Advanced rate limiting now lives directly in limit-count, reducing configuration complexity for enterprise quota and traffic governance.
Developer Portal upgrades strengthen developer authentication, platform administration, approval workflows, and the API documentation experience.
Adopting 3.10.1 requires coordinated review of dashboards, security defaults, traffic controls, and Developer Portal credential workflows.

An AI feature can look healthy on a traditional API dashboard while users are already having a poor experience.

The request may return HTTP 200, but the first token arrives too slowly. Total latency may look acceptable, but only because a short response hides a slow model. Token usage may climb without a matching increase in request volume. A fallback may eventually succeed while doubling the user's wait. Tool-calling traffic may fail differently from ordinary chat completions, yet both appear under the same route and status-code charts.

These are not edge cases once large language model applications reach production. LLM traffic is still API traffic, but its cost, latency profile, streaming behavior, and failure modes require additional operational context.

API7 Gateway 3.10.1 is a major platform release built around that production challenge. AI Gateway improvements remain its technical center: new Prometheus metrics expose token and latency distributions, built-in NGINX variables describe streaming and tool behavior, AI Proxy Multi adds limits around fallback, and reliability fixes make upstream outcomes easier to interpret.

But production AI does not operate apart from the rest of an API program. The same platform teams must govern quotas, protect developer access, approve subscriptions, and give consumers usable documentation. API7 Gateway 3.10.1 therefore extends beyond LLM observability with unified enterprise rate limiting and a more capable Developer Portal. The release connects AI traffic operations with the broader responsibilities of an API management platform.

flowchart LR
    apps[AI Applications] --> gateway[API7 AI Gateway]
    gateway --> context[LLM Request Context]
    gateway --> metrics[Prometheus Metrics]
    gateway --> policy[Bounded Fallback Policy]
    context --> logs[Gateway Logs]
    metrics --> dashboards[Operational Dashboards]
    policy --> models[Model Instances]
    logs --> response[Incident Response]
    dashboards --> response

HTTP Monitoring Is Necessary but Not Sufficient for LLMs

Standard API observability begins with request rate, error rate, and latency. Those signals remain essential for AI traffic. They reveal whether an endpoint is reachable, how often it fails, and how long the complete request takes. But they do not explain enough about an LLM interaction.

Streaming changes how users perceive latency. A response that takes 20 seconds to complete may feel responsive if the first token arrives quickly and output continues steadily. The same total latency feels broken if the user waits 18 seconds before seeing anything. This makes time to first token, or TTFT, an operational signal rather than a model benchmark alone.

Token volume also changes the meaning of traffic. Two requests can have the same status and duration but dramatically different cost. Prompt tokens, completion tokens, reasoning tokens, and tokens read from or written to a provider cache can affect spend and capacity planning. A rising token distribution can reveal a prompt change or a new use case even when the request count is flat.

Tool calls add another dimension. An LLM response that invokes tools has a different operational path from a text-only response. Teams may need to know whether a request was streaming, how many tools were exposed, and whether the model produced a tool call before they can explain latency or errors.

The gateway is a useful place to collect this context. It already sees the normalized client request, selected provider, upstream response, routing decision, and final HTTP outcome. Capturing LLM-specific signals there avoids requiring every application team to invent a separate logging and metrics schema.

Measure the Experience and the Work Behind It

API7 Gateway 3.10.1 adds Prometheus histograms for prompt-token and completion-token distributions. It also adds a total-latency histogram and exposes streaming TTFT through apisix_llm_latency{type="ttft"}. Histogram buckets are configurable in the Prometheus plugin settings, allowing teams to align dashboards with the latency and token ranges that matter to their applications.

These metrics support several practical views:

TTFT percentiles for streaming endpoints, which reflect how quickly users begin receiving output;
total latency percentiles, which show how long the entire generation takes;
prompt-token distributions, which help identify growing input size and context-window pressure;
completion-token distributions, which help explain output length, cost, and total generation time;
comparisons between providers, routes, or environments when the gateway metric labels support the required breakdown.

The release also changes the existing TTFT metric. The standalone apisix_llm_ttft metric is replaced by the ttft type on apisix_llm_latency. Teams upgrading to 3.10.1 should update Prometheus queries, recording rules, alerts, and Grafana panels that still reference the previous metric name. Otherwise, an upgrade can create an observability blind spot even though the underlying capability has improved.

flowchart TD
    request[LLM Request] --> prompt[Prompt Token Distribution]
    request --> first[Time to First Token]
    first --> stream[Streaming Response]
    stream --> completion[Completion Token Distribution]
    completion --> total[Total Latency]
    prompt --> dashboard[AI Operations Dashboard]
    first --> dashboard
    completion --> dashboard
    total --> dashboard

Metrics should be interpreted together. A TTFT increase with stable prompt size may point to provider queueing or network delay. An increase in total latency alongside longer completions may reflect expected application behavior. Higher prompt-token counts combined with slower TTFT may indicate that a new prompt template or larger retrieved context is increasing model work. The gateway does not replace provider-side analysis, but it gives platform teams a consistent view across applications and model endpoints.

Put LLM Context Into the Logs Teams Already Use

Metrics show patterns; logs provide the request-level context needed to investigate them. API7 Gateway 3.10.1 adds built-in NGINX variables for LLM requests:

$llm_total_tokens
$llm_stream
$llm_has_tool_calls
$llm_tool_count
$llm_end_user_id
$llm_cache_read_input_tokens
$llm_cache_creation_input_tokens
$llm_reasoning_tokens

The values are mapped across supported OpenAI Chat and Responses formats, Anthropic, and DeepSeek. Platform teams can include the relevant variables in access-log formats and logger plugins, creating a common operational vocabulary even when applications use different providers.

This is valuable during incident response. If streaming requests show a different error pattern, $llm_stream separates them from non-streaming traffic. If tool-enabled requests become slower, $llm_has_tool_calls and $llm_tool_count provide a way to identify that class of interaction. Cache token fields can help explain why two otherwise similar calls have different usage profiles. End-user identity can support per-user investigation when it is available and appropriate to collect.

Logging every available variable is not automatically the right design. Teams should choose fields that answer known operational questions, avoid high-cardinality dimensions in metrics, and treat end-user identifiers as potentially sensitive data. Access controls, masking, retention, and regional privacy requirements should be reviewed before user context is added to centralized logs.

The goal is a stable schema that helps responders connect a slow or costly request with its LLM behavior, not a maximum-volume record of every possible attribute.

Bound Model Fallback Before It Multiplies Latency

Provider fallback improves availability only when its behavior is predictable. Retrying another model instance after a fast connection failure can rescue a request. Retrying after a provider has already spent a long time processing can double latency, duplicate expensive work, and still leave the user waiting for an error.

AI Proxy Multi in API7 Gateway 3.10.1 adds max_retries and retry_on_failure_within_ms to its fallback mechanism. max_retries limits how many additional instances may be attempted after a failure. retry_on_failure_within_ms restricts fallback to failures that occur within a defined time window, allowing slow failures to return to the client instead of automatically starting another long attempt.

sequenceDiagram
    participant App as AI Application
    participant GW as API7 AI Gateway
    participant A as Model Instance A
    participant B as Model Instance B

    App->>GW: LLM request
    GW->>A: First attempt
    alt Fast eligible failure
        A-->>GW: Failure within retry window
        GW->>B: Bounded fallback attempt
        B-->>GW: Model response
        GW-->>App: Successful response
    else Slow failure
        A-->>GW: Failure after retry window
        GW-->>App: Preserve failure without doubling latency
    end

These controls turn fallback into an explicit latency budget. Teams should set them based on the application experience they want to protect. An interactive assistant may tolerate one rapid fallback but not a second full model-generation attempt. A background summarization job may accept a longer recovery window. The correct policy depends on the workload, provider behavior, and end-to-end timeout—not on a universal retry count.

The release also fixes health-check behavior in AI Proxy Multi. After per-instance health checkers were created, a cached server picker could continue sending some traffic to instances already marked unhealthy. The fix rebuilds selection behavior correctly, helping configured health and fallback policies work as intended. For operators, this reduces the gap between what the dashboard says about an instance and where requests actually go.

Make Failures Easier for Applications and Operators to Interpret

Observability depends on accurate outcomes. If a gateway converts a provider timeout into the wrong status or removes an error body, both applications and operators lose useful information.

API7 Gateway 3.10.1 improves several AI Proxy failure paths. An upstream LLM timeout, including a DNS-resolution timeout, now returns HTTP 504 instead of HTTP 500. That distinction matters: 504 Gateway Timeout identifies a gateway waiting unsuccessfully for an upstream, while a generic 500 can suggest an internal gateway error.

The release also addresses request forwarding in passthrough mode. The client's HTTP method and query string are now preserved rather than forcing POST and dropping query parameters. This is important for providers such as Azure OpenAI, where an API version may be supplied in the query string. A request rejected because its query string disappeared can look like a provider or credential problem unless the forwarding behavior is understood.

Request-body limits are now explicit for ai-proxy and ai-proxy-multi, with a default of 64 MiB. Oversized requests are rejected with HTTP 413. Most deployments remain constrained by the lower NGINX client_max_body_size default, but teams that intentionally allow large prompts or multimodal payloads should align both limits. Explicit bounds protect memory while giving clients a recognizable response when a payload is too large.

Together, correct status codes, preserved request semantics, bounded bodies, and reliable health selection make dashboards and logs more trustworthy. A responder can classify an incident faster when the status and context describe the actual failure domain.

Build an Operating Model Around the New Signals

The release provides the instrumentation, but teams still need to decide how it becomes part of daily operations. A practical rollout can follow five steps.

Update the metric migration first. Replace apisix_llm_ttft queries with apisix_llm_latency{type="ttft"} in dashboards, alerts, and recording rules before upgrading production gateways.
Choose service-level indicators. Define TTFT and total-latency objectives separately for interactive streaming and non-streaming workloads. Add token distributions where they help explain capacity or cost.
Design a minimal LLM log schema. Select request-level variables that responders will use. Review end-user identifiers and token information with security and privacy owners.
Set fallback budgets per workload. Configure retry count and eligible-failure timing based on the application's latency budget. Test fast failures, slow failures, and unhealthy instances.
Test real client behavior. Validate passthrough methods and query strings, large request rejection, provider timeouts, streaming responses, and tool calls in staging.

Platform teams should also decide who owns each signal. The gateway team may own availability, provider routing, and fallback. Application teams may own prompt size and tool definitions. FinOps teams may care about token distributions. Security teams may govern end-user logging. A shared dashboard is most useful when each alert has a clear operational owner.

With that AI operating model established, the rest of the release broadens the same principle—centralized control with explicit ownership—from model traffic to API quotas and developer access.

Simplify Enterprise Rate Limiting with Unified Limit Count Capabilities

Rate limiting is one of the clearest expressions of API governance. It translates service capacity, commercial plans, and consumer entitlements into rules enforced on every request. That work becomes harder when operators must choose between a standard plugin and a separate advanced variant for different environments.

API7 Gateway 3.10.1 brings the capabilities previously exclusive to limit-count-advanced directly into limit-count. The standard plugin now supports Redis Sentinel deployments, sliding-window counting, multiple independent limits in one configuration, dynamic count and time_window values from NGINX variables, and delayed Redis synchronization through sync_interval.

This unification matters operationally. A platform team can express a per-consumer quota dynamically, combine short burst protection with a longer usage allowance, and use Redis Sentinel for a more resilient shared counter store without switching to a separate plugin model. Multiple rules also let teams keep related limits together instead of distributing quota logic across several configurations.

Existing limit-count-advanced configurations continue to work because the plugin remains as a thin wrapper, so 3.10.1 does not require an immediate configuration migration. The important upgrade effect is the Redis-backed counter format: keys are versioned, existing counters are not migrated, and counters reset once during the upgrade before expiring on their original TTL. Teams should account for that brief reset window when enforcing contractual quotas or protecting capacity.

The result is simpler API traffic management without reducing policy flexibility. Enterprise-grade controls become part of the primary rate-limiting path, making configurations easier to standardize, review, and operate across gateway groups.

Improve API Product Management with a More Powerful Developer Portal

Operating an API management platform is not only about controlling runtime traffic. Organizations also need to turn APIs into products that developers can discover, understand, request, and use under clear access policies. API7 Gateway 3.10.1 strengthens that product lifecycle through a broader Developer Portal experience.

Security begins with the developer account. Developers and API consumers can configure TOTP-based two-factor authentication from their account security settings and use a six-digit authenticator code during sign-in. This gives API programs a stronger account control for users who may hold production credentials or access partner-only products.

Platform teams also gain a centralized administration surface. A dedicated admin area can list and search users, change roles, ban or unban accounts, delete users, and filter organizations by membership. These controls make developer access an explicit operational responsibility rather than a collection of manual database or support workflows. Policy-based SSO routing further helps organizations direct users to credentials, magic links, or the appropriate identity provider based on email-domain rules.

For controlled onboarding, the portal adds an approval workflow for developer registration and API product subscriptions. Platform administrators can review and act on requests from the portal, while decisions remain synchronized with the Dashboard and record the acting administrator. That shared workflow is important for enterprise API governance: teams can preserve oversight of who enters the ecosystem and which products they are allowed to consume without pushing every decision into an offline ticket queue.

The documentation experience becomes part of the product experience as well. The portal can host a built-in Markdown documentation site under /docs, with sidebar navigation, a table of contents, code-copy controls, page copying, full-text search, and highlighted result snippets. Developers can move from discovery to implementation without leaving the branded portal or searching through a disconnected documentation system.

Together, stronger authentication, centralized administration, governed approvals, and integrated documentation create a clearer path from API publication to responsible adoption. The gateway governs requests at runtime; the Developer Portal governs how people discover and gain access to the APIs behind those requests.

What to Review Before Upgrading to 3.10.1

API7 Gateway 3.10.1 changes observability, security defaults, traffic controls, and developer workflows. Grouping the review by operational owner makes the upgrade easier to validate.

AI Observability Migration

Replace apisix_llm_ttft with apisix_llm_latency{type="ttft"} in Prometheus queries, recording rules, alerts, and Grafana dashboards.
Confirm histogram buckets are appropriate for current prompt sizes, token distributions, and latency objectives.
Review fallback retry counts, retry timing, and end-to-end application timeouts.
Decide whether LLM end-user IDs or other new log fields require masking, access-control, or retention changes.

Security Changes

Confirm that JWT consumers are ready for default verification of the exp and nbf claims; expired or not-yet-valid tokens that were previously accepted will return HTTP 401.
If Data Plane data encryption is enabled, review the newly encrypted logger, serverless, AI moderation, OpenID Connect, and error-log fields.
Coordinate Control Plane and Data Plane sequencing so an older Data Plane does not receive ciphertext it cannot decrypt.

Traffic Management Changes

Account for the one-time reset of Redis-backed limit-count counters caused by the new versioned counter format.
Review request-body limits at NGINX and the hmac-auth, forward-auth, ai-proxy, and ai-proxy-multi layers.
Check clients using batch-requests: the default maximum is 1,000 pipeline items, undocumented fields are rejected, and timeout must be at least one millisecond.

Developer Portal Changes

Update integrations to capture key-auth keys and basic-auth passwords when credentials are created or regenerated; these secrets are no longer returned by read or list operations.
Confirm the operational owners for TOTP support, platform-admin permissions, registration approvals, subscription approvals, and SSO domain rules.

The complete upgrade guidance is available in the API7 Gateway 3.10.1 release notes. Validate the changes in staging with representative streaming, non-streaming, tool-calling, and fallback traffic before production rollout.

Operate AI and API Traffic as One Platform

Production AI systems need an operational model that reflects how LLMs actually behave. HTTP availability is only the foundation. User experience depends on first-token latency and streaming. Cost and capacity depend on token volume. Reliability depends on provider health, error semantics, and fallback budgets. Incident response depends on having the same context across providers and applications.

API7 Gateway 3.10.1 brings these concerns closer to the gateway layer. LLM metrics describe the experience and workload. Built-in variables add request-level context to logs. Bounded fallback controls protect latency. Reliability fixes make health selection, timeout responses, and provider-specific forwarding more predictable.

The broader platform release connects those AI Gateway capabilities with enterprise API governance and developer experience. Unified limit-count capabilities make sophisticated quotas easier to standardize. Developer Portal security, administration, approvals, and documentation help teams manage APIs as products rather than endpoints alone.

API7 Gateway 3.10.1 helps organizations move from simply routing AI requests to operating AI and API traffic with stronger visibility, predictable reliability, and enterprise governance. In practical terms, the release combines AI Gateway capabilities, API traffic reliability, enterprise policy control, and a better developer experience in one API management platform. Read the API7 Gateway 3.10.1 release notes, explore the API7 AI Gateway documentation, and review the Developer Portal documentation to plan the rollout.