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Podcast · English · new episodes most days
AI Dev Tools: The Crazyrouter Podcast
Short, practical episodes for developers who run AI APIs in production: retries and deadlines, streaming, cost control, tool calling and routing across models. Every episode below has written show notes you can skim before or after listening.
Reliability and failure handling
Retries, deadlines, backpressure, health checks and failover for production AI traffic.
- EP584 · Oct 19, 2026 · 11:55Backpressure for AI Gateways - Admit Only Work You Can FinishA gateway can have healthy servers and working providers yet still deliver a terrible experience because every request waits behind too much other work. This episode explains backpressure as a clear admission contract: bounded waiting, truthful capacity accounting, streaming flow control, fairness across tenants, and overload signals that reach callers before resources run out.
- EP582 · Oct 17, 2026 · 8:59Deadline Propagation for AI Gateways - Make Every Timeout Share One ClockIndependent timeouts on retrieval, model calls, and fallbacks can keep a backend generating long after the user has given up. This episode explains the difference between timeouts and deadlines and shows how to carry one parent deadline through admission, queues, retries, streaming, and cancellation.
- EP581 · Oct 16, 2026 · 9:46Retry Budgets for AI Gateways - Recover Without Amplifying FailureWhen the application, its SDK, and the gateway each retry independently, one slow upstream can turn into multiplied traffic, late answers, and duplicated charges. This episode reframes retries as a shared resource-allocation policy with three budgets: an end-to-end deadline, an attempt cap with a single owner, and protection against repeated side effects.
- EP311 · Aug 30, 2026 · 8:03AI API Gateway Failure Domains - Make Fallbacks Truly IndependentTwo AI routes can carry different provider names yet share a cloud region, account quota, identity service or gateway adapter, so failing over between them changes labels without changing risk. This episode explains failure-domain-aware routing: mapping dependencies, defining required independence per request, and proving that a fallback really avoids what broke.
- EP285 · Aug 29, 2026 · 5:53AI API Gateway Rate Limit Headers - Make Capacity Signals ActionableA bare 429 tells a client almost nothing, and AI requests vary too much in tokens, latency and cost for guesswork retries. This episode explains how an AI API gateway can turn rate limit responses into precise capacity signals: which resource was exhausted, at what scope, when retrying makes sense, and how to test the headers as a real API contract.
- EP200 · Aug 27, 2026 · 8:30AI API Gateway Health Probes — Test Readiness Before You Route Production TrafficA provider can answer a ping and still fail on streaming, structured output or your deadline. This episode explains how AI API gateways can use small, deliberate health probes to decide whether a specific provider, model, region and adapter is ready for a specific workload before routing real users to it.
- EP181 · Oct 8, 2026 · 8:00AI API Error Taxonomy — Make Failures ActionableA bare 500 or 429 leaves clients guessing whether to retry, change the request, or wait for a job that may still be running. This episode lays out an error taxonomy for AI API gateways: separate transport from operation failures, return stable codes classified by the next safe action, keep the full error chain internally, and test streaming and async boundaries as part of the contract.
- EP116 · Aug 4, 2026 · 4:59AI API SLOs and Error Budgets — Reliability You Can Actually OperateMost teams want high reliability, but few define what reliability means for a streamed model response, a tool call, a background generation job or a degraded fallback. This episode explains how to design service-level indicators around user outcomes, set objectives per workload, use error budgets and burn-rate alerts, and keep reliability honest about fallbacks, cost and policy.
- EP114 · Aug 2, 2026 · 5:20AI API Monitoring and Observability — Find the Failure Before Users DoA green uptime check does not mean an AI product is healthy: an endpoint can return HTTP 200 while latency doubles, output quality drops or tool calls fail. This episode lays out a practical observability approach for AI API applications, covering end-to-end tracing, streaming and quality metrics, cost per successful task, actionable alerts and safe telemetry.
- EP7 · Mar 28, 2026 · 5:07AI API Rate Limits — What Every Developer Needs to KnowRate limits are the reason an AI app that works perfectly in development starts throwing 429 errors the moment real traffic arrives. This episode explains what providers actually limit, why launches are the most common breaking point, and five practical strategies for staying online, from exponential backoff to spreading load across several model providers.
Cost, budgets and quotas
Attribute spend, set budgets and share capacity without surprise bills.
- EP338 · Aug 31, 2026 · 8:13AI API Gateway Tokenizer Governance - Keep Counting, Limits, and Billing AlignedThe same text can produce different token counts across model families, tokenizer revisions, and chat templates, so a gateway that counts with the wrong assumptions accepts requests that fail and rejects ones that fit. This episode explains how to make every token estimate reproducible and keep admission, routing, truncation, and billing aligned when tokenizers change.
- EP185 · Oct 18, 2026 · 8:54AI API Quotas — Separate Fairness From Mere Rate LimitsRate limits govern how fast traffic arrives; quotas govern how much of a scarce resource a customer, team, workflow or model may consume over time. This episode explains how to build quotas that are fair and measurable: name the real resource, keep quota, rate limit and concurrency separate, reserve atomically, bound bursts, isolate noisy neighbors and roll policy changes out like an API contract.
- EP180 · Oct 7, 2026 · 8:14AI API Budgets — Put Spend Controls Where They MatterA spend dashboard tells you what already happened; a budget control decides what may happen next. This episode covers how to scope budgets, estimate and reserve cost before work starts, settle against a durable ledger, account for retries and fallbacks, and return failures that developers can act on.
- EP115 · Aug 3, 2026 · 5:14AI API Cost Attribution — Know What Every Feature Actually CostsA monthly provider bill shows how much money left the account, not which feature created the cost, which customer got value, or which route wasted tokens on retries. This episode explains how to build cost attribution for AI API products, from request identity and workflow-level cost to price history, budgets, leakage detection and routing decisions.
- EP13 · Apr 3, 2026 · 5:45Self-Hosting vs API Gateway — The Real Cost of Running Your Own AI ModelsSelf-hosting an open-source model promises zero per-token cost and full control, but the hardware, engineering time and limited model choice add up quickly. This episode lays out the situations where self-hosting is the right call, why most teams are better served by an API gateway, how a hybrid setup combines both, and a four-question framework for deciding.
Streaming and latency
Keep token streams fast, recoverable and fair to slow clients.
- EP307 · Aug 30, 2026 · 8:17AI API Gateway Stream Recovery - Reconnect Without Duplicating WorkStreaming turns a single AI request into a long-lived operation that can break halfway, reconnect through another worker, or finish while nobody is listening. This episode describes a recovery contract for an AI API gateway that replays output safely, never runs a tool twice, never double-charges, and is honest when the outcome is unknown.
- EP306 · Aug 30, 2026 · 8:28AI API Gateway Session Affinity - Keep Stateful Work on the Right RouteAI requests often look stateless over HTTP while depending on conversation caches, provider sessions or regional boundaries underneath. This episode covers how an AI API gateway can keep follow-up requests on the right worker, region or provider session, and how to define migration, failover and cleanup so continuity never turns into a security or correctness problem.
- EP177 · Oct 4, 2026 · 7:19AI API Streaming Backpressure — Keep Tokens Flowing Without Melting ClientsA model can produce tokens faster than a browser, mobile connection, proxy or parser can read them, and unbounded buffers turn that mismatch into memory growth and wasted generation. This episode treats streaming as a resource contract: bound every buffer, honor transport flow control, propagate cancellation in both directions, and measure delivery lag as well as model latency.
- EP176 · Oct 3, 2026 · 8:10AI API Tail Latency — Reduce Slow Requests Without Doubling SpendAverages can look healthy while a small share of AI API requests waits far longer than the median. This episode explains how to attack that tail with an end-to-end deadline first, evidence second, and bounded hedging only where duplicate work is safe. It also covers cancellation, attempt budgets, retry coordination and the failure cases worth testing.
- EP16 · Apr 6, 2026 · 7:59Streaming vs Non-Streaming API Calls — Performance Deep DiveStreaming and non-streaming LLM calls take about the same total time and cost the same per token, but they feel very different to users and behave differently in production. This episode explains time to first token and perceived latency, when streaming is the right choice, when a single complete response is simpler and safer, and practical tips for timeouts, errors and hybrid setups.
Agents, tools and integrations
Tool calling, function calling, agent protocols, output contracts and base URL setup.
- EP340 · Aug 31, 2026 · 9:11AI API Gateway Structured Output Contracts - Evolve Schemas Without Breaking ClientsStructured output is more than asking a model for JSON; it is a contract covering shape, types, nullability, semantics, errors, and versioning. This episode explains how a gateway can register and version schemas, route only to providers that can honor them, and evolve contracts without breaking clients.
- EP77 · Jun 4, 2026 · 9:56Base URL Bugs Are Developer Experience BugsMany failed AI API requests have nothing to do with the model, the prompt or the provider: the Base URL is wrong. This episode explains what a Base URL is, the most common ways it gets misconfigured, why the resulting errors are misleading, and how teams can turn repeated support questions into guides and tools that prevent the problem.
- EP50 · May 2, 2026 · 6:07The AI Agent Protocol Wars — MCP vs A2A and Why Developers Need a Strategy NowMCP and Google's Agent-to-Agent protocol are often framed as rivals, but they solve different problems: MCP connects one AI application to tools and data, while A2A lets multiple agents discover each other and collaborate. This episode explains each protocol, how they stack together, and a practical strategy for building agent systems today.
- EP19 · Apr 8, 2026 · 6:49Function Calling Across Different AI ModelsEvery major model supports function calling, but OpenAI, Anthropic, Google and DeepSeek implement it differently, and those differences surface as bugs in production. This episode compares their request and response formats, reliability and parallel-calling behavior, explains why a normalizing gateway avoids four separate integration layers, and lists five edge cases to test before choosing a model.
- EP12 · Apr 2, 2026 · 7:21Building AI Agents with Tool Calling — A Practical GuideTool calling is what turns a chatbot into an agent: the model decides which function to call and with what arguments, your code runs it, and the result feeds back into the conversation. This episode explains the think-act-observe loop, how OpenAI, Anthropic and Google approach tool calling, how to build a first agent in four steps, and four lessons for running agents in production.
Models, context and routing
Choosing models, long context, embeddings, caching and token-aware routing.
- EP317 · Aug 30, 2026 · 8:37AI API Gateway Context Compaction - Preserve Meaning Under Token LimitsTrimming a long conversation to fit a model window can silently drop the constraint, correction, or safety rule that made the request correct. This episode treats context compaction as a correctness and policy boundary, not a token optimization, and lays out how a gateway can shrink prompts while preserving what the model is authorized and expected to know.
- EP310 · Aug 30, 2026 · 8:02AI API Gateway Token-Aware Load Balancing - Distribute Work, Not Request CountsRequest-count load balancing assumes every request costs about the same, which is false for AI workloads where one call may be tiny and the next carry a huge context, images and a long stream. This episode explains how an AI API gateway can estimate real work, track committed load per route, and route by predicted completion while keeping policy, fairness and billing explainable.
- EP237 · Aug 28, 2026 · 10:20AI API Gateway Semantic Caching - Reuse Results Without Reusing RiskSemantic caching can cut latency and provider cost, but a cache hit is a routing, policy, freshness and billing decision, not just a lookup. This episode lays out the contract a gateway needs before it reuses an earlier model answer: what is cacheable, what belongs in the key, how to isolate tenants, and how to fail safely.
- EP41 · Apr 27, 2026 · 7:47The 2 Million Token Context Window Era — What It Actually Changes for DevelopersTwo-million-token context windows let many applications skip the retrieval, chunking and summarization infrastructure that teams spent months building. This episode covers what you can stop building, how long-context cost compares with running a RAG pipeline, the latency and attention gotchas, and four patterns that work well in production.
- EP23 · Apr 12, 2026 · 5:33Embedding Models Compared — Which One Should You Actually Use?Embedding models power search, recommendations and RAG, and there are more viable options than ever. This episode compares five families developers use in production, explains the four factors that actually decide the right choice for your app, and argues that chunking strategy often matters more than which embedding model you pick.
The full archive
This page collects episodes with written show notes. The complete back catalogue is on Apple Podcasts, Spotify and the RSS feed.