SaaS, AI & Developer Infrastructure
API Observability Intelligence API
Your customers have more dashboards than they can read. What they do not have is anything that tells them which of ten thousand endpoints is about to become an incident.
- API gateway and management vendors
- Developer platforms
- Observability tools
- Hosting and PaaS providers
- Integration platforms
What your customers would see
example interfaceMockupIllustrative mockup of the interface we would build. Figures shown are examples, not results from a live deployment.
The problem this solves
Observability has an abundance problem rather than a data problem. A platform serving a few thousand API customers collects latency percentiles, error rates, throughput and traces for every endpoint, and presents them as charts somebody has to think about. The signal is in there. Nobody has time to look for it, so problems are found by customers complaining instead.
Threshold alerting is the usual next step and it ages badly. A fixed 500ms alert fires every Monday morning when traffic peaks and stays silent through a slow degradation from 80ms to 400ms over three weeks. Error rate alerts fire on a deploy that caused a hundred identical validation failures with the same root cause, and present them as a hundred separate problems.
What changes the economics is that the useful work is statistical rather than infrastructural. Seasonality-aware baselines per endpoint, error clustering by signature so one deploy produces one alert, degradation trends extrapolated to when a service will breach its own limits, and dependency attribution so the alert names the slow downstream rather than every caller of it. For a gateway or developer platform, this turns metrics you already store into a feature you can charge for.
Who buys this
API gateway and management vendors, Developer platforms, Observability tools, Hosting and PaaS providers, Integration platforms
What the API returns
What comes back
- Anomalies per endpoint scored against a seasonality-aware baseline, not a fixed threshold
- Error clusters grouped by signature, with first-seen time and the deploy or config change nearby
- Latency trend decomposition into seasonal, trend and residual components
- Degradation forecasts: when a trend reaches a stated limit, with a confidence interval
- Dependency attribution so an alert names the slow upstream rather than its callers
- Change-point detection correlated with deploys, releases and configuration updates
- Per-consumer views, so a platform can tell one customer their integration is the problem
- Alert quality statistics — precision, acknowledgement and suppression rates by rule
Endpoints
| Method | Path | Returns |
|---|---|---|
| POST | /v1/ingest | Push request metrics, error records and deploy events |
| GET | /v1/anomalies | Current anomalies ranked by severity and blast radius |
| GET | /v1/errors/clusters | Error clusters with signatures, trends and first-seen times |
| GET | /v1/endpoints/{id}/forecast | Latency and error-rate forecast with confidence bands |
| GET | /v1/incidents/{id}/attribution | Likely contributing services and changes for an incident |
| WS | /v1/stream | Push anomalies and threshold forecasts as they are detected |
Request and response
illustrativeRequest
{
"tenant_id": "gatewayco",
"scope": { "service": "payments-api", "window": "last_24h" },
"include": ["anomalies", "error_clusters", "forecast"],
"forecast_limits": { "p95_latency_ms": 800, "error_rate": 0.02 },
"min_severity": 50
}Response
{
"service": "payments-api",
"generated_at": "2026-10-22T13:20:04Z",
"anomalies": [
{
"endpoint": "POST /v1/charges",
"metric": "p95_latency_ms",
"observed": 612,
"expected_range": [180, 260],
"severity": 84,
"started_at": "2026-10-22T11:05:00Z",
"seasonality_adjusted": true,
"affected_consumers": 214
}
],
"error_clusters": [
{
"cluster_id": "err_c_118",
"signature": "UpstreamTimeout: ledger-service connect ETIMEDOUT",
"count_24h": 4188,
"first_seen": "2026-10-22T11:04:12Z",
"nearby_change": { "type": "deploy", "ref": "ledger-service@2026.10.22-1", "offset_s": -48 },
"distinct_consumers": 214
}
],
"forecast": {
"endpoint": "POST /v1/charges",
"metric": "p95_latency_ms",
"limit": 800,
"projected_breach": "2026-10-22T17:40:00Z",
"confidence_interval": ["2026-10-22T15:10:00Z", "2026-10-23T02:30:00Z"]
},
"attribution": { "most_likely_cause": "ledger-service deploy", "confidence": 0.74 }
}Where the data comes from
| Your gateway and proxy logs | Request-level records you already emit — route, status, latency, consumer and upstream — which is most of what the analysis needs |
| Metrics and trace backends | Existing Prometheus, OpenTelemetry or equivalent stores, read rather than replaced; nobody wants a second collection agent |
| Deploy and change events | Release, configuration and feature-flag events from your CI and deployment systems, which is what makes attribution possible rather than speculative |
| Incident history | Past incidents with their causes and timelines, used to validate that forecasts would have fired early enough to matter |
| Service dependency topology | Declared or inferred call graphs, so attribution points at a cause instead of listing symptoms |
| Consumer and tenant metadata | Plan, region and integration version, so per-consumer impact is reportable without exposing one customer's traffic to another |
The intelligence layer
The difference between a demo and a service is mostly in this section. These are the parts that keep the answer trustworthy once real traffic is hitting it.
- Per-endpoint baselines with daily and weekly seasonality, because a Monday-morning peak is not an anomaly and a quiet Sunday spike is
- Error clustering on normalised signatures — stack frames, message templates and parameter stripping — so one cause produces one alert
- Change-point detection on latency and error series, correlated against deploy events within a bounded window rather than by eyeballing a chart
- Trend extrapolation with explicit confidence intervals, since a breach forecast stated as a single time is read as a promise
- Dependency-aware attribution that walks the call graph, so the slow downstream is named once instead of every caller being alerted
- Alert precision measured from acknowledgement and suppression behaviour, with noisy rules reported back for tuning
- Cardinality control on endpoint and label dimensions, because unbounded path parameters are what makes observability bills explode
- Validation against held-out incident history: would this have fired, and how long before the customer noticed
What it changes for the business
Problems found before customers report them
Degradation forecasting gives a team hours of warning on trends a threshold alert will never catch.
Alert volume that people still read
Error clustering and seasonality-aware baselines turn a hundred notifications about one deploy into one.
A premium tier with a clear boundary
Intelligence sits naturally above raw metrics in a pricing table, and customers understand why it costs more.
Support deflection
Per-consumer attribution lets you tell a customer their own integration changed, with evidence, instead of investigating it for them.
How it fits into your product
How it embeds
- White-label API and components inside your existing console, with your brand throughout
- Reads your current metrics and log backends rather than requiring a new agent in your customers' infrastructure
- WebSocket push and webhooks into the incident tools your customers already run
- Per-tenant isolation with scoped keys, so analysis of one consumer's traffic is never visible to another
- Metering by monitored endpoint or ingested event volume, which maps cleanly onto a usage-based price
- In-region or in-cluster deployment where telemetry cannot leave a customer's environment
Included in every build
- Authentication, scoped API keys and per-customer rate limiting
- Input validation with predictable, documented error responses
- Versioned endpoints so your integration never breaks without notice
- Request and response logging for audit and dispute resolution
- Latency, error-rate and usage monitoring with alerting
- OpenAPI specification, sandbox environment and client examples
- Backups, failover design and a documented recovery procedure
- An agreed support arrangement once the service is live
How we would build it
about 12 weeksDiscovery and data review
Weeks 1–2
We agree the exact question the API answers, review the sources you already hold, check what has to be licensed, and write down the response contract your developers will code against.
Architecture and prototype
Weeks 3–4
You get a written architecture plus a working endpoint on real data — narrow, but genuinely callable — so integration can be tested against something concrete rather than a document.
Production build
Weeks 5–10
Pipelines, models, storage, authentication, rate limiting, logging, tests and documentation. Security and observability are part of this phase, not a later one.
Integration and handover
Weeks 11–12
We support your engineers through their integration, run load tests at your expected volume, and agree how the service is monitored and supported once it is live.
Commercial models
There is no fixed price list, because the cost is driven by data complexity, integration count, model requirements, request volume and the support level you need. Scope is agreed in writing before development starts.
Fixed-scope prototype
A defined slice of the service, built and priced as one piece of work. The usual first step when the business case needs proving internally.
Fixed-price build
An agreed scope against a written architecture, delivered for an agreed price. Best when the requirements are already clear.
Build then retainer
We build the service, then operate and improve it under a monthly agreement covering monitoring, updates and model maintenance.
White-label product
The service runs under your brand, your domain and your pricing, with MLPivot invisible to your customers.
Questions buyers ask
Can it predict incidents?
It can extrapolate measurable trends, which is a narrower thing. A gradual latency climb or a steadily rising error rate can be projected to a breach time with a confidence interval, and that is genuinely useful. A sudden failure — a dependency falling over, a certificate expiring, a bad config push — has no prior trend to extrapolate and will not be forecast. We would present the first as a projection and never describe the second as predictable.
How much data before baselines are useful?
Two to three weeks per endpoint to learn weekly seasonality properly, and longer for anything with monthly or quarterly patterns. Before that the API reports wide expected ranges rather than confident anomalies. Low-traffic endpoints are harder: with a few requests an hour, most statistical detection is noise, and we would group or exclude them rather than alert on them.
Will this replace our existing observability stack?
No, and it should not try. It reads what you already collect and adds a layer of analysis on top. Replacing a working metrics and tracing stack is an expensive project with little upside, and a vendor proposing it is usually selling storage rather than insight.
How confident is the attribution?
Confident enough to shorten an investigation, not confident enough to be treated as a root cause. Correlating a change-point with a deploy forty-eight seconds earlier is strong evidence; it is still correlation, and the API returns a confidence value rather than a verdict. Co-occurring deploys are the common failure mode and are reported as competing candidates rather than resolved arbitrarily.
What about the cost of all this analysis?
It is a real constraint and worth sizing early. High-cardinality endpoints, verbose traces and short retention windows all push cost up, and the honest answer is sampling plus cardinality limits rather than analysing everything. We would model the ingest volume and cost during discovery so the pricing you offer your customers has a known margin.
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Want this built for your platform?
Tell us what your product does today and what you want another system to be able to ask it. We will come back with an approach, a scope and a price.