2023 in Review: AI-Driven Infrastructure and the Rise of Platform Engineering

As 2023 draws to a close, it’s been a transformative year for infrastructure and platform engineering. From the explosion of AI workloads to the maturation of technologies like eBPF and Rust, the landscape has shifted dramatically. This post reflects on the major trends, challenges, and learnings from the year.

The AI Infrastructure Boom

2023 will be remembered as the year AI moved from research to production at scale. The infrastructure requirements for AI workloads have driven innovation across the stack.

Key Developments

Vector Databases Went Mainstream

Moved from niche to critical infrastructure
Pinecone, Weaviate, Qdrant saw massive adoption
HNSW and IVF algorithms became table stakes
Hybrid search (vector + keyword) emerged as best practice

GPU Shortages and Optimization

NVIDIA H100 GPUs became harder to acquire than unicorns
Led to creative optimization strategies
Quantization, distillation, and model compression became essential
Alternative accelerators (Cerebras, Graphcore) gained traction

Embedding Models Evolved

From BERT to specialized models (E5, instructor)
Multilingual and multimodal embeddings matured
Dimension reduction without accuracy loss improved

What We Learned

The infrastructure for AI is fundamentally different from traditional web workloads:

Stateful by Nature: Vector databases require careful sharding and replication
Memory-Bound: High-dimensional vectors stress memory bandwidth
Cost-Intensive: GPU and storage costs dwarf traditional compute
Latency-Sensitive: Real-time inference requires sub-100ms responses

My biggest learning: Don’t underestimate the operational complexity of AI infrastructure. A vector database isn’t just Postgres with vectors—it requires specialized operational expertise, monitoring, and optimization.

Rust’s Continued Rise

Rust adoption accelerated in 2023, particularly for infrastructure and systems programming.

Where Rust Shined

High-Performance Data Paths

Packet processing at line rate
Zero-copy parsers
Lock-free data structures
SIMD optimization

Safety in Critical Systems

Memory safety without garbage collection
Fearless concurrency
Type-safe configurations
Compile-time guarantees

Developer Productivity

Cargo ecosystem matured significantly
Async runtime stabilization (Tokio 1.x)
Better IDE support and tooling
Growing talent pool

Challenges Remain

Not everything is perfect in Rust-land:

Compile times can still be painful for large projects
Learning curve remains steep for teams
Async ecosystem still has rough edges
Binary sizes can be larger than equivalent C/C++

My take: Rust is the right choice for new systems-level infrastructure, but requires team investment in training and tooling. The safety and performance benefits are worth it.

eBPF Goes Production

eBPF moved from “interesting technology” to “production-critical” in 2023.

Major Use Cases

Observability Without Instrumentation

Continuous profiling (Parca, Pyroscope)
Network flow monitoring
Security event collection
Performance analysis

Network Performance

XDP for DDoS mitigation
Service mesh data planes (Cilium)
Load balancing at line rate
Connection tracking

Security Monitoring

Runtime security (Falco, Tetragon)
File integrity monitoring
Privilege escalation detection
Malware detection

Lessons Learned

eBPF is powerful but has limitations:

Kernel version dependencies still cause issues
Verifier rejections can be frustrating
Debugging is hard compared to user-space
Map size limits require careful planning

Best practice: Start with well-tested eBPF frameworks (Cilium, Falco) rather than writing raw eBPF. Graduate to custom programs only when needed.

Multi-Cloud Reality Check

The promise of multi-cloud was portability and resilience. The reality is more nuanced.

What Actually Worked

Strategic Multi-Cloud

Different clouds for different workloads
Leverage cloud-specific strengths
Avoid single-vendor lock-in for critical services

Edge + Cloud Hybrid

Edge for low latency
Cloud for capacity and analytics
Clear data flow and ownership

What Didn’t Work

Cloud-Agnostic Abstractions

Lowest common denominator
Miss cloud-specific optimizations
Complexity without clear benefits

Active-Active Across Clouds

Cross-cloud latency still significant
Data consistency challenges
Cost of data egress prohibitive

My conclusion: Multi-cloud by default is wrong. Multi-cloud for strategic reasons (resilience, M&A, regulations) makes sense. Multi-cloud for portability is often premature optimization.

Platform Engineering Maturity

2023 saw platform engineering emerge as a recognized discipline with clear patterns.

What Worked

Developer Self-Service

Reduce time from idea to production
Service catalogs and templates
API-driven everything
Clear golden paths

Policy as Code

Enforce security and compliance
Cost management automation
Centralized guardrails
Shift-left security

Cost Visibility

Per-team/per-service cost allocation
Automated optimization recommendations
Showback and chargeback
Resource utilization tracking

What Needs Improvement

Developer Experience

Too many tools, not enough integration
Cognitive load still high
Documentation often outdated
Onboarding remains painful

Platform Operations

Platform teams often understaffed
Treating platform as product needs maturity
Measuring platform ROI is challenging
Balancing standardization vs. flexibility

The key insight: Platform engineering is product development, not just infrastructure. Treat developers as customers, measure satisfaction, iterate based on feedback.

Performance Optimization Trends

Performance work in 2023 focused on efficiency in an economic downturn.

Major Themes

Right-Sizing Everything

Automated resource optimization
Spot instances and reserved capacity
Graviton adoption (ARM in cloud)
Function-level cost attribution

Latency Reduction

Zero-copy wherever possible
Lock-free data structures
CPU cache optimization
SIMD for data processing

Observability Overhead Reduction

Sampling strategies
Efficient metrics aggregation
eBPF for low-overhead collection
Tail-based tracing

The common thread: Every microsecond and every dollar matters. Performance optimization isn’t premature—it’s good engineering in a resource-constrained world.

Security Evolution

Security in 2023 was about shifting left and reducing attack surface.

Zero Trust Everywhere

Mutual TLS by default
Identity-based access control
Continuous verification
Hardware attestation

Supply Chain Security

SBOM (Software Bill of Materials) adoption
Container image signing (Sigstore)
Dependency vulnerability scanning
Provenance verification

Runtime Security

eBPF for threat detection
File integrity monitoring
Anomaly detection with ML
Automated incident response

The mindset shift: Security is not a gate, it’s a continuous process. Automate, detect, respond—not prevent.

Technology Predictions for 2024

Looking ahead to 2024, here’s what I’m watching:

LLMs in Production

RAG (Retrieval-Augmented Generation) will become standard
Vector database importance will grow
Prompt engineering will professionalize
Fine-tuning will become more accessible

WebAssembly at the Edge

WASM for edge functions
Portable, secure, fast
Better language support
Standard component model

Platform Engineering Evolution

AI-assisted platform operations
Automated optimization
Predictive scaling
Natural language interfaces

Rust Ecosystem Growth

More critical infrastructure in Rust
Better async ecosystem
Improved compile times
Enterprise adoption increases

Observability 2.0

OpenTelemetry becomes standard
eBPF-based observability mainstream
AI-powered anomaly detection
Automated root cause analysis

Key Lessons from 2023

Technical Lessons

Complexity is the enemy - Simple systems win over clever ones
Measure everything - You can’t optimize what you don’t measure
Design for failure - Resilience > perfection
Operational excellence matters - Great tech with poor ops fails
Developer experience is paramount - Make the right thing easy

Organizational Lessons

Platform teams are strategic - Investment in platforms pays dividends
Security can’t be bolted on - Build it in from the start
Cost optimization is continuous - Not a one-time project
Documentation is infrastructure - Treat it as first-class
Feedback loops are critical - Short cycles, rapid iteration

Personal Lessons

Depth beats breadth - Master the fundamentals
Writing clarifies thinking - Document to understand
Community accelerates learning - Engage, share, contribute
Balance is essential - Sustainable pace wins marathons
Stay curious - Technology never stops evolving

Looking Forward

2023 was a year of rapid change and evolution. AI moved from experimental to production-critical. Rust continued its ascent. eBPF became mainstream. Platform engineering matured. Security shifted left.

2024 promises to be even more interesting. LLMs will reshape how we interact with systems. WebAssembly will bring new deployment models. Platform engineering will incorporate more AI. Observability will become more automated.

The constant in all this change: Good engineering fundamentals never go out of style. Focus on reliability, performance, security, and developer experience. Master the basics. Build on solid foundations.

Here’s to an exciting 2024 ahead!