[ LLMOps ]
Ship LLM Changes Without Breaking Production
Your LLM feature works in a demo, but every prompt tweak or model update is a gamble. We set up the tracing, evals, gateways and cost controls that let your team release changes weekly and know what they did.
Example release lane
- v2.3 Shorter support repliesEvals passed
- v2.4 Cheaper model tierHeld: eval below the gate
- v2.4.1 Retrieval filter fixEvals passed
- v2.5 Semantic cache on FAQsCost per request down
Signs You Need LLMOps
LLMOps becomes urgent once real users depend on an LLM feature and nobody can say whether last week's change made it better or worse.
- Symptom 01
Releases that break quietly
A prompt edit fixes one complaint and breaks three other flows. Eval suites in CI catch regressions on every pull request before users do.
Prompt and model changes ship with evidence
- Symptom 02
Token bills growing faster than usage
Spend climbs every month without a clear reason. Cost attribution, caching and model routing show where tokens go and cut the waste.
Cost per request visible and under control
- Symptom 03
Bugs nobody can reproduce
A customer reports a bad answer and the team cannot see what happened. Full traces of prompts, retrieval and tool calls make every run debuggable.
Root cause found in minutes from a trace
- Symptom 04
Locked to one model provider
Switching models means rewriting code across services. A model gateway puts every provider behind one API with routing and fallbacks.
New models trialed without code rewrites
- Symptom 05
Outages and provider incidents
Rate limits or provider downtime take your feature offline. Retries, fallback models and alerting keep it running and tell you when it is degraded.
Fewer user-facing failures during provider issues
- Symptom 06
Audit and compliance questions
Security or a customer asks what the model saw and said. Logged, redacted traces and versioned prompts give a clear answer.
Audit requests answered from records, not memory
What We Set Up and Run
LLMOps borrows from DevOps and MLOps, with extra care for non-deterministic outputs and per-token costs.
- 01End-to-end tracingOpenTelemetry-based traces for every prompt, retrieval, tool call and model response, linked to users and sessions.
- 02Eval pipelines in CIGolden datasets, LLM-as-judge and rule-based checks run on every pull request, with merge gates on agreed thresholds.
- 03Prompt versioningPrompts stored as versioned assets with change history, environment promotion and instant rollback.
- 04Model gatewayOne API across OpenAI, Anthropic, Google and self-hosted models, with routing, rate limits, retries and fallbacks.
- 05CachingProvider prompt caching plus exact and semantic response caches, with invalidation rules so stale answers do not leak.
- 06Cost dashboardsSpend broken down by feature, customer, model and prompt version, with budgets and alerts per team.
- 07Incident responseAlerts on quality, latency and error spikes, runbooks for common failures and post-incident reviews that add new eval cases.
- 08Online evaluationSampled production traffic scored continuously, with user feedback and A/B tests feeding back into the eval set.
The LLMOps Stack Around Your App
LLMOps wraps your existing application. Most of it can be added without rewriting the product.
Chatbots, agents, RAG features and copilots already in use.
- Product features
- Agents
- RAG pipelines
- APIs
Gateway and control
Every model call passes through one controlled entry point.
Prompt and release management
Versioned prompts and configs promoted like code.
Observability and evaluation
Traces, scores and feedback that show how the app behaves.
Reporting and response
Where teams see cost, quality and incidents.
Start With an LLMOps Audit
We trace a week of real traffic, measure cost and quality per feature and hand you a ranked list of fixes, before you commit to a full setup.
Book the LLMOps Audit
Teams That Ship LLM Changes Weekly
“I had the pleasure of working with Sajal Tech on a travel website, and it was an incredible experience from start to finish. They demonstrated professionalism, clear understanding of requirements, and excellent communication throughout.”
Operational Controls for LLM Apps
Good LLMOps is also good governance: you can show what changed, why, and what it did to users.
Release gates
No prompt or model change reaches production unless eval pass rate, safety checks and cost per request stay within agreed limits.
Redacted tracing
PII is masked before traces are stored, retention is set per environment and access to raw logs is role-based.
Change traceability
Every production answer can be tied to the exact prompt version, model, retrieval results and config that produced it.
Spend and abuse limits
Per-user and per-tenant budgets, rate limits and anomaly alerts stop runaway loops and abusive traffic, managed by an ISO 27001 certified team.
Where LLMOps Pays Back First
The more people depend on an LLM feature, the sooner tracing, evals and cost controls pay for themselves.
SaaS products
LLM features used by thousands of customers, where a bad prompt release reaches everyone at once and spend scales with every tenant.
- Eval gates in CI
- Per-tenant budgets
- Model gateway with fallbacks
LLMOps Pricing
Indicative starting prices. Tooling licenses, if any, are billed by the vendor, and self-hosted options are available.
LLMOps audit
Review of your current LLM app with tracing added and a prioritized fix list.
From$1,500
1 week
- Tracing on key flows
- Cost and latency baseline
- Failure pattern review
- Prioritized roadmap
LLMOps setup
Most chosenTracing, CI evals, prompt versioning, gateway and dashboards for one product.
From$8,000
3 to 5 weeks
- Eval suite and CI gates
- Prompt registry
- Model gateway and caching
- Cost and quality dashboards
- 30 days of support
Managed LLMOps
Ongoing operation of your LLM stack by a senior engineer.
From$3,000/mo
Monthly
- Monitoring and incident response
- Eval set upkeep
- Model upgrade testing
- Monthly cost review
LLMOps Tools We Work With
We use what fits your stack and hosting rules. Self-hosted options are available for teams that cannot send traces to a SaaS tool.
- Langfuse
- LangSmith
- Arize Phoenix
- Helicone
- OpenTelemetry
- Datadog LLM Observability
How We Roll Out LLMOps
We start by making the current system visible, then add controls in the order that reduces the most risk.
1 week
Observability baseline
Instrument your app with tracing, map every model call and produce a first report on cost, latency and failure patterns.
- Tracing live
- Baseline report
- Fixed quote
1 to 2 weeks
Evals and release gates
Build golden datasets from real traces, wire evals into CI and agree the thresholds that block a merge.
- Eval suite
- CI gates
1 to 2 weeks
Gateway and cost controls
Add the model gateway, caching, fallbacks, budgets and dashboards, then tune routing to cut spend without losing quality.
- Model gateway
- Cost dashboards
- Runbooks
Monthly
Managed operations
We watch quality and cost, respond to incidents, test new models against your evals and report what changed.
- Monthly ops report
- Model upgrade reviews
MLOps focuses on training and serving models you build. LLMOps focuses on apps built on top of large language models, where prompts, retrieval and provider APIs change often and outputs are not deterministic. It adds prompt versioning, eval suites, token cost tracking and model gateways.
An audit with tracing on key flows starts from $1,500. A full setup with CI evals, gateway and dashboards usually starts around $8,000, and managed LLMOps from $3,000 per month. Tool licenses are separate, and open-source options like Langfuse can be self-hosted.
LangSmith fits teams already on LangChain or LangGraph and happy with a hosted service. Langfuse is open source, framework-agnostic and easy to self-host, which suits stricter data rules. Both handle tracing, datasets and evals well, and we can set up either.
We sample real traces, pick representative and failure cases, and have your domain experts confirm the expected answers. Most teams start with 100 to 300 cases and grow the set from every incident and user complaint.
They are useful when calibrated. We check judge scores against human labels on a sample, use clear rubrics and combine them with deterministic checks such as JSON validity, citation presence and exact-match facts.
It depends on how repetitive your traffic is and how much you rely on the largest models. We measure it on your traces first, then show the expected saving and its effect on eval scores before switching anything on.
Tracing is sent asynchronously and adds negligible latency. A gateway adds a small hop, which caching usually more than offsets. We measure p95 latency before and after setup.
No. Most tracing is added through SDK wrappers or OpenTelemetry, and the gateway is a drop-in replacement for provider API calls. Prompt extraction into a registry is done flow by flow.
We run it against your eval suite, compare quality, latency and cost to the current model and recommend whether to switch. With a gateway in place, the switch is a config change with instant rollback.
Let's build your next product together
Book a free strategy call and leave with a clear plan and estimate. No commitment.
- 01Pick a time that suits you
- 0230 minutes on scope, stack, timeline and budget
- 03Fixed-price proposal, NDA on request