Resources
Insights on AI security, cost optimization, and building secure AI infrastructure.
Your team uses Claude Enterprise daily, but can you prove what personal data they shared? Shim scans Claude Enterprise usage for PII and gives you the audit trail, without ever storing your content.
OpenAI's compliance logs vanish after 30 days. If you can't prove what PII your team shared in ChatGPT Enterprise within that window, the audit trail is lost. Here's how Shim captures it continuously.
Learn how to implement PII redaction in ChatGPT and OpenAI APIs. Protect customer data, ensure GDPR/HIPAA compliance, and prevent data breaches.
Full OpenAI API pricing for every GPT-5.5, GPT-5.4, and multimodal model in 2026. Per-token costs, cached input discounts, Batch API savings, and how to cut your bill further.
Complete claude api pricing breakdown for every current model. Side-by-side costs for Opus, Sonnet, and Haiku plus prompt caching, batch, and fast mode multipliers.
Build a practical AI governance framework for your engineering org. Covers model selection policies, PII controls, audit trails, and gateway enforcement.
Map the AI security threat landscape from prompt injection to data exfiltration. Learn which controls work at the gateway, model, and application layer.
Shadow AI is already inside your organization. Learn how to detect unauthorized AI usage, build a governed gateway, and enforce PII controls without killing productivity.
LiteLLM has faced a supply chain attack and multiple RCE exploits in 2026. Here's what happened, the risks of self-hosted AI gateways, and safer alternatives.
Compare Anthropic API pricing against OpenAI across every model tier. Real production cost breakdowns for Claude and GPT, plus how to cut spend with caching and multi-provider routing.
Compare 10 AI governance tools across policy enforcement, audit logging, PII controls, and model inventory. Find the right platform for your compliance stack.
AI tokens are the units LLMs use to process text. Learn how tokenization works, how to count tokens with tiktoken, and how to reduce token costs.
Security-focused comparison of LiteLLM alternatives after the 2026 supply chain attack. Portkey, Kong, Cloudflare, Helicone evaluated for governance.
The LiteLLM PyPI supply chain attack compromised versions 1.82.7 and 1.82.8 with a three-stage credential stealer. Full timeline, technical analysis, and how to protect your AI stack.
Build a production rag pipeline from chunking through generation. Covers embedding model selection, vector databases, hybrid retrieval, reranking, and securing the generation layer.
A practical AI risk management framework for engineering teams. Map production AI risks to NIST AI RMF functions and enforce controls at the gateway level.
How much energy does a single LLM API call consume? Data from Epoch AI, the IEA, and peer-reviewed research breaks down the real numbers by model size.
Green AI starts at the API layer. Model selection, semantic caching, and smart routing cut AI energy use by orders of magnitude without infrastructure changes.
AI compliance in 2026 means logging, PII redaction, and audit trails built into your call layer. Cover the EU AI Act, US state laws, GDPR, and gateway enforcement.
Prompt caching works differently across OpenAI, Anthropic, and Google. Compare activation methods, TTLs, pricing, and token minimums to optimize costs across providers.
AI guardrails are policies, technical controls, and monitoring that keep LLMs safe in production. Learn the types, where they run, and how to implement them.
LLMs can't unlearn data, treat all input as one token stream, and leak through integrations. Learn where AI privacy breaks down and how to enforce it at the infrastructure layer.
LLMOps manages production LLM systems you don't control. Learn how it differs from MLOps across cost, monitoring, versioning, and deployment.
The NIST AI framework maps directly to infrastructure you already run. Learn how its four functions translate to gateway controls, monitoring, and access policies.
LLM observability goes beyond uptime dashboards. Learn what to monitor in your AI pipeline, how tracing and evals work, and where an AI gateway fits in.
OpenAI rate limits use RPM, TPM, and tier-based caps to throttle API access. Learn how the system works, what triggers 429 errors, and how to handle them.
Sustainable AI starts with software decisions: model selection, semantic caching, and workload routing cut emissions, cost, and latency at the same time.
LLM security requires pipeline-level controls, not model-layer patches. Learn how prompt injection, data exfiltration, and supply chain risks map to defense-in-depth strategies.
Semantic caching matches LLM queries by meaning, not string. Learn how the four-step pipeline works, how to tune similarity thresholds, and why cache misses are invisible failures.
LLM guardrails are application-level controls that govern what your model can see, say, and do. Learn input, output, and gateway-level patterns for production AI.
AI data governance for LLM apps requires runtime enforcement, not static policies. Learn how to build a compliant data stack with PII controls, audit trails, and gateway-level protection.
EU AI Act compliance mapped to engineering controls. Risk classification, audit logging, human oversight, and transparency requirements developers must build before August 2026.
Reference architecture for enterprise LLM deployment covering multi-tenant isolation, GPU sizing, compliance controls, cost allocation, and the gateway layer that ties production AI together.
MCP security risks are structural, not incidental. Learn how confused deputy attacks, token theft, prompt injection, and supply chain threats target MCP servers.
Envoy AI Gateway routes LLM traffic across 16 providers on Kubernetes. Learn what its v1.0 ships, what it lacks, and who should use it.
Gemini API pricing for every model tier and service mode in 2026. Compare Standard, Batch, Flex, and Priority costs, plus caching, billing tiers, and add-on fees.
Three AI agents deleted three production databases. System prompts failed every time. Learn why ai agent security requires application-layer guardrails, not model instructions.
The LiteLLM supply chain attack exposed why every open source AI gateway is a high-value target. How the backdoor worked, what got stolen, and what it means for gateway security.
AI FinOps moves cost control into the request path. Learn how engineering teams set per-feature budgets, catch runaway agents, and cut LLM bills 40-85%.
OpenRouter vs LiteLLM compared on routing, pricing, data privacy, and operational overhead. Which multi-model gateway fits your team's compliance and infrastructure needs?
Databricks AI Gateway routes across model providers but locks you into Unity Catalog, DBU billing, and Delta table logging. Here is what that means for mixed architectures.
AI agent observability tracks full workflow traces, not just individual LLM calls. Learn why multi-step agents need per-trace cost, latency, and error monitoring.
Compare OpenAI and Anthropic compliance APIs side by side: coverage, retention, key models, and gaps. Learn how to monitor LLM usage across both providers.
AI agent guardrails intercept at two points: pre-LLM and post-LLM. Learn the patterns for PII protection, self-correction loops, and centralized enforcement in production agents.
LLM budget limits at the org level don't stop one rogue key from draining your account. Learn how to enforce per-key spend caps across LiteLLM, Portkey, MLflow, and agentgateway.
An MCP gateway centralizes how AI agents connect to MCP servers. Learn what it does, how it differs from API and LLM gateways, the three product categories, and when your team needs one.
An MCP proxy handles transport and protocol routing. An MCP gateway enforces identity, consent, and authorization. Here is where the line falls in production.
Unity AI Gateway extends Databricks Unity Catalog to govern models, agents, and MCP services. A deep look at what it solves and where platform lock-in starts.
EU data residency for LLM APIs requires more than a regional endpoint. Learn how to route GPT-4, Claude, and Gemini traffic through EU infrastructure and stay GDPR compliant.
A single fallback model is not an LLM routing strategy. Learn how retries, fallback chains, circuit breakers, hedged requests, and smart routing each solve a different failure mode.
AWS MCP Gateway (AgentCore) connects Bedrock agents to AWS services via MCP. Learn the architecture, service quotas, fixed limits, and when self-managed alternatives fit better.
DeepSeek API pricing for V4 Flash and V4 Pro in 2026, with per-token cost tables compared to OpenAI GPT-5.5, GPT-5.4, and GPT-5.4 mini.
Grok API pricing for every current model, including cached input rates, tool invocation fees, batch discounts, and the hidden cost drivers in agentic workflows.
Bankacılıkta yapay zeka ajanlarının güvenlik risklerini, prompt injection ve MCP açıklarını, koruma katmanlarını ve ağ geçidi katmanı zorunluluğunu inceleyin.
LiteLLM's Rust migration cuts memory 11x and latency 150x. Config, database, and API stay the same. Here is the timeline and what gateway users need to know.
KVKK kapsamında LLM trafiğinde PII redaksiyon: maskeleme ile anonimleştirme farkı, sınır ötesi veri aktarımı riskleri ve gateway katmanında çözüm mimarisi.
An LLM gateway for Kubernetes needs two layers: a control plane for secrets and budgets, and an inference plane for GPU-aware routing. Here is how to design the split.
Fintech ekipleri için LLM maliyet optimizasyonu: model yönlendirme, önbellekleme, toplu işleme ve bütçe limitleriyle yapay zeka harcamalarını kontrol altına alın.
Compare seven Langfuse alternatives on self-hosting, agent tracing, evaluation depth, and cost. Find the right fit for teams outgrowing Langfuse in 2026.
Türk bankalarında ve fintech şirketlerinde LLM güvenlik riskleri: KVKK ve BDDK yükümlülükleri, prompt injection tehditleri, PII redaksiyon ve teknik önlemler.
BDDK ve KVKK uyumlu yapay zeka gateway ile Türk bankalarında LLM güvenliği: PII maskeleme, denetim izi ve prompt filtreleme tek mimaride.
The OWASP LLM Top 10 for 2025 maps real attack patterns to gateway-level controls. Learn which risks matter for your architecture and how to test for them.
Bifrost AI gateway benchmarks show 9.5x throughput over LiteLLM, but raw speed misses the point. Here is where the real security gaps in LLM gateways show up.
Palo Alto Networks is acquiring Portkey. Evaluate LiteLLM and Shim as portkey alternatives based on your actual need: routing, observability, or data privacy.
Claude rate limits span two separate systems: API tiers (RPM/TPM by spend) and Code session caps. Every tier's numbers, 429 handling, and how to stretch throughput.
Portkey vs LiteLLM compared after the $700M Palo Alto acquisition. Performance, cost, compliance, and what changes now that Portkey is a security product.
Indirect prompt injection exploits RAG pipelines and agent tools through trusted data, not user input. Learn why it resists input-layer fixes and what defenses actually work.
12 prompt injection examples you can run as test cases against your own LLM stack, covering direct chat attacks and the indirect pipeline exploits most teams miss.
Gemini rate limits span three dimensions plus a spend-based layer most teams miss. Here is how RPM, TPM, RPD, and per-tier spend caps actually work.
Load balancing LLM traffic isn't replica load balancing. Learn the failure taxonomy, circuit breakers, traffic distribution strategies, and format normalization production systems need.
LLM API keys get stolen through GitHub commits, misconfigured clouds, and exposed pipelines. Learn why billing alerts fail and how a gateway fixes llm api key management.
Darktrace found a LiteLLM gateway on Amazon Bedrock hijacked for cryptomining. What the incident reveals about ai gateway security blind spots and how to harden yours.
An AI firewall, AI gateway, and AI Control Platform are three distinct layers, not three names for the same thing. Here is what each one does and where it goes blind.
Mistral API pricing for every model in 2026. Full price table, Le Chat plans, free tier details, and cost comparison against GPT-5.4 and Claude Sonnet 4.6.
The GPT-5 API has shipped six model versions since August 2025. Pricing tiers, rate limits, sub-variants, and mid-stream safeguards make a gateway layer essential.
AI agent testing requires layered evals, shadow mode, and continuous monitoring. Learn practical approaches to component, trajectory, and security testing before production.
AI TRiSM is Gartner's framework for enforcing AI trust, risk, and security at runtime. Learn what the four mandatory capabilities mean for your LLM infrastructure.
AI red teaming for LLM apps requires testing the full stack: model, retrieval pipeline, tools, and gateway layer. Learn structured methods, team composition, and tooling.
An AI control plane governs routing, budgets, and security across multi-provider LLM deployments. Learn the architecture and why your gateway layer is where control lives.
Unit tests break for LLM applications. Learn the methods, metrics, and architecture that make llm testing work: datasets, LLM judges, traces, and runtime enforcement.
OpenClaw alternatives compared by how they handle PII redaction. IronClaw, ZeptoClaw, Hermes Agent, and gateway-layer options for compliant LLM deployments.
Kong AI Gateway extends Kong's API platform with LLM routing, semantic caching, and MCP governance. But its per-service pricing punishes AI-native teams experimenting with models.
OpenClaw security fails at three levels: a CVSS 8.8 WebSocket RCE, poisoned skills on ClawHub, and fake installers exploiting search. Here is how each one works.
Most LLM logs capture HTTP metadata, missing what auditors examine. Learn what a compliance-grade audit trail requires for FINRA, GDPR, and HIPAA.
Snowflake Cortex AI Gateway governs agent actions across MCP servers, not LLM API traffic. Compare it to LiteLLM, Portkey, and Kong to decide which architecture fits your stack.
The OWASP Top 10 for Agentic Applications maps ten risks that mostly cannot be fixed with prompt engineering. Here is how ASI01-ASI10 map to infrastructure-layer controls.
AI agents need their own authentication model. Why API keys fail, how OAuth 2.1 and short-lived tokens fix it, and why the gateway is the right enforcement point for AI agent authentication.
Route self-hosted vLLM models through a production AI gateway. Compare Kubernetes Inference Extension, LiteLLM, Kong, and ngrok paths for auth, caching, and PII protection.
Agentic AI governance requires identity, consent, and runtime enforcement. Prompt-level controls cannot bind autonomous agents that authenticate and act faster than humans review.