AI Gateway & Observability
FISTA Solutions builds the layer every AI call passes through: provider and model routing as configuration, per-team cost attribution and budgets, prompt and response logging with redaction, end-to-end tracing, rate limits, and policy enforcement — so AI usage across the organization is visible and governable.
- 150+
- projects delivered
- 50+
- companies served
- 99.9%
- verified uptime
- 47%
- efficiency gains
- 12+
- countries reached
What we build
What does AI gateway and observability include?
Gateway work covers provider abstraction and routing, failover and retry behavior, per-team authentication and quotas, cost attribution, prompt and response logging with redaction, semantic and exact caching, tracing, and policy enforcement points.
- 01
Routing and failover
Provider and model selection as configuration, with failover and retry behavior defined centrally.
Routing - 02
Budgets and quotas
Per-team and per-application budgets with alerts and enforcement, so spend is bounded by design.
Control - 03
Logging with redaction
Prompt and response logging with PII redaction, retention limits, and scoped access.
Compliance - 04
Caching
Exact and semantic caching where appropriate, cutting both cost and latency on repeated work.
Efficiency - 05
Tracing and dashboards
End-to-end traces with quality, latency, and cost dashboards per team and application.
Observability - 06
Policy enforcement
Content policy, model allowlists, and data-handling rules enforced at the gateway rather than per application.
Governance
Requirements
Which requirements shape AI gateway and observability?
A gateway sits on the critical path for every AI request, so it must be fast, highly available, and never a bottleneck — while delivering the control that justifies its existence: attribution, policy, logging, and reversible provider choice.
| Requirement | Why it matters | How FISTA implements it |
|---|---|---|
| Latency overhead | The gateway must not slow AI down. | Overhead budgeted in single-digit milliseconds, measured continuously and treated as a regression when exceeded. |
| Availability | A gateway outage is an AI outage. | Redundant deployment with health checks, and a documented bypass path for emergencies. |
| Cost attribution | Untracked spend cannot be governed. | Per-team and per-application attribution on every request, with budgets and alerting. |
| Logging compliance | Prompts contain sensitive data. | Redaction before storage, retention limits, and access controls documented for privacy review. |
| Provider reversibility | Lock-in is a business risk. | Provider abstraction so switching or adding a model is configuration rather than an application change. |
Where AI fits
How should you sequence AI gateway and observability?
Introduce a gateway before AI usage sprawls: route one application through it, add attribution and logging, then make it the required path with policy enforcement and caching once teams see the benefit.
- 01
1. Route one application
A single application through the gateway proves latency and reliability before wider adoption.
- 02
2. Add attribution
Per-team cost visibility, which is usually what makes the gateway immediately popular with finance.
- 03
3. Add logging and tracing
Redacted logs and traces so debugging AI behavior stops being guesswork.
- 04
4. Make it the path
Policy enforcement and model allowlists once teams rely on it, replacing direct provider access.
- 05
5. Optimize
Caching and routing tuned against real traffic to cut cost without changing applications.
Cost and timeline
What does AI gateway and observability cost, and how long does it take?
Cost is driven by traffic volume and feature scope; the gateway typically pays for itself in caching and routing savings. FISTA does not quote blind: the scoping call returns a design and a savings estimate.
Gateways usually pay for themselves. Caching, routing to cheaper models for simple tasks, and eliminating duplicated calls typically save more than the gateway costs to build and run.
The governance value is harder to price and often larger. Knowing what AI is being used for, by whom, and at what cost is what makes the next round of AI investment a decision rather than a guess.
Send the scope you have, even if it is a paragraph. You get a written brief, an architecture sketch, and a phased estimate before any commitment.
Get a scoped quoteDelivery
How does FISTA deliver an AI deployment?
FISTA deploys AI in four phases: an assessment that inventories workloads, data boundaries, and constraints and produces a target architecture; a platform build with networking, identity, secrets, and observability as code; a migration with evaluation gates and shadow traffic; and a production cutover with dashboards, budgets, runbooks, and rollback.
- 1
Assess and target
Workload inventory, data classification, latency and volume profile, compliance constraints, and a target architecture with cost model.
OutputTarget architecture, cost model
- 2
Build the platform
Networking, identity, key management, model endpoints, gateway, tracing, and evaluation pipeline delivered as infrastructure-as-code.
OutputPlatform as code, control matrix
- 3
Migrate with gates
Move applications behind the gateway, run evaluation and shadow traffic, and tune routing, caching, and capacity.
OutputEval reports, shadow results
- 4
Cut over and operate
Graduated production rollout, dashboards for quality, latency, and cost, runbooks, on-call, and a change process with rollback.
OutputProduction platform with SLOs
Why FISTA
Why choose FISTA Solutions for AI gateway and observability?
FISTA builds gateways that add control without adding latency, with attribution and redaction from the first release. Work is contracted through a US entity with full IP assignment.
AI Gateway specifics
- Gateway overhead is budgeted in single-digit milliseconds and measured continuously as a regression signal.
- Per-team and per-application cost attribution exists from the first release, not as a later reporting project.
- Prompt and response logging is redacted before storage with retention limits and scoped access.
- Provider abstraction keeps model and vendor choice reversible as the market changes.
How FISTA engineers
- Spec-Driven Development: every deliverable starts as a written specification with acceptance criteria, so scope is testable before it is built.
- AI-native delivery: engineers direct coding agents under review gates and evaluation harnesses, compressing build time without loosening verification.
- Official Anthropic partner, with production experience across Claude, OpenAI, Google, and open-weight models, chosen per workload rather than by default.
- One accountable delivery lead, weekly demos on your environment, and code in your repositories from week one.
What you get as a client
- 150+ projects delivered for 50+ companies across 12+ countries since 2017, with 99.9% verified uptime on systems we operate.
- A US entity (FISTA Solutions Inc., Wilmington, Delaware) for contracting, invoicing, and IP assignment, with an engineering center in Faisalabad, Pakistan for cost-efficient senior capacity.
- US business-hours overlap for standups and reviews; written decision logs so nothing depends on a meeting you missed.
- Flexible engagement: fixed-scope build, embedded forward deployed engineers, or a dedicated team that you can scale month to month.
Clear answers
What platform teams ask before deploying AI.
Straightforward guidance for evaluating scope, fit, and the next step.
01Will a gateway slow down our AI calls?
It adds a small amount of latency, budgeted in single-digit milliseconds and measured continuously. Caching typically makes net latency better rather than worse for repeated work.
02What happens if the gateway goes down?
It is deployed redundantly with health checks, and a documented emergency bypass path exists. A single-instance gateway on the critical path would be an unacceptable risk.
03Can it route between providers?
Yes, by task, cost, or availability, with failover. That routing is what makes provider choice reversible and lets simple tasks use cheaper models without application changes.
04Is prompt logging a privacy problem?
It can be, which is why redaction happens before storage, retention is limited, and access is scoped and logged. The configuration is documented for your privacy review.
05How long does a gateway take to build?
A working gateway with routing, attribution, and logging typically takes weeks; policy enforcement and caching follow as adoption grows.
Scoped in writing before you commit
Put every AI call on a path you can see and govern.
Bring your current AI usage, known and suspected. The scoping call returns a gateway design and a savings estimate.