Inference Gateway
Multi-provider LLM gateway with fallbacks, caching, and per-tenant budgets.
Python for Production AI Workloads
Take your Python AI MVP from beta to scale. We add caching, queueing, GPU inference, and observability so the system survives launch day, not just the demo.
10k+
Concurrent inference req/s
<150ms
p99 LLM streaming first-token
99.9%
Production AI uptime delivered
60%
Average inference cost reduction
Production Python AI engineering: what we add to scale
1
Throughput engineering
- Async fan-out for parallel LLM and tool calls
- Connection pooling for vector DB and Postgres
- Request coalescing for duplicate user queries
- Prompt caching at the gateway layer
2
Cost control at scale
- Per-tenant token budgets and rate limits
- Model routing: small for simple, large for hard
- Response caching with semantic similarity
- Spot GPU instances for batch workloads
3
Reliability and SLOs
- Circuit breakers around every LLM provider
- Graceful fallbacks (Anthropic <-> OpenAI <-> local)
- Idempotent retries with exponential backoff
- Synthetic eval traffic to catch quality drift
4
Operability
- OpenTelemetry traces across every span
- Token, latency, and cost metrics in Grafana
- Structured logs ready for any SIEM
- Replayable request bundles for debugging prompts
Production Python AI services we deliver
Engineering work that takes a Python AI MVP into year two
GPU Serving
vLLM, TGI, or Triton-backed Python services for self-hosted open models.
Async Pipelines
Celery, RQ, or Dramatiq pipelines for long-running embedding and fine-tune jobs.
Real-time Streaming
WebSocket and SSE Python servers streaming tokens and tool events to clients.
Performance Tuning
Profiling with py-spy, hot-path Cython rewrites, and connection pool sizing.
Migration to Scale
Lift a Flask/Django prototype into ASGI, async DB, and proper background jobs.
Why teams trust SpeedMVPs to scale Python AI
We measure before we tune
Every optimization starts with py-spy + Grafana evidence, no premature rewrites.
We pick boring infra
Postgres + Redis + a queue, proven stacks that scale to seven figures of users.
We bake in evals
Quality regression suites run on every PR so improvements ship without breaking accuracy.
We optimize cost early
Tracking dollars per query from week one: surprises don't show up in your invoice.
We document handoffs
Runbooks, oncall guides, and architecture diagrams ship with every project.
We stay vendor-neutral
Our Python AI services swap LLM providers in hours, not weeks.
Why teams trust SpeedMVPs to scale Python AI
We measure before we tune
Every optimization starts with py-spy + Grafana evidence, no premature rewrites.
We pick boring infra
Postgres + Redis + a queue, proven stacks that scale to seven figures of users.
We bake in evals
Quality regression suites run on every PR so improvements ship without breaking accuracy.
We optimize cost early
Tracking dollars per query from week one: surprises don't show up in your invoice.
We document handoffs
Runbooks, oncall guides, and architecture diagrams ship with every project.
We stay vendor-neutral
Our Python AI services swap LLM providers in hours, not weeks.
Python at production AI scale. FAQ
Not for I/O-bound workloads, which is what most AI inference is. We use asyncio and ASGI frameworks like FastAPI to handle thousands of concurrent users on a single process. For CPU-bound work we move to multiprocessing, Ray, or no-GIL builds.
Almost never for an AI MVP. The hot path is the LLM provider, not Python. We've seen Python services serve 10k+ concurrent users with sub-200ms first-token latency. Migration is justified only when CPU-bound logic dominates the request budget.
Three levers: model routing (use the smallest model that works), prompt and response caching, and per-tenant budgets that fail fast. We've cut inference cost by 60% on multiple projects using these alone.
OpenTelemetry traces every span (prompt build, LLM call, tool exec, DB read), tokens and dollars get emitted as metrics, and structured logs ship to Datadog or Grafana. You can answer 'why was this slow?' in under a minute.
Yes. We deploy vLLM, TGI, or Triton on Modal, Runpod, or AWS GPU instances and front them with a Python gateway that handles batching, queueing, and fallback to hosted APIs.
