# AI Integration Services & OpenAI Integration Services — Paiteq > AI integration services and OpenAI integration services for existing SaaS — Claude API integration, Vertex, Azure OpenAI, abstraction, fallback, cost engineering. **HTML version:** https://www.paiteq.com/services/ai-integration/ ## Key facts - Providers: OpenAI, Anthropic Claude, Vertex AI, Azure OpenAI. - Posture: abstraction layer, provider fallback, cost engineering, observability. - Built for teams plugging AI into existing SaaS rather than greenfield apps. ## Related pages - [LLM Development](https://www.paiteq.com/services/llm-development/) - [AI Workflow Automation](https://www.paiteq.com/services/ai-workflow-automation/) - [Services hub](https://www.paiteq.com/services/) ## About Paiteq Enterprise AI engineering — production agents, RAG, LLM apps, automation, generative AI. Eval-first, senior-led, fixed-scope engagements. Same-day reply from engineering. NDA counter-signed before discovery. Walk-away clause on every engagement. **Site index for agents:** https://www.paiteq.com/llms.txt **Full content for agents:** https://www.paiteq.com/llms-full.txt **Book a call:** https://www.paiteq.com/contact/ --- ## Full content AI INTEGRATION SERVICES # *AI integration services* for the product you've already shipped. A drop-in ai integration services engagement built for the SaaS product that already has users, an existing UI, and a release train. OpenAI, Claude, Vertex, Azure OpenAI integration, Bedrock, or a self-hosted Llama 4, wired through a provider abstraction layer with rate-limit, fallback, cost ceiling, and eval-gated rollout. The drop-in ai ships behind a feature flag and the ai api integration runs through the gateway; we don't re-platform your stack to ship the integration seam that survives the second provider outage. [Scope an integration](/contact/?topic=ai-integration) [See integration patterns](#patterns) Default stack OpenAI · Anthropic · Vertex · Bedrock Gateway LiteLLM / Portkey / in-house Worker Observability Langfuse · Helicone Engagements 1–8 weeks · fixed scope 001 / PATTERNS ## Four integration patterns. Every ai integration services engagement maps to one. Most teams arrive with the right feature idea and the wrong integration shape attached. The four patterns below cover roughly every real ai feature integration we've scoped, drop-in, proxy, sidecar, forked fine-tune deploy. Pick the closest, the kickoff call refines if needed. We won't sell you a sidecar microservice when a two-week drop-in fits, and we won't pretend a drop-in survives the second provider outage when it won't. 01 ### Drop-in The lightest integration: one provider, one endpoint, one feature wired into an existing screen or job. Auth keys live in your secret store, requests hit the provider directly, responses render in the existing UI. Two-week scope from kickoff to first user. We ship this when the feature is genuinely additive, a summarisation button, a draft-reply panel, a classify-and-route hook, and the downside of a provider outage is degraded UX, not broken core flow. Pick when - One feature, one workload, one provider - SaaS product with an existing screen - risk tolerance for direct provider dependency - team can ship the prompt + the UI in a sprint Skip when - Mission-critical workload - multi-tenant where a noisy tenant can blow your rate-limit - cost ceiling matters more than latency - you'll need a second provider within six months Stack OpenAI SDKAnthropic SDKCloudflare WorkersVercel Edge 02 ### Proxy Your app calls a thin gateway (LiteLLM, Portkey, an in-house Cloudflare Worker, or an internal FastAPI service). The gateway speaks every provider's dialect, enforces per-tenant rate-limits, falls back to a secondary provider on 429 / 5xx, logs every call to Langfuse or Helicone, and caps spend per tenant per day. Cost engineering, observability, and vendor portability all converge on one thin layer. This is the integration shape that survives the second provider outage. Pick when - Multiple providers in play - multi-tenant SaaS - cost guardrails or per-tenant quotas are a contract requirement - you need to swap providers without redeploying every microservice - observability is a board-level ask Skip when - Single feature, single provider, single tenant, the proxy adds operational surface that doesn't pay for itself yet Stack LiteLLMPortkeyLangfuseCloudflare WorkersRedisPostgres 03 ### Sidecar microservice The AI feature ships as a separate service, Python on Fly.io, a Cloudflare Worker, a containerised FastAPI on the existing Kubernetes, with its own deploy cadence, its own observability stack, its own eval suite, its own provider config. The host app calls it like any other internal API. Lets the AI team ship without blocking the host-app release train, lets eval failures fail the AI service without taking down the parent product, lets you scale the AI tier independently. Pick when - Existing product on a tight release train - AI feature has its own eval cadence - the AI workload's resource profile differs from the host (long-running, bursty, memory-heavy) - team boundary between product and AI engineering Skip when - Single-team SaaS where the overhead of a second service costs more than the AI feature earns - tightly-coupled UX where latency budget can't absorb an extra network hop Stack FastAPIFly.ioCloudflare WorkersLangGraphvLLMLangfuse 04 ### Forked fine-tune deploy The model itself moves inside your perimeter, Llama 4 70B on H100s via vLLM, a fine-tuned Mistral Small 4 on Bedrock or SageMaker, a LoRA-adapted Qwen 3 on Modal. You pay for compute instead of per-token, you control the upgrade cadence, the data never leaves your cloud account. The integration surface looks similar to the proxy pattern, your app hits an internal endpoint, but the endpoint runs your model, not a vendor's. Cross-link: this is where P3 LLM and P12 MLOps overlap on a real engagement; we own the integration seam, P3 owns model build, P12 owns serving infrastructure. Pick when - Data residency or sovereignty constraint (HIPAA, EU, defence-adjacent) - per-token cost dominates spend at scale - latency budget needs predictable tail - provider's behaviour drift is breaking your evals each release - you have a fine-tune that genuinely beats frontier on your task Skip when - Volume too low to amortise GPU rent (under ~3M tokens/day) - team can't run a model serving stack 24/7 - your eval set doesn't yet justify owning the model lifecycle Stack vLLMLlama 4Mistral Small 4ModalBedrockSageMaker If your shape doesn't fit, the framing call is free, DM us with the constraint that matters most (latency, cost, residency, portability) and we'll write back inside a business day with which pattern fits and what it'd cost. 002 / SHIP ## OpenAI integration services, Claude, and Vertex, what an ai integration company actually delivers. Six deliverables that show up in every production-grade ai integration services engagement. The first two, provider API integration and abstraction layer, are the seams; the other four are the engineering that keeps the seams holding under real traffic. [ 01 / API ↗ Provider API integration OpenAI, Anthropic Claude, Google Vertex, Azure OpenAI, AWS Bedrock, direct SDK integration with auth, retries, structured output, streaming, function calling. The ai api integration shape that ships first in most ai integration services engagements; later retrofits add abstraction. SDKStreamingFunctions ](#providers)[ 02 / LAYER ↗ Provider abstraction layer Thin gateway in front of every provider, uniform contract, model-agnostic request shape, fallback ladder, cost telemetry, prompt-format normalisation. LiteLLM or Portkey at the edge, or an in-house Worker. The integration that survives the next provider outage. LiteLLMPortkeyFallback ](#abstraction)[ 03 / LIMITS ↗ Rate-limit + cost engineering Per-tenant quotas, daily cost ceiling, token-budget-per-request, cache tiers (semantic + exact), burst smoothing, exponential back-off, 429 fallback. Cost ceiling and rate-limit live as code, not as a hopeful chart in the post-mortem. QuotasCacheBack-off ](#cost)[ 04 / FALLBACK ↗ Graceful fallback ladder Primary → secondary provider → degraded mode → human queue. Wired to health checks, latency thresholds, and 429 + 5xx detection. The drill: a vendor goes down, your product holds. We test this once a month on a calendar invite, not as a tabletop exercise. Multi-providerHealthDrill ](#abstraction)[ 05 / ROLLOUT ↗ Eval-gated feature rollout Shadow → 1% → 10% → 100%, gated by eval scores + cost telemetry + user-feedback signal. Rollback is a feature flag, not a redeploy. Every gate is a named metric, a named threshold, a named owner. The AI feature ships behind a flag from day one. ShadowCanaryFlags ](#rollout)[ 06 / OBSERVE ↗ Observability + drift watch Langfuse, Helicone, or LangSmith wired before the first user lands. Per-call cost, latency p50/p95/p99, hallucination scoring, prompt versioning, regression alarms. Production traces feed the eval set monthly so the integration doesn't quietly drift after the launch party. LangfuseDriftAlarms ](#observability) 003 / PROVIDERS ## OpenAI, Claude, Vertex, Azure, Bedrock, or self-hosted, when each one wins. The provider question is the second-most-asked in every kickoff (after "what'll this cost"). The honest answer is workload-shaped, not brand-shaped. Below: six providers we integrate against in production, what each one wins on, where each one breaks, and the Paiteq pattern we default to. None of this is a vendor pitch, we don't take referral fees from any provider, and the proxy layer means we can swap a provider for the next engagement without breaking the host app. Decision cards, strengths · when we pick · when we don't · the pattern we default to. OpenAI Strengths Frontier reasoning with GPT-5; broadest function-calling ecosystem; the realtime Voice API ships sub-400ms turn-take with tool calling baked in; Assistants API for stateful threads. When We Pick Function calling is the dominant workload; voice agent needs realtime streaming; team already has OpenAI procurement done; product needs the broadest tooling ecosystem on day one. When We Don't Hard data-residency requirement; per-token cost dominates spend at scale; you've been burned by behaviour drift between GPT-5 minor releases and your evals can't absorb it. Paiteq Pattern Default for voice + function-heavy workloads. Always paired with eval gates and a secondary provider behind the proxy layer. GPT-5Realtime APIAssistants Anthropic Claude Strengths Claude Opus 4.7 holds the lead on long-context reasoning and instruction-following on contract-grade documents; Claude Sonnet 4.6 is the production workhorse for tool-use agents; computer-use + Skills + MCP support out of the box. When We Pick Long-context retrieval workloads; tool-using agent in production; legal / healthcare / finance where instruction-following on long policy docs is the failure mode; an openai integration services engagement that's hit accuracy ceiling on GPT-5 and needs a second opinion. When We Don't Voice-agent realtime workloads (OpenAI's Voice API still leads on turn-take); workloads where the cheapest possible per-token cost matters more than reasoning quality. Paiteq Pattern Default for agents and long-context. Most claude api integration engagements pair Opus 4.7 for planning with Sonnet 4.6 for tool calls, cost halves, quality holds. Opus 4.7Sonnet 4.6MCP Google Vertex AI Strengths Gemini 3.0 Pro at 1M+ tokens of context is genuinely useful for whole-corpus retrieval; Vertex AI integration includes Model Garden (Llama, Mistral, Claude on Vertex), built-in eval tooling, and tight BigQuery + AlloyDB hooks for enterprises already on GCP. When We Pick Enterprise already on GCP with BigQuery as the data warehouse; multimodal workloads with video; team needs a single procurement / billing surface across frontier + open-weights; whole-document retrieval over PDFs. When We Don't Team is on AWS or Azure and procurement of a third cloud is a six-month exercise; workloads where the per-token cost of Pro at 1M context outruns the value. Paiteq Pattern Default for GCP shops and multimodal-heavy workloads. Vertex's serverless deployment handles bursty multi-tenant load well, fewer surprises than self-managed inference at small scale. Gemini 3.0 Pro1M contextVertex Azure OpenAI Strengths Same GPT-5 family with Microsoft's enterprise procurement, data-residency choice, and Azure private endpoints. The compliance + procurement surface most enterprises pre-clear for. Fewer rate-limit headaches than direct OpenAI for high-volume accounts on committed-throughput tiers. When We Pick Microsoft-shop enterprise with Azure as the primary cloud; data residency contractually required; procurement timeline rules out a new vendor; existing Azure spend commitments unlock favourable rates. When We Don't Model versions you need haven't landed on Azure yet (latency between OpenAI launch and Azure availability still runs weeks); engineering team prefers OpenAI's faster ecosystem cadence. Paiteq Pattern Default for Microsoft-shop enterprises. Every azure openai integration we ship is paired with provider abstraction so the swap to direct OpenAI (or Anthropic) doesn't break the app contract. The azure openai integration also unlocks Microsoft's enterprise support tier when something goes sideways at 3am. GPT-5 on AzurePrivate endpointResidency AWS Bedrock Strengths Single API across Claude, Llama 4, Mistral, Cohere, Nova; AWS IAM + VPC + KMS native; PrivateLink endpoints; SageMaker integration for fine-tuned model deploy. The model-multiplexer Azure customers want and AWS finally ships. When We Pick AWS-shop enterprise; multi-model strategy across frontier + open-weights without a second procurement surface; data plane needs to live entirely inside AWS account boundaries. When We Don't Workload needs the absolute latest GPT-5 or Claude release the week it ships (Bedrock typically trails by days to weeks); cross-region deployment is the dominant constraint and Bedrock's regional model availability doesn't line up. Paiteq Pattern Default for AWS-shop enterprises and multi-model strategies. Bedrock's provisioned-throughput tier is the cleanest answer for the rate-limit anxiety that drives a lot of inbound ai integration services questions. Multi-modelPrivateLinkProvisioned Self-hosted vLLM Strengths Llama 4 70B / 405B, Mistral Small 4, Qwen 3, DeepSeek V3 on your own H100 cluster (or rented Modal / RunPod / Lambda). Predictable cost per million tokens (~$0.05-0.20 amortised on dedicated GPU), full data residency, fine-tune lifecycle you own. When We Pick Per-token cost dominates spend (>3M tokens/day); data sovereignty is a hard contractual constraint; you have a fine-tune that beats frontier on your eval set; latency tail needs predictable bounds. When We Don't Volume too low to amortise GPU rent; team can't run a model serving stack 24/7; workload genuinely needs frontier capability that open-weights can't match yet (most agent + voice + complex reasoning still benefit from hosted frontier). Paiteq Pattern Default behind the proxy as a cost-tier route, easy queries go to the self-hosted model, hard queries to hosted frontier. Cross-links: P3 LLM owns model build; P12 MLOps owns serving infra; P10 owns the integration seam between your app and the model. Llama 4vLLMSelf-host 004 / FIT ## Where each provider wins, workload × provider heat-grid. Eight production workloads across six providers. Three dots = default pick on quality + ecosystem; two = competitive; one = possible but not the first choice; zero = don't. This is the grid we run in the kickoff when a team asks "should we be on OpenAI or Claude", the answer almost always depends on which row you're optimising for, not which column you've already done procurement on. Workload Provider OpenAI Anthropic Vertex Azure OAI Bedrock Self-host Chat / assistant Function calling / tools Long-context Q&A (>200k) Structured extraction Vision / OCR Voice agent (realtime) Code generation Embedding / retrieval Chat / assistant OpenAIAnthropicVertexAzure OAIBedrockSelf-host Function calling / tools OpenAIAnthropicVertexAzure OAIBedrock Self-host Long-context Q&A (>200k) AnthropicVertexBedrock OpenAIAzure OAISelf-host Structured extraction OpenAIAnthropicVertexAzure OAIBedrockSelf-host Vision / OCR OpenAIAnthropicVertexAzure OAI BedrockSelf-host Voice agent (realtime) OpenAIAzure OAI AnthropicVertexSelf-host Code generation OpenAIAnthropicVertexAzure OAIBedrockSelf-host Embedding / retrieval OpenAIAnthropicVertexAzure OAIBedrockSelf-host Possible fit Good fit Primary vertical Cell ratings reflect 2026-05 production experience and shift with each model release. We re-score the grid quarterly; the live version is the one in the engagement kickoff deck. 005 / ABSTRACTION ## The provider abstraction layer, six things it owns. The single deliverable that separates a hopeful integration from one that survives the next provider outage. Whether it's LiteLLM behind a Cloudflare Worker, a Portkey managed gateway, or 600 lines of in-house Python, the abstraction layer owns six things, and the engagement isn't done until all six are wired. - 01 ### Uniform request contract Your app calls one shape. The gateway translates to OpenAI, Anthropic, Vertex, or Bedrock dialect. Adding a fifth provider doesn't ripple back into every microservice, the contract is the seam, not the SDK. Most in-house gateways land at 400-800 lines of TypeScript or Python; LiteLLM gives this for free with broad coverage. - 02 ### Fallback ladder Primary → secondary → degraded → human queue. Routing driven by rolling p95 latency, 429 / 5xx rate, and an explicit health probe, not blind retry. The drill is calendarised monthly. Without this, the first real outage is a 3am Slack thread; with it, the page-fix is the user not noticing. - 03 ### Observability seam Every call traced to Langfuse, Helicone, or LangSmith, request, response, tokens-in, tokens-out, latency, cost, prompt version. Production traces feed the eval set monthly. The integration that's invisible after launch is the one that quietly drifts on the next model release. - 04 ### Prompt-format normalisation System / user / assistant messages, tool-use schemas, JSON-mode constraints, multimodal blocks, each provider has its own dialect. The gateway normalises so application code stops caring. Swapping Claude for GPT-5 should be a config flip, not a refactor. - 05 ### Cost telemetry Per-tenant token spend in Postgres or Redis, exposed to product analytics and to the finance team's dashboard. Daily ceilings, weekly trends, anomaly alerts wired from week one. The cost story is a number the CFO can read on any Monday morning, not a surprise on the next invoice. - 06 ### Vendor swap drill A scripted exercise: kill the primary provider's key, watch the secondary take over, measure the latency hit, read the degraded UX. Once a month, on the calendar. Catches the staleness in the fallback config that nobody noticed for the last quarter. The drill is the cheapest insurance on the integration. 006 / LIMITS ## Rate-limit and cost engineering, the five controls that ship as code. The most common post-launch own-goal in ai feature integration is a runaway bill from a malformed prompt or a noisy tenant. The fix isn't a dashboard; it's five controls wired into the gateway before launch. Without them the post-mortem reads "we'll add rate-limits this sprint"; with them, the launch story is uneventful. - 01 ### Per-tenant quotas Daily and monthly token budgets per tenant, stored in Redis with TTL, checked before every request. When a tenant hits 80%, an alert fires to the account owner; at 100%, requests downgrade to the cheaper model or return a polite 429 the host app can handle. The contract reads cleaner than "we'll cap your usage", there's a number, in writing, enforced at the gateway. - 02 ### Daily cost ceiling Account-wide daily dollar cap with an alert at 80% and a hard cut at 100%. Worst case a runaway script burns one day's ceiling, not one month's. The ceiling is set in the contract, exposed in the admin UI, and the alert lands in Slack or PagerDuty, not buried in a CloudWatch tab nobody opens. - 03 ### Token budget per request No single request can exceed N tokens of completion. Protects against a malformed prompt that asks for "summarise this 80MB document" and runs the bill into four figures on one call. Easy to skip on day one; expensive to add after the first surprise invoice. - 04 ### Cache tiers (exact + semantic) Exact-match cache for identical requests (the same FAQ summarisation hits 50 times an hour); semantic cache via Redis + a small embedding model for near-duplicates. Typical hit rate lands somewhere in the 15-40% range depending on workload shape, that's a 15-40% straight cut in provider spend, paid back over the lifetime of the feature. - 05 ### Burst smoothing + back-off A leaky-bucket queue in front of the provider, when traffic spikes past your rate-limit, requests queue rather than 429. Exponential back-off with jitter on retries. Lets a marketing spike degrade gracefully into a longer tail of latency rather than a wall of failed requests. 007 / ROLLOUT ## Eval-gated feature rollout, shadow to 100%. Every ai feature integration ships behind a feature flag from day one. The flag stays in code for 90 days minimum post-launch, when a provider has an outage, the fallback ladder runs through the same flag-gated path. Removing the flag prematurely is the most common own-goal in this category; we keep it open longer than feels comfortable. WEEK 1–2 ### Shadow Wire the AI feature behind a flag. Production traffic runs both the legacy path and the new path; only the legacy result reaches the user. We log AI output, latency, cost, eval scores for two weeks. Catches regressions before any user sees them. WEEK 3–4 ### 1% canary Flip the flag for 1% of users, usually internal employees plus a small opt-in cohort. Two-week dwell. Per-call cost, latency p95, eval drift, user-feedback signal all watched against thresholds. Rollback is a flag flip; nobody redeploys at 2am. WEEK 5–6 ### 10% expansion Tenant + geography + plan-tier filters open up. Two-week dwell. Cost telemetry tightens: per-tenant ceiling, daily cap, alert if any tenant exceeds the budget envelope. This is where rate-limit reality bites, provider quotas, burst smoothing, retry-with-back-off all get exercised. WEEK 7+ ### 100% Flag is open to everyone. The flag itself stays in code for at least 90 days, when a provider has an outage, the fallback ladder runs through the same flag-gated path. Removing the flag prematurely is the most common own-goal in ai feature integration. Eval gates between stages: regression vs the prior stage on a 20-80 example task-specific eval set in Inspect AI or Promptfoo. A stage doesn't open until the eval score holds and the cost telemetry is inside the budget envelope. 008 / PICK ## Pick the integration pattern. Two or three questions usually narrow the pattern down to one. The tree below is the same one we run in the framing call, answer the constraint that matters most and the recommendation falls out. If two constraints tie (cost and residency, say), we'll walk both branches and price each in the kickoff memo. Click an answer to advance. The terminal is the pattern we'd default to, pricing and scope come in the kickoff. Path Question Pick one Result 009 / COMPARE ## Drop-in vs proxy vs sidecar vs forked deploy, side-by-side. Same four patterns from the carousel, rendered as a comparison grid for the procurement spreadsheet. Pull this into the kickoff memo verbatim if it's useful. ### Drop-in vs Proxy Drop-in Proxy Setup time 1–2 weeks 3–5 weeks Operational surface Provider SDK only Gateway + fallback + cache Vendor portability Low, direct binding High, swap at gateway A direct SDK binding means a provider deprecation or acquisition is a **rewrite event**, not a config change. The proxy's single outward contract lets you swap OpenAI for Anthropic, or add a self-hosted fallback, without touching the host application. Most teams don't feel this until month nine; the ones who wired a proxy at month one don't panic. Cost shape Per-token, ungated Per-token + ceiling at edge Ungated per-token billing is fine until a malformed prompt or a runaway background job hits the API at 3am. A gateway ceiling, daily spend cap per tenant, per-request token budget, burst smoothing, turns a cost-spike incident into a logged alert. The gating pays for itself on the first abuse event that would otherwise have tripled the monthly bill. Latency tail Provider's tail = yours +30–80ms gateway hop Data residency Provider's policy Policy + log control With a direct integration, every request-response pair is governed exclusively by the provider's data processing agreement. A gateway layer lets you **strip PII before the payload leaves your perimeter**, log only the fields you're permitted to retain, and route regulated tenants to an EU-region endpoint, all without changing the host application's contract. Break-even volume Any ~500k tokens/day The proxy's operational overhead, gateway infra, fallback drill, telemetry, only amortises above roughly 500k tokens per day. Below that threshold a drop-in integration ships faster and costs less to run. The right call is drop-in now with a proxy retrofit planned as a named line item when volume or vendor-risk conversations hit the board. Proxy wins as soon as you hit a second provider, a second tenant, or a second outage. ### Sidecar vs Forked deploy Sidecar Forked deploy Setup time 4–8 weeks 8–16 weeks Operational surface Sidecar service + own deploys Inference cluster + model lifecycle Vendor portability Medium, sidecar isolates provider Highest, own the model Cost shape Per-token + per-service budget Per-GPU-hour amortised Neither shape is cheaper by default, the crossover depends on volume profile. Sidecar per-token billing is cheaper at **bursty, irregular loads** because you pay only for what you use. A forked self-hosted model amortises its GPU-hour cost at **sustained high volume**, the savings show up once utilisation stays above ~60% across the run window. Latency tail +50–120ms service hop You set the tail with your hardware A hosted provider's p95 latency is outside your control, you inherit whatever tail the provider's fleet produces at peak load. A forked self-hosted model lets you **provision for your exact SLA**: dedicate the right GPU tier, tune the batching parameters, and own the p99. This matters most for synchronous, user-facing workloads where a 300ms tail is a UX problem. Data residency Sidecar policy + provider behind Fully inside your perimeter A sidecar still routes inference through a hosted provider, your sidecar controls the *application* perimeter, but the LLM call crosses the wire. A forked deploy means **no token leaves your VPC**: weights live on your infrastructure, inference runs on your hardware, and the data processing agreement is with yourself. This is the deciding factor for HIPAA, FedRAMP, and EU-AI-Act Article 28 workloads. Break-even volume ~1M tokens/day or eval-cadence pressure ~3M tokens/day on hosted frontier baseline Forked deploys reach cost parity with hosted frontier models only around 3M tokens/day on rented H100s, below that threshold the GPU-hour commitment exceeds per-token billing. Sidecar reaches its break-even earlier (~1M tokens/day) because the added cost is a service hop, not an inference cluster. Eval-cadence pressure is the non-volume trigger: if your team runs nightly eval suites at scale, the sidecar isolation pays off independently of token count. Forked deploy wins on residency, predictable cost, and latency tail, at the price of running an inference cluster. 010 / USE CASES ## Where ai integration services land in production. Six typical-shape engagements across SaaS, fintech, healthcare, ecommerce, and edtech. Function, segment, and deliverable shape are real engagement framings; the cards describe scope and shipped artefact rather than client-specific numbers. PROVIDER INTEGRATION B2B SaaS · enterprise tier ### openai integration services for a CRM-adjacent workflow Typical shape: existing product, one summarisation feature against meeting notes, request to ship a Claude fallback when the primary OpenAI integration hits 429. Deliverable: provider abstraction layer (LiteLLM behind Cloudflare Worker), eval set against domain-specific examples, fallback drill calendarised monthly. Deliverable: working drop-in plus proxy retrofit · eval harness · fallback drill ABSTRACTION LAYER Fintech · regulated DACH ### claude api integration with strict cost ceiling Typical shape: regulated lender adding a draft-decision-rationale feature to an existing underwriting workspace; per-tenant cost cap mandatory; provider must be EU-region; fallback to Azure OpenAI Sweden Central. Deliverable: gateway with per-tenant token budget, daily cost ceiling enforced at edge, Langfuse traces wired before launch. Deliverable: proxy layer · cost ceiling · per-tenant quotas · regulator-readable audit log VERTEX INTEGRATION Healthcare · multi-region ### vertex ai integration for whole-record clinician Q&A Typical shape: clinical workflow needs to answer questions over the full record, often >300k tokens of context. Gemini 3.0 Pro at 1M context handles the read; Claude Opus 4.7 as the secondary on Bedrock for the cases where Vertex's tail latency spikes. Deliverable: routing layer, eval set with named clinician-graded gold answers, drift alarms. Deliverable: multimodal retrieval · routing · clinician-graded eval set SELF-HOSTED RETROFIT DTC ecommerce · high-volume catalogue ### Llama 4 self-host behind a proxy for description generation Typical shape: bulk product-description generation runs millions of tokens nightly; hosted frontier cost runs into the high four-figures monthly. Retrofit: Llama 4 70B on vLLM (rented H100s, scaled down out of run-window), routed to from the existing proxy for the workloads where the eval holds. Deliverable: vLLM serving · proxy route · eval gate against frontier baseline ROLLOUT B2B SaaS · multi-tenant ### ai feature integration shipped through a four-stage rollout Typical shape: a draft-reply feature inside a customer-support inbox; risk tolerance for hallucination is low. Wired through shadow → 1% → 10% → 100% with eval gates at each stage and per-tenant cost ceiling enforced from canary. Rollback is a feature flag; nobody redeploys at 2am. Deliverable: flag-gated rollout · eval harness · drift watch wired before launch PROVIDER SWAP Edtech · single-feature ### Migration off ChatGPT integration to a multi-provider proxy Typical shape: original chatgpt integration was a direct OpenAI SDK call, a provider outage took the feature down twice in a quarter, the second outage drove the SLA conversation. Deliverable: Portkey proxy retrofit, secondary on Anthropic, fallback drill, no change to the host app's contract. Deliverable: proxy retrofit · multi-provider fallback · host contract preserved 011 / ENGAGE ## Four ways to start an ai integration services engagement. Fixed scope, fixed fee, written deliverable. We don't sell hours; we sell the integration seam. The four shapes below cover almost every inbound, Drop-in, Abstraction-Layer Retrofit, Sidecar Build, Provider-Migration Audit. Mixed engagements bill as two consecutive shapes, not an open retainer. 01 DROP-IN API INTEGRATION Fixed scope 1–2 weeks ### One feature, one provider, shipped in two weeks. In scope - 60-minute kickoff to lock the feature + provider - Direct SDK integration (OpenAI / Anthropic / Vertex / Azure / Bedrock) - Auth + secrets wired into the existing secret store - Eval set of 20–40 task-specific examples in Inspect AI or Promptfoo - Feature flag in the host app, rollout starts at 1% canary - Handover doc + 60-minute review session Out of scope - Abstraction layer / multi-provider (Shape 02) - Sidecar microservice (Shape 03) - Self-hosted model serving (route to P12 MLOps) 02 ABSTRACTION-LAYER RETROFIT Fixed scope 3–5 weeks ### Existing integration → multi-provider proxy with fallback + cost ceiling. In scope - Audit of the current integration shape + provider-binding surface - Gateway build (LiteLLM, Portkey, or in-house, pick at kickoff) - Fallback ladder + monthly drill schedule - Per-tenant quotas + daily cost ceiling enforced at edge - Langfuse / Helicone / LangSmith wired (pick at kickoff) - Cutover plan with rollback to the prior direct integration Out of scope - New AI feature build (Shape 01) - Sidecar architecture (Shape 03) - Production-ops platform at scale (route to P12 MLOps) 03 SIDECAR INTEGRATION BUILD Fixed scope 4–8 weeks ### AI feature as a standalone service alongside the host app. In scope - Sidecar service in Python or TypeScript on the host's existing runtime - Independent deploy + eval cadence - Own observability stack (Langfuse / Helicone) - Provider abstraction layer baked into the sidecar - Contract with the host app documented + versioned - Runbook for the host-app team to consume the sidecar Out of scope - Host-app changes beyond the API contract - Net-new agentic workflows (route to P1 Agent) 04 PROVIDER-MIGRATION AUDIT Fixed scope 2–4 weeks ### Decision memo + cutover plan for moving providers. In scope - Audit of the current provider integration + eval baseline - Candidate-provider longlist scored against the current eval set - Cost + latency + residency modelled for each candidate - Cutover sequencing with named rollback gates - Risk register across IP, data residency, behaviour drift - Procurement-ready recommendation memo Out of scope - The cutover itself (route to Shape 01 or 02 after the audit) - Ongoing retainer (separate engagement) 012 / STACK ## Vendors we integrate against in production. Frontier providers, gateway tooling, observability, and self-hosted serving, the surface a real ai integration company touches every week. - OpenAI - Anthropic - Google Vertex - Azure OpenAI - AWS Bedrock - LiteLLM - Portkey - Langfuse - Helicone - vLLM - Modal - Cloudflare Workers - OpenAI - Anthropic - Google Vertex - Azure OpenAI - AWS Bedrock - LiteLLM - Portkey - Langfuse - Helicone - vLLM - Modal - Cloudflare Workers 013 / WHY PAITEQ ## Why teams pick us as their ai integration company. - 01 ### Engineers ship the integration The partner who signs the scope is the engineer who writes the abstraction layer. No analyst-to-engineer ladder, no slide-deck-only deliverable. The handover is a pull request, not a presentation. - 02 ### No vendor kickbacks We don't take referral fees from OpenAI, Anthropic, Google, Microsoft, AWS, Portkey, LiteLLM, Langfuse, or any other vendor we recommend. The only money in our P&L is the engagement fee. Provider recommendations follow the eval, not the rebate sheet. - 03 ### Fixed scope, fixed fee, written deliverable One to eight weeks per engagement; no time-and-materials clock; no open-ended retainer. The integration is the artefact. The handover doc names the gateway, the providers, the eval set, and the fallback drill cadence. - 04 ### The fallback drill is calendarised Monthly outage simulation, every active integration. Most ai integration services teams ship the fallback config and never test it; ours runs the full ladder on a calendar invite so the staleness gets caught before a real outage finds it. - 05 ### Eval set ships with the integration Inspect AI or Promptfoo, 20-80 task-specific examples, versioned in your repo, gating every rollout flag. Without the eval the integration ages out the week after the next model release. With it, the regression catches itself in CI. - 06 ### Cross-cutting AI estate, not single-provider We integrate against every major provider in production every quarter, OpenAI, Anthropic, Vertex, Azure, Bedrock, and self-hosted on vLLM. The recommendation in each engagement comes from current-quarter experience, not last year's blog post. 014 / FAQ ## What buyers ask before signing an ai integration services contract. What's the difference between AI integration services and AI development services? Integration assumes you have a product already. There's an existing UI, an existing data model, an existing release train, an existing team, and the question is how to add an AI feature into that surface without re-architecting the product. The deliverables look different too: an integration engagement ships an **abstraction layer**, a **rate-limit posture**, a **fallback drill**, and an **eval-gated rollout**. A development engagement ships a new AI app from scratch, different shape, different scope, different risk profile. If you're building the AI product itself (the chat, the agent, the retrieval pipeline as the central UX), route to [LLM development services](/services/llm-development/) or [AI agent development company](/services/ai-agent-development/). If you have a SaaS product and you want to add Claude or GPT-5 features without re-platforming, that's the ai integration services shape and you're on the right page. OpenAI integration services or Claude API integration, which provider do you default to? It depends on the workload, not the brand preference. We default to **OpenAI** for function-calling-heavy agents, voice realtime workloads, and broadest tool-ecosystem needs, GPT-5 plus the Realtime API still leads on those shapes. We default to **Anthropic Claude** for long-context retrieval, contract-grade instruction-following, and tool-using agents where Sonnet 4.6's behaviour is more predictable across runs. In practice, most production integrations end up with both behind a proxy. Easy traffic routes to Haiku 4.5 or GPT-5 mini; hard traffic to Opus 4.7 or GPT-5; fallback ladder configured against 429 and 5xx. Single-provider integrations are fine for two-week MVPs; they age badly once the first outage hits. How long does a typical ai integration services engagement take? The four shapes we ship cover most of the inbound. **Drop-in API integration** runs 1-2 weeks, single feature, single provider, single tenant. **Provider abstraction layer build** runs 3-5 weeks, gateway, fallback ladder, cost telemetry, observability wired before launch. **Sidecar service build** runs 4-8 weeks, separate service for the AI feature with its own deploy and eval cadence. **Forked fine-tune deploy** runs 8-16 weeks because it pulls in P3 LLM (model build) and P12 MLOps (serving infrastructure) as adjacent practices. Every engagement starts with a 60-minute scoping call. If the surface looks too narrow or too broad to map cleanly to one shape, we say so before any contract gets signed. Do you build a provider abstraction layer in-house or use LiteLLM / Portkey? Depends on three signals: tenant count, regulatory posture, and how much of the contract is provider-specific. For small-to-mid SaaS teams under 50 tenants with relaxed compliance needs, **LiteLLM** behind a Cloudflare Worker is usually the right call, Apache 2.0, broad provider coverage, low operational surface. For multi-tenant SaaS with quotas, observability requirements, and a compliance team in the loop, **Portkey**'s managed gateway earns its fee. For regulated enterprises where the gateway itself needs to live inside a VPC with audit-logged config changes, we build an in-house thin gateway in TypeScript or Python, usually 400-800 lines of code that owns the contract, the fallback ladder, and the cost telemetry. None of these are "the right answer" globally. The wrong shape is the one that doesn't fit your compliance + ops surface. What about rate-limit and cost engineering, what does that actually mean? It means the cost ceiling and the rate-limit live as code. Concretely: **per-tenant token budgets** stored in Postgres or Redis with TTL, checked before every request; **daily cost ceiling per tenant** with an alert at 80% and a hard cut at 100%; **per-request token budget** (e.g. "no single request can exceed 8k tokens of completion") to prevent runaway costs from a malformed prompt; **exponential back-off** with jitter on 429 and 5xx; **cache tier**, exact-match cache for repeated identical requests, semantic cache (Redis + a small embedding model) for near-duplicates; **burst smoothing** via a leaky-bucket queue when provider rate-limits would otherwise drop traffic. Without these, the post-launch story usually goes: the feature ships, a customer abuses it accidentally, the bill triples, the team panics, the feature gets pulled until the rate-limit work that should've been week 1 finally happens. Better to do it before launch. Can you integrate AI into a product without changing the existing tech stack? Mostly yes. The integration surface is usually three places: an **auth-and-secrets seam** (provider API keys live in your existing secret manager, Vault, AWS Secrets Manager, GCP Secret Manager, Doppler, not in a new system), a **network egress seam** (calls to the provider's endpoint go through your existing egress controls), and a **data-plane seam** (request and response payloads pass through your existing logging / audit pipeline). All three commonly slot into the existing stack, Node, Python, Go, Ruby, Java, .NET, using the provider's SDK or a thin HTTP client. What does sometimes change: observability gets a new tier (Langfuse or Helicone next to the existing APM), the gateway adds a hop (Workers / FastAPI service / Portkey), and the eval suite is genuinely new tooling (Inspect AI, Promptfoo, RAGAS) because most existing test infrastructure doesn't grade LLM output. How do you handle provider outages without breaking the product? A fallback ladder, wired before launch, drilled monthly. The shape: primary provider (e.g. OpenAI GPT-5) → secondary provider (e.g. Claude Sonnet 4.6) → degraded mode (e.g. a deterministic non-LLM path that returns a "this feature is temporarily reduced" UX) → human queue (e.g. the request lands in a support inbox). The gateway routes based on health checks (rolling latency p95, 429 / 5xx rate, explicit health endpoint), not blind retry, blind retry on a degraded provider just stacks the same failure twice. The drill: a calendarised monthly exercise where we simulate the primary provider being down (kill the key, blackhole the endpoint, set the gateway health check to fail), watch the fallback chain run through every hop, measure the latency hit, and read the user-facing UX in degraded mode. Most production-grade ai integration services engagements ship this; most that don't, hit their first real outage as a Slack-channel-3am-fire-drill. Where does ai integration services overlap with workflow automation, MLOps, or AI agent development? Integration is the seam between your existing product and an AI model. [AI automation agency](/services/ai-workflow-automation/) is the seam between your existing tools (CRM, ERP, ticketing, data warehouse) and event-driven orchestration with LLM-in-the-loop, different shape, different deliverable. [MLOps services](/services/mlops/) is the production-ops layer (model serving, drift detection, feature stores, observability platform) that the integration plugs into at scale. [AI agent development company](/services/ai-agent-development/) is when the AI is the product, not a feature inside another product, multi-step tool-using agent with planner / executor / verifier separation. Most real engagements straddle two of these. We're explicit about the seam: P10 owns "we have a SaaS product, we want to add AI features"; P3 owns "we're building the AI app from scratch"; P12 owns "the AI feature is in production, monitor and operate it." When an engagement spans, we name the seams in writing before scope gets locked. Do you ship the eval set as part of the integration, or is that separate? Part of the integration, always. An ai feature integration without an eval set is a one-shot demo that ages out the week after a provider releases a new minor version and the behaviour drifts. The eval set lives in **Inspect AI** or **Promptfoo**; it gets versioned in your repo; it runs in CI on every model upgrade; it gates the rollout flag flips at 1% → 10% → 100%. We ship a starting set of 20-80 examples graded against the actual task, task-specific, not generic, and a process for adding to it from production traces monthly. Without the eval set, the failure mode is silent regression: the model changes, the integration still returns 200s, and a user-facing quality bug shows up three weeks later in a support ticket. The eval set is the cheapest insurance you can buy on an integration. FURTHER READING ## Where this practice connects. An integration brief usually sits next to either an [LLM development engagement](/services/llm-development/) (when the host SaaS needs a custom model layer alongside the integration) or a [production agent build](/services/ai-agent-development/) (when the integration is the substrate for a tool-calling agent). If the surface is a conversational widget rather than an autonomous workflow, the right route is [chatbot development services](/services/chatbot-development/) with the gateway integration sitting underneath. For [AI for SaaS companies](/ai-for-saas/), the integration seam is often the only safe place to add LLM capability without re-platforming. When the integration sits across a legacy estate, route through [AI migration services](/services/ai-migration/) first; the modernization sequencing dictates what integration is even possible. The mobile-app variant of this work flows through the same team that maintains [GetWidget](https://www.getwidget.dev/) (the open-source Flutter UI library, 4,800+ stars on the [github repo](https://github.com/ionicfirebaseapp/getwidget), 2026-Q2), so the SDK + UI + provider abstraction sit in one bench. 015 / Related practices ## Adjacent services. [ LLM DEVELOPMENT LLM Development Custom LLM apps — RAG, fine-tuning, evaluation, deployment. ](/services/llm-development/)[ AI AGENT DEVELOPMENT AI Agent Development Autonomous, tool-using AI agents for production workloads. ](/services/ai-agent-development/)[ AI CONSULTING AI Consulting AI strategy, audits, roadmap. ](/services/ai-consulting/) 016 / Scope an integration ## Ship the integration seam that *survives* the second outage. Drop-in API integration in 1–2 weeks. Abstraction-layer retrofit in 3–5. Sidecar integration build in 4–8. Provider-migration audit in 2–4. [Scope an integration](/contact/?topic=ai-integration) [See integration patterns](#patterns)